Schedule

23 May

24 May

23 May

24 May

Speakers

Sponsors

Exhibitors

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Matthew Mattina

Senior Director, Machine Learning & AI Research
ARM
Matthew Mattina is Senior Director of Machine Learning & AI Research at Arm, where he leads a team of researchers developing advanced hardware, software, and algorithms for machine learning. Prior to joining Arm in 2015, Matt was Chief Technology Officer at Tilera, responsible for overall company technology, processor architecture and strategy. Prior to Tilera, Matt was a CPU architect at Intel and invented and designed the Intel Ring Uncore Architecture. Matt has been granted over 30 patents relating to CPU design, multicore processors, on-chip interconnects, and cache coherence protocols. Matt holds a BS in Computer and Systems Engineering from Rensselaer Polytechnic Institute and an MS in Electrical Engineering from Princeton University.

24 May

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Jonathan Mailoa

Computational Research Engineer
Robert Bosch
Jonathan Mailoa is currently a research engineer at Bosch Research and Technology Center, where he works on atomistic computational material science simulation of materials relevant for energy applications such as batteries and fuel cells. Prior to that, he completed his PhD in Electrical Engineering and Computer Science at MIT, developing novel tandem solar cell device architectures. He is currently interested in developing molecular dynamics force field based on machine learning methods.

23 May

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Kelly Davis

Machine Learning Ressearcher
Mozilla
Kelly Davis has many irons in the fire. He studied Mathematics and Physics at MIT, then went on to do graduate work in Superstring Theory/M-Theory. He then jumped ship, coding at a startup that eventually went public in the late 90's. When the bubble burst, he jumped back into an academic setting and joined the Max Planck Institute for Gravitational Physics where he worked on software systems used to help simulate black hole mergers. Jumping ship yet again, he went back into industry, writing 3D rendering software at Mental Images/NVIDIA. When that lost its charm, he founded a NLU at a startup, 42, that created a system, based off of IBM'S Watson, able to answer general knowledge questions. After a brief stint as the Director of Machine Learning at another Berlin startup, he joined Mozilla where he now leads the machine learning group.

23 May

23 May

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Cheng Zhan

Senior Data Scientist
Anadarko
Cheng Zhan is a Senior Data Scientist at Anadarko Petroleum, where he works on field development optimization and long-term production forecasting. He focuses on building machine learning algorithms to create strategic and financial impact for the company. Prior to his current role, he worked as a Geophysicist at TGS and CGG, utilizing seismic data and inversion methods to help operators make better decisions in exploration. He holds a PhD in mathematics from University of Houston, and a B.S. in Mathematics from Sun Yat-sen University.

24 May

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Michael Sollami

Lead Data Scientist
Salesforce
Michael received a doctorate in mathematics from the University of Wyoming. Since 2012 he has led research and development teams at a number of successful Boston-based startups. Currently a lead data scientist on Salesforce's Einstein team, he enoys designing and building deep learning systems with applications to e-commerce and computer vision.

24 May

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Jason Yosinki

Co-Founder & Research Scientist
Uber AI Labs
Jason Yosinski is a machine learning researcher, founding member of Uber AI Labs, and scientific adviser to Recursion Pharmaceuticals. His work focuses on building more capable and more understandable AI. He suspects scientists and engineers will build increasingly powerful AI systems faster than we can understand them, motivating much of his work on what has been called AI Neuroscience -- an emerging field of study that investigates fundamental properties and behaviors of AI systems. Dr. Yosinski was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, Caltech/NASA Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC. Prior to his academic career, Jason cofounded two web technology companies and started a program in the Los Angeles school district that teaches student algebra via hands-on robotics. In his free time, Jason enjoys cooking, sailing, reading, paragliding, and sometimes pretending he's an artist.

23 May

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Brandon Rohrer

Data Scientist
Facebook
Brandon love solving puzzles and building things. Applied machine learning gives him the opportunity to do both in equal measure. He started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and complex system modeling at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. At Facebook he works to get internet and electrical power to those in the world who don't have it, using deep learning and satellite imagery, and to do a better job identifying topics reliably in unstructured text. In his spare time he likes to rock climb, write robot learning algorithms, and go on walks with his wife and our dog, Reign of Terror.

23 May

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Akram Bayat

Research Assistant
UMassd Boston
Akram Bayat is a Research Assistant at the University of Massachusetts Boston where she also received her Ph.D. in computer Science at the Visual Attention Laboratory of the advised by Professor Marc Pomplun. Akram received both the master of Electrical Engineering and the master of Computer Science prior to joining Ph.D. program. She is currently working on how to apply human attentional mechanism to deep neural network for the scene and object recognition. Akram has conducted several projects on Human activity recognition and eye-movement based user classification. She is also interested in computer vision, machine learning, data mining, and human-user interface design.

23 May

23 May

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Davis Addy

Sr. Principal Technology Leader – Food Safety & Product Quality
Chick-fil-A
Davis Addy is the Sr. Principal IT Leader for Food Safety & Product Quality at Chick-fil-A. His team is responsible for internal Restaurant assessment and evaluation platforms in addition to developing digital solutions that leverage AI, advanced analytics, and connected devices in the Restaurant. Prior to joining Chick-fil-A in 2016, Davis spent eight years with General Electric (GE) supporting enterprise application rollouts across North America, Europe, and Asia. Davis holds a BS in Computer Engineering from the University of Florida and a MS in Information Systems Management from Georgia State University. He currently resides in Atlanta, GA with his wife Kim and their two daughters Brooklyn (6) and Hollyn (2).

23 May

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Rana el Kalouby

Co-Founder & CEO
Affectiva
A pioneer in artificial emotional intelligence (Emotion AI), Rana el Kaliouby, PhD, is co-founder and CEO of MIT spinoff and category-defining company Affectiva. Rana led the innovation of the company’s award-winning technology, which uses deep learning and massive amounts of data to analyze complex and nuanced emotions and cognitive states from face and voice, for industries like automotive, market research, HR video recruitment, and mental health. Rana is now paving the way for Human Perception AI: software that can detect all things human, from nuanced human emotions and complex cognitive states, to behaviors, activities and the objects people use. Rana is one of few women leading a disruptive AI company. A Muslim-American and passionate advocate, she frequently speaks in press and on stage about innovation, women in technology, ethics in AI and leadership. Forbes recently included Rana in their list of America’s Top 50 Women in Tech, Fortune Magazine included her in their 2018 40 under 40 and she was named one of the three Global Business pioneers by Bloomberg in 2017. Rana is also a member of the World Economic Forum’s Young Global Leaders and a member of the Partnership on AI. Rana holds a BSc and MSc in Computer Science from the American University in Cairo, a PhD from the Computer Laboratory at the University of Cambridge and a Post Doctorate at MIT.

23 May

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Jay Baxter

Senior Machine Learning Engineer
Twitter Cortex
Jay Baxter is a Senior Machine Learning Engineer at Twitter Cortex, where he works on applying scalable machine learning methods to improve Twitter's recommendations and conversational health. Previously, he had worked on a variety of software and machine learning projects, ranging from book search and alerts at Google to entity coreference resolution at Diffeo. He received his M.Eng. and S.B. in Computer Science from MIT, where he led development on a probabilistic database system called BayesDB.

24 May

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Andrei Polzounov

Senior Research Scientist
Blue River Technology
Andrei is a senior research scientist at Blue River Technology. He is focused on deep learning and computer vision for perception for smarter agricultural machines. Previously, Andrei worked on processing Airbus’ satellite imagery, drones for Lockheed Martin and text localization and semantic understanding of text for Singapore’s Agency for Science Technology and Research. In his spare time Andrei enjoys skiing and hiking.

23 May

24 May

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David Held

Assistant Professor
Robotics Institute Carnegie Mellon Uni
David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute. His research focuses on robotic perception for autonomous driving and object manipulation. Prior to coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University where he developed methods for perception for autonomous vehicles. David also has a B.S. and M.S. in Mechanical Engineering at MIT. David is a recipient of the Google Faculty Research Award in 2017.

23 May

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Stefanos Nikolaidis

Assistant Professor
University of Southern California
Stefanos Nikolaidis is an Assistant Professor of Computer Science at the University of Southern California, where he directs the Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab. Research in ICAROS spans the whole spectrum of human-robot interaction science: from distilling the fundamental mathematical principles that govern interactive behaviors, to developing approximation algorithms for deployed robotic systems and testing them "in the wild" with actual end users. Previously, Stefanos completed his PhD at Carnegie Mellon's Robotics Institute and received his MS from MIT. He has also a MEng from the University of Tokyo and a BS from the National Technical University of Athens. Stefanos has worked as a research associate at the University of Washington, as a research specialist at MIT and as a researcher at Square Enix in Tokyo. He has received a Best Enabling Technologies Paper Award from the IEEE/ACM International Conference on Human-Robot Interaction in 2015, a best paper nomination from the same conference in 2018 and was a best paper award finalist in the International Symposium on Robotics 2013.

23 May

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Zoya Bylinskii

Resereach Scientist
Adobe Research
Zoya Bylinskii is a Research Scientist in the Creative Intelligence Lab at Adobe Research in Cambridge and an Associate of the Institute of Applied Computational Science at Harvard University. She received a Ph.D. and an M.Sc. in Computer Science from the Massachusetts Institute of Technology in 2018 and 2015, respectively, and an Hon. B.Sc. in Computer Science and Statistics from the University of Toronto in 2012. Zoya is a 2018 Rising Star in EECS, a 2016 Adobe Research Fellow, a 2014 NSERC Postgraduate Scholar, a 2013 Julie Payette Research Scholar, and a 2011 Anita Borg Scholar. Zoya works at the interface of human vision, computer vision, and human-computer interaction.

23 May

23 May

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Karen Hao

AI Reporter
MIT Tech Review
Karen Hao is the artificial intelligence reporter for MIT Technology Review, where she covers the latest developments, ethics, and social impact of the technology. She also writes the AI newsletter, The Algorithm, which thoughtfully demystifies the field’s latest news and research. Prior to joining the publication, she was a reporter and data scientist at Quartz and an application engineer at the first startup to spin out of Google X.

23 May

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Gautam Shroff

VP & Chief Scientist
Tata Consultancy Services
Dr. Gautam Shroff is a Vice President and Chief Scientist in Tata Consultancy Services and heads TCS Research. He has published 90 research papers in the areas of computational mathematics, parallel computation, distributed systems, software architecture, software engineering, big data, information fusion, virtual reality as well as artificial intelligence including machine learning, deep learning, Bayesian inference and natural language processing. He has written two books “Enterprise Cloud Computing” published by Cambridge University Press, UK, in October 2010, and “The Intelligent Web”, published by Oxford University Press, UK, in 2013 (paperback ed. 2015). Prior to joining TCS in 1998, Dr. Shroff had been on the faculty of the California Institute of Technology, Pasadena, USA (1990 - 91) and thereafter of the Department of Computer Science and Engineering at Indian Institute of Technology, Delhi, India (1991 - 1997). He has also held visiting positions at NASA Ames Research Center in Mountain View, CA, and at Argonne National Labs in Chicago. He completed his B.Tech degree in Electrical Engineering from IIT Kanpur in 1985, and Ph.D in Computer Science from Rensselaer Polytechnic Institute Troy, NY in 1990.

24 May

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Arnie Kravitz

CTO
ARM
Arnie Kravitz drives ARM’s technical vision. He helps it fulfill its mission of increasing U.S. global competitiveness by accelerating innovative technologies that make robots more accessible to U.S. manufacturers. With over 37 years designing, developing, and manufacturing a large portfolio of products Arnie draws from a broad portfolio of experiences. These include; expert, imagery interpretation, and automatic target recognition systems; self-learning inference engine applications, aquatic robots, autonomous vehicles, robotics, augmented, and virtual reality vision systems, commercial consumer electronics, and cryptographic devices. He has taught as an Adjunct Professor at The Johns Hopkins University and has significant experience exploiting emerging tools and techniques to rapidly transform new ideas into manufactured products. Arnie holds a master’s degree in Electrical Engineering from Rensselaer Polytechnic Institute.

24 May

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Richard Mallah

Director of AI Projects
Future of Life Institute
Richard Mallah is Director of AI Projects at Future of Life Institute, an existential risk mitigation and technology beneficence NGO, where he works on the robust, safe, beneficent development of advanced AI. He helps move the world toward existential hope and away from outsized risks via meta-research, analysis, research organization, community building, and advocacy, with respect to technical progress, strategy, and policy coordination. Richard is also part of IEEE’s initiative on autonomous systems ethics, a committee member at Partnership on AI, a senior advisor to the The Future Society, and head of AI R&D at startup Avrio AI.

23 May

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Cansu Canca

Founder/Director
AI Ethics Lab
Cansu is the founder and director of the AI Ethics Lab, where she leads teams of computer scientists and legal scholars to provide ethics analysis and guidance to researchers and practitioners. She has a Ph.D. in philosophy specializing in applied ethics. She works on ethics of technology and population-level bioethics with an interest in policy questions. Prior to the AI Ethics Lab, she was a lecturer at the University of Hong Kong, and a researcher at the Harvard Law School, Harvard School of Public Health, Harvard Medical School, Osaka University, and the World Health Organization.

23 May

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Gracelyn Shi

Innovator
The Knowledge Society
Gracelyn Shi is a 15-year-old machine learning developer and genomics researcher. She is interested in the intersection of technology and biology, especially artificial intelligence and genetics. She has developed machine learning projects related to machine learning detection, fluorescently tagging cells, and predicting transcription factor-DNA binding. She has worked with companies such as Walmart and Wealthsimple on consulting projects. She has also done research related to induced pluripotent stem cells and regenerative medicine. Her mentors include professionals and researchers in the regenerative medicine industry and machine learning industry.

24 May

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Albert Lai

Innovator
The Knowledge Society
Albert Lai is a 16-year-old artificial intelligence and deep learning developer. Highly interested in math and programming, he’s combined his skillset towards machine learning, most notably towards computer vision and natural language processing with Pytorch, including classification and text generation models. He’s currently working on a project to denoise EEG brainwaves with deep learning for epilepsy diagnosis with BCIs. He’s working with leading professors and researchers in the field to implement his solution. He enjoys running, building robots, and competitive programming in his spare time.

24 May

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Liam Hinzman

Innovator
The Knowledge Society
Liam Hinzman is a 16-year-old machine learning developer. He began his journey developing neural networks when he was 14 and is now building Zilic, a single test that can detect any disease in a medical image using machine learning. He’s spoken about his work at conferences and companies including BCTech Summit, Microsoft, and Amazon. This summer he is interning at Layer 6 and SickKids, one of Canada’s largest medical research institutes.

24 May

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Yunus Saatchi

Senior Machine Learning Scientist
Uber AI Labs
Yunus did his PhD at the Machine Learning lab at the University of Cambridge, under the supervision of Carl Rasmussen and Zoubin Ghahramani, now chief scientist at Uber! His PhD was in scalable methods for a brand new type of Gaussian process known as the structured Gaussian process: a Gaussian process with a covariance structure chosen to make it scalable. After getting addicted to getting slow but awesome code run faster during his PhD, high-frequency trading seemed like a natural choice for Yunus, so he spent two years at Tower Research Capital, a New York-based quantitative hedge fund. He then switched gears (and countries) and joined one of the relatively older AI research labs in the Bay Area, namely Vicarious for another two years, where he worked on deep generative models and scalable sum-product networks. Getting the urge to apply some deep learning models in the wild, he joined comma.ai, a self-driving car startup in San Francisco, as Chief Machine Learning Officer. There he built an operational, self-driving system purely for the highway and congested highway traffic scenarios. Since then he has been a senior research scientist at Uber AI Labs, where he has implemented Bayesian optimization and reinforcement learning systems at Uber scale. He is also an advisor and investor in several ML startups across the globe.

23 May

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Jane Hung

AI Resident
Uber AI Labs
Jane received her PhD from MIT and the Broad Institute in Anne Carpenter’s Imaging Platform. While applying deep learning-based computer vision models to biological problems like malaria detection, she became interested in software that bridges the gap between research and real world use cases. At Uber AI Labs, she has been working with product teams like Elevate on machine learning models for improved planning and Freight for improved price forecasting. She is also working on combining reinforcement learning algorithms with realistic simulations.

23 May

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David Bau

PhD Student
MIT CSAIL
David Bau is a PhD student at MIT CSAIL, advised by Professor Antonio Torralba. David previously worked at Google and Microsoft where he has contributed to several widely used products including Google Image Search and Microsoft Internet Explorer. David believes that complex systems should be built to be transparent, and his research focuses on the interpretability of deep networks.

23 May

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Neil Tenenholtz

Director of Machine Learning
MGH & BWH Center for Clinical Data Science
Neil Tenenholtz is the Director of Machine Learning at the MGH & BWH Center for Clinical Data Science, where his responsibilities include the training of novel deep learning models for clinical diagnosis, the development of robust infrastructure for their deployment into the clinical setting, and the creation of tooling to facilitate these processes. Prior to joining the Center, Neil was a Senior Research Scientist at Fitbit where he leveraged machine learning and modeling techniques to develop new features and algorithms that reside both on-device and in the cloud. Neil received his PhD from Harvard University where he was a recipient of the NSF Graduate Research Fellowship and the Link Foundation Fellowship in Advanced Simulation and Training.

23 May

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Amir Tahmasebi

Director of Machine Learning & AI
Codametrix
Amir Tahmasebi is the director of machine learning and AI at CODAMETRIX, Boston, MA. He is also a lecturer in Electrical and Computer Engineering Department at Northeastern University, Boston, MA. Prior to joining CODAMETRIX, Dr. Tahmasebi was a Principal R&D Engineer at Disease Management Solutions Business of Philips HealthTech, Cambridge, MA. Dr. Tahmasebi’s research is focused on patient clinical context extraction and modeling through image analysis and Natural Language Processing, outcome analytics and clinical decision support. Dr. Tahmasebi received his PhD degree in Computer Science from the School of Computing, Queen's University, Kingston, Canada. He is the recipient of the IEEE Best PhD Thesis award and Tanenbaum Post-doctoral Research Fellowship award. He has been serving as an industrial Chair for IPCAI conference since 2015. Dr. Tahmasebi has published and presented his work in a number of conferences and journals including AMIA, JDI, IEEE TMI, IEEE TBME, MICCAI, IPCAI, HBM, SPIE, RSNA, and SIIM. He has also been granted more than 10 patent awards.

23 May

23 May

23 May

24 May

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Wei-Lun Hsu Alterovitz

Senior Bioinformatics Data Scientist
FDA
Dr. Wei-Lun Alterovitz is a senior bioinformatics data scientist doing biomedical research in the Center for Biologics Evaluation and Research (CBER) at FDA. She is currently focusing on establishing informatics methodologies to classify phenotypes, adverse outcomes, and disease-causing genotypes to better understand pathogenesis, molecular mechanisms of diseases and their effects to patient-related outcomes. Dr. Alterovitz is also interested in real-world data, evidence, and use cases of deep learning algorithms in drug discovery process to improve clinical trial design and possible treatment outcomes. Her ultimate goal is applying AI and data analytics to the healthcare industry to improve public health.
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Mark Homer

Head of Healthcare AI
Fidelity Investments
Mark Homer is Head of Healthcare AI at Fidelity. He has been developing machine learning algorithms and solutions in healthcare for over a decade. Mark received his BS and MS from MIT, PhD in biomedical engineering from Brown University, and an MMSC from Harvard Medical School where he won a National Library of Medicine fellowship award. He has over 20 years of experience in AI, working in a variety of domains including drones, bioreactors, and brain-computer interfaces.

24 May

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Yi Pan

Professor
Georgia State Uni
Yi Pan is currently a Regents’ Professor and Chair of Computer Science at Georgia State University, USA. He has served as an Associate Dean and Chair of Biology Department during 2013-2017 and Chair of Computer Science during 2006-2013. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan's research interests include parallel and cloud computing, wireless networks, and bioinformatics. Dr. Pan has published more than 200 journal papers with over 80 papers published in various IEEE journals. In addition, he has published over 150 papers in refereed conferences. He has also co-authored/co-edited 43 books. His work has been cited more than 8000 times. Dr. Pan has served as an editor-in-chief or editorial board member for 15 journals including 7 IEEE Transactions. He is the recipient of many awards including IEEE Transactions Best Paper Award, several other conference and journal best paper awards, 4 IBM Faculty Awards, 2 JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship.

23 May

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Mark Gooding

Cheif Scientist
Mirada Medical
Dr Mark Gooding, Chief Scientist at Mirada Medical, obtained his DPhil in Medical Imaging from University of Oxford in 2004. He was employed as a postdoctoral researcher both in university and NHS settings, where his focus was largely around women’s health. In 2009, he joined Mirada Medical, motivated by a desire to see technical innovation translated into clinical practice. While there, he has worked on a broad spectrum of clinical applications, developing algorithms and products for both diagnostic and therapeutic purposes. If given a free choice of research topic, his passion is for improving image segmentation, but in practice he is keen to address any technical challenge. Dr Gooding now leads the research team at Mirada, where in addition to the commercial work he continues to collaborate both clinically and academically. Dr Gooding has been responsible for leading the research and the development of DLCExpert™ technology, which uses AI (Artificial Intelligence) to learn the clinician’s contouring preferences and automatically apply them to images. This technology demonstrates that AI is not just about huge technological leaps forward. It can be more rapidly applied to everyday tasks to make incremental step-change improvements to the effectiveness of radiotherapy treatment planning, saving time for oncologists and potentially improving patient care.

24 May

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Stephen Odaibo

CEO & Founder
Retina AI
Dr. Stephen G. Odaibo is Founder, CEO, and Chief Software Architect of RETINA-AI, a company using Artificial Intelligence to improve Healthcare. He is a Retina specialist, Mathematician, Computer Scientist, and Full-Stack AI Engineer. Dr. Odaibo is the only Ophthalmologist in the world with advanced degrees in both Mathematics and Computer Science. In 2017 UAB College of Arts and Sciences awarded Dr. Odaibo its highest honor, the Distinguished Alumni Achievement Award. In 2005 he won the Barrie Hurwitz Award for Excellence in Clinical Neurology at Duke Univ. School of Medicine where he topped the class in Neurology and in Pediatrics. In 2016 Dr. Odaibo delivered the Opening Keynote address at the Global Ophthalmologists Meeting in Osaka Japan. And he delivered the inaugural Special Guest Lecture in Ophthalmology at the University of Ilorin, Nigeria. In 2018, Dr. Odaibo delivered the keynote address at the National Medical Association's New Innovations in Ophthalmology Session. And he delivered a Plenary Keynote address on AI in Healthcare at AI Expo Africa in Cape town, South Africa. He is author of the book "Quantum Mechanics and the MRI Machine'' (2012), and of the book "The Form of Finite Groups: A Course on Finite Group Theory" (2016).

24 May

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Neva Durand

Chief Computational Scientist
Baylor College of Medicine
Neva C. Durand is the chief computational scientist at Aiden lab at Baylor College of Medicine, where she creates analysis and visualization software for assays exploring how DNA folds in three dimensions. Her pipeline has been adopted as the NIH standard for this experiment. Previously, she developed object and feature recognition systems using machine learning in Poggio lab at MIT and in Ponce lab at INRIA. She received her PhD in computer science from the University of Washington in 2009.

23 May

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Amit Deshwar

Deep Genomics
Amit Deshwar is the Director of Predictive Systems at Deep Genomics. His doctoral work was at the University of Toronto under Quaid Morris using Bayesian Non-parametrics to study intra-tumour heterogeneity and evolution. He is a Vanier Scholar and former Junior Fellow at Massey College. Previously he worked at Google, started two companies and obtained undergraduate degrees in Software Engineering and Psychology from the University of Calgary.

23 May

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James Cai

Head of Data Science
Roche Innovation Center
Dr. James Cai is the Head of Data Science at Roche Innovation Center New York, responsible for supporting drug discovery and development projects by leveraging big data and advanced analytics. Through exploration and mining of biomedical big data, e.g., those in genomics, electronic health records (EHRs), digital images, text documents and wearable devices, he and his team have made numerous discoveries that impacted drug projects at Roche via new insights and better decision making. James worked in the pharma industry for the past 18 years. He has been a bioinformatics scientist, software developer, business analyst, project manager, data scientist, and manager at Roche. James has a Ph.D. in Molecular Biology from Cornell University and a Master’s degree in Biomedical Informatics from Columbia University.

23 May

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Ajit Narayanan

CTO & Co-Founder
mfine
An electronics engineer by education, Ajit has more than 20 years of expertise in creating products & building organizations across e-commerce, Consumer Internet, Mobile, Analytics, Integration and Platforms. He is an inventor on many patents in these domains and his current research includes Virtual Assistants, Explainable Artificial Intelligence and Knowledge Representation. In his previous avatar, Ajit was the CTO for Myntra, India's largest e-commerce store for fashion and lifestyle products. At Myntra, Ajit was responsible for leading a 400+ tech team and define strategy and direction for Consumer Applications, Supply Chain, Analytics and Data Science Platforms. Ajit started off in mobile technology at SAP, where he built products for offline and online mobile application development, domain programming languages for User Interfaces and Integration and API management.

24 May

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Sadid Hasan

Senior Scientist
Philips Research
Dr. Sadid Hasan is a Senior Scientist in the Artificial Intelligence Group at Philips Research, Cambridge, Massachusetts. His recent work involves solving problems related to clinical information extraction, paraphrase generation, natural language inference, and clinical question answering using Deep Learning. Sadid has over 60 peer-reviewed publications in the top NLP/Machine Learning venues, where he also regularly serves as a program committee member/area chair including ACL, IJCAI, EMNLP, NeurIPS, ICML, COLING, NAACL, AMIA, MLHC, MEDINFO, ICLR, ClinicalNLP, TKDE, JAIR etc.

23 May

23 May

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Farhan Siddiqui

Advanced Analytics Architect
Pfizer
Farhan Siddiqui is an Advanced Analytics Architect at Pfizer and is a technology leader with extensive technology management experience delivering cloud hosted advanced analytics solutions for top pharmaceutical companies in US. Deep expertise in cloud computing, big data and machine learning
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Pablo Cingolani

Principal Scientist
AstraZeneca
Pablo is a leader in computational biology and life sciences with vast experience in algorithms/methods development, engineering, big data analysis, AI/machine learning and cloud/high-performance computing. At AstraZeneca, he applies bioInformatics & AI techniques to improve research and drug development in Oncology.

23 May

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Alex Ermolaev

Director fo AI
Change Health
Alex Ermolaev, Director of AI at Change Healthcare, has developed and led a variety of AI projects over the last 20 years, including enterprise AI, platforms/tools, NLP, imaging and self-driving cars. Alex is one of the most frequent “AI in Healthcare” speakers in the Silicon Valley. Change Healthcare is one of the largest healthcare technology companies in the world.

24 May

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Niels Bantilan

ML Engineer
Talkspace
Niels is a machine learning engineer at Talkspace, where he works with data scientists, clinicians, and product managers to build NLP-powered tools that enhance asynchronous talk therapy. His current work at Talkspace centers around early detection and continuous monitoring systems for acute mental health conditions, as well as building the experimentation infrastructure to measure the efficacy of Talkspace’s suite of therapist notification systems. He holds a Masters in Public Health with a specialization in sociomedical science and public health informatics, and has a background in developmental biology and immunology. His research interests include reinforcement learning, automated machine learning, and fairness, accountability, and transparency in machine learning. He regularly contributes to open source projects and likes to develop data validation tools for improving data science practice.

23 May

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Ron Xu

Senior Data Scientist
Aetna
Ron Xu is a Senior Data Scientist at Aetna, where he utilizes machine learning and deep learning to optimize the care management programs. Prior to that, he has built dozens of models to improve the marketing campaigns for Staples and designed a NLP system to classify customers' feedback in CVS. He holds a M.S. in economics from Suffolk University and a B.S. in statistics from Qingdao Technological University.

24 May

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Rudina Seseri

Founder & Managing Partner
Glasswing Ventures
Rudina Seseri is founder and managing partner at Glasswing Ventures, an Entrepreneur-In-Residence at Harvard Business School and an Executive-In-Residence for Harvard University’s Innovation-Lab. With over 14 years of investing and transactional experience, Rudina has led technology investments and acquisitions in startup companies in the fields of robotics, Internet of Things (IoT), SaaS marketing technologies and digital media. Most recently, Rudina co-founded and launched Glasswing Ventures, an early-stage firm focused on companies with enabling Artificial Intelligence technologies that specifically address the connected world and the security of this ecosystem.
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Akane Sano

Assistant Professor
Rice University
Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering and Computer Science. She directs Computational Wellbeing Group. She has worked on measuring, predicting and improving mental health and cognitive performance for students, employees, city residents and patients with mental disorders using mobile and wearable sensors. She obtained her PhD at MIT Media Lab, and her MEng and BEng at Keio University, Japan. Before she came to the US, she was a researcher/engineer at Sony Corporation and worked on wearable computing, intelligent systems and human-computer interaction.

23 May

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Jordan Smoller

Director - Psychiatric and Neurodevelopmental Genetics Unit
Massachusetts General Hospital
Dr. Jordan Smoller is a psychiatrist, epidemiologist, and geneticist whose research focuses on genetic and environmental determinants of psychiatric disorders across the lifespan and using “big data”, including electronic health records and genomics, to advance precision medicine. Dr. Smoller is the Massachusetts General Hospital (MGH) Trustees Endowed Chair in Psychiatric Neuroscience and Professor of Psychiatry at Harvard Medical School. He is Director of both the Psychiatric and Neurodevelopmental Genetics Unit and the Precision Medicine Research Unit in the MGH Center for Genomic Medicine. Dr. Smoller is an author of more than 350 scientific publications and is also the author of The Other Side of Normal (William Morrow, 2012).

23 May

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Sujay Kakarmath

Physician Scientist, Data Science and Analytics
Partners Healthcare
Dr. Kakarmath is a digital health scientist at Partners Healthcare Pivot Labs and an Instructor at Harvard Medical School. His research is focused on the evaluation of the clinical utility of digital health solutions, including machine learning and artificial intelligence-based products. Dr. Kakarmath's team works closely with technology innovators from academia, startups and industry giants to guide the ideation, design, prototyping, validation, and deployment of digital health solutions. His work has been published in prestigious journals and showcased at major academic conferences such as those of the American Academy of Neurology, the American Medical Informatics Association, the International Society for Pharmacoeconomics and Outcomes Research, the Connected Health Conference, Precision Medicine Summit and HIMSS.

24 May

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Alice Xiang

Research Scientist
Partnership on AI
Alice Xiang is a Research Scientist at the Partnership on AI, where she focuses on fairness, transparency, and accountability in AI. Alice is both a lawyer and data scientist. Prior to joining PAI, she worked as an attorney at Gunderson Dettmer, representing startups and venture capital firms. She has also previously worked at the Department of Justice, Federal Reserve, and LinkedIn. Alice holds a Juris Doctor from Yale Law School, a Master’s in Development Economics from Oxford, a Master’s in Statistics from Harvard, and a Bachelor’s in Economics from Harvard.

23 May

24 May

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Kate Taylor

AI4All
Alumna
Kate Taylor is a high school junior in the Boston area and a Boston University AI4ALL alumna. As an AI4ALL Alumni Chapter Lead, she works to embody AI4ALL values, grow personally, and strengthen her community. As a young person in STEM, she believes she has a responsibility to support her peers and facilitate a safe space of discussion and dialogue where everyone can come together and feel truly supported and inspired. She is on the varsity swim team at her high school and is a swimming instructor at a local swim school.

24 May

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Tracy Qiu

AI4All
Alumna
Tracy Qiu is a high school student in the Boston area and a Boston University AI4ALL alumna. After spending a life-changing summer with an empowering group of young women at BU AI4ALL, she plans to continue pursuing her passion for STEM alongside the supportive AI4ALL community, and she looks forward to breaking down barriers in the male-dominated field of Artificial Intelligence. Passionate about giving back to her community, she has organized events at her high school donating over $5,000 to the Boston Children's Hospital, and in the future, she hopes to innovate with AI to benefit her community and those in need.

24 May

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Janos Perge

CVS
Principal Data Scientist
Janos A. Perge Ph.D. is a principal data scientist at CVS/Aetna, where he creates machine learning models for care management programs to reduce healthcare costs and improve health outcomes. Prior to his work in healthcare analytics he developed neural prostheses such as mind-controlled robotic arms for people with paralysis at Brown University. He also developed a research lab to study and modify learning by light impulses (i.e. optogenetics). He earned a Ph.D. in neuroscience from Utrecht University, the Netherlands. Feels passionate about coding strategies of live neural networks and their relevance to problems in artificial intelligence.

23 May

23 May

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Katherine Stevo

Buckingham Browne & Nichols School
High School Junior
Katherine Stevo is a 17-year-old junior at the Buckingham Browne & Nichols School in Cambridge, MA. Her main passion is the intersection of robotics and artificial intelligence. She is a 2018 alumna of AI4ALL, a program that educates young high schoolers about artificial intelligence. At AI4ALL, her team created a translation device utilizing computer vision to assist people who use American Sign Language. She is also a graduate of the Artemis Project and Codebreakers. Her other interest is the ethics of artificial intelligence, specifically mitigating bias, and she spoke about the topic at The Atlantic's Humanity and Tech conference at MIT. In the past couple of months, she won first place at the Code Day Boston Hackathon and also received second place and the people's choice award at MIT's Hack For Inclusion. She is currently working on developing her project from MIT's hackathon, which focuses on assisting visually impaired people to navigate public transportation.

24 May

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Hye Sun Na

GE
Director of Program Integration, Product Quality
Hye Sun Na is a Director of Program Integration and Product Quality at GE Healthcare driving the development of a platform for creating deep learning models. She works closely with device product teams to define the AI strategy and integrate deep learning into GEHC’s portfolio of imaging and clinical monitoring systems. Prior to joining the AI team, Hye Sun was a senior engineer on the CT physics team, leading feature development for GE’s Revolution CT system. She has over 10 years of engineering experience in diagnostic imaging including X-ray, MR, and CT. Hye Sun holds a Biomedical Engineering degree from the University of Texas at Austin and is a member of the American Association of Physicists in Medicine.

24 May

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Sarah Fay

Glasswing Ventures
Managing Director
Sarah Fay has more than 20 years of experience in the marketing services industry, with a track record of leveraging technology to deliver groundbreaking new models for advertising and media. In her role as Managing Director at Glasswing Ventures, Sarah leads and evaluates investments in early stage AI-powered companies. Sarah serves as Independent Director on the Boards of public and private technology and digital media companies including J2 Global (Nasdaq: JCOM), The Street (Nasdaq:TST), Narrative, Celtra, Socialflow, Stella Rising (formerly Women’s Marketing Inc, acquired by Stephens Group), and also served as an Independent Director on the Board of [X+1] (acquired by Rocket Fuel). She served as CEO of Aegis Media North America, a media and digital marketing communications company, where she was also responsible for launching and growing a significant part of that business during her eleven-year tenure. Previously, Sarah served as President of Carat US, and Isobar US, where she was tasked with the integration of digital and traditional media services. Sarah’s contributions to the broader community include serving on the Executive Board of the Ad Club of Boston, as Director on the Board of The Massachusetts Innovation and Technology Exchange (MITX) and as Board Advisor to Advertiser Perceptions. She is a board advisor to clypd, Distroscale, AdDaptive Intelligence, Viral Gains, and Wonderlust. Sarah earned a BA in English from the University of Vermont and has received over 30 accolades and awards for her accomplishments such as “Media All Star” (AdWeek), “Top 100 Most Influential People in Media (Media Post), Women to Watch (Ad Age), and “Fast 50” (Fast Company).

24 May

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Vlad Sejnoha

Glasswing Ventures
Venture Partner
Vlad brings over 30 years of experience in AI, machine learning and natural language processing to Glasswing Ventures and its portfolio. From 2001 through 2018, Vlad Sejnoha was Nuance’s Senior Vice President and Chief Technology Officer. In this role, Vlad led the company’s 350-strong worldwide research organization, which is responsible for developing conversational AI technologies, such as deep learning for the recognition of speech and images, natural language understanding, dialog, reasoning and other AI technologies, and which directly supports a broad range of Nuance products. These include virtual assistants for automotive, healthcare, and enterprise applications, real-time dictation and transcription, and medical information extraction from narrative text and the recognition of medical images for the automatic population of electronic health records. In addition, Vlad worked with the company's divisions on technology, product, and M&A strategy, managed the integration of acquired core technologies and teams, and directly managed select incubator projects. These have included apps such as the Dragon Go! mobile semantic voice search which made Time’s ’50 best iPhone apps’ list, Dragon Dictation, which was inducted into the Apple Hall of Fame, and Dragon TV, which won the 2017 Technology and Engineering Emmy award. Vlad was also responsible for the company’s external research relationships including a five-year collaboration with IBM Research, and was the president of the Nuance Foundation which advances the field of multimodal applications and user interface technology through research grants. In his role as CTO, Vlad acted as a spokesman for Nuance’s technology vision, and has presented at venues such as GigaOm Mobilize, CES, MIT Tech Mobile Summit, and SXSW, and his contributions and interviews have appeared in media such as the MIT Technology Review, Wired, and NY Times, among others. Prior to Nuance, Vlad has held a variety of research positions, including that of Chief Scientist at Kurzweil AI which produced the first commercially marketed large-vocabulary speech recognition system. Upon the acquisition of Kurzweil AI by L&H, Vlad led the company-wide speech recognition research effort there. Vlad has been working in the field of speech recognition and natural language processing for over 30 years and has been awarded 23 patents. He is a recipient of the 2014 CTO of the Year award from the Massachusetts Technology Leadership Council. Vlad holds BEng and MEng degrees in Electrical Engineering from McGill University.

24 May

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Olivia Toth

The Future Data Scientists of the World
Founder/President
Olivia is an undergraduate student at Mckendree University, double majoring in data analytics and sociology. She also is minoring in math and computer science. Recently, she founded the FDSW Club, where she leads fellow students in gaining real-life experience in data science. Her experience comes from independent studies with teachers, personal work, and internships. This learning process is guided by an interest in how deep learning can help data science processes and understanding. Prior to FDSW, she was a vice president of her local Association for Computing Machinery Club. She also presented a data science project at the 2017 MAA Mathfest.

24 May

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Audace Nakeshimana

MIT D-Lab
AI Fairness Researcher
Audace Nakeshimana is a rising senior at MIT studying Computer Science with a minor in Economics. He is an undergraduate researcher at MIT D-Lab, working to create technical guidelines aimed at helping faculty, students and industry professionals in developing countries to get a hands-on approach to quantifying and mitigating bias in Machine Learning applications. Audace is passionate about tackling technology challenges in resource-constrained environments. Prior to his current work, Audace has worked at MIT CSAIL in developing algorithms for understanding human-computer interaction under latency conditions, and he is a founder of KnowledgeGate, a startup working on technologies for enabling e-learning in regions of low internet connectivity.

23 May

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Danil Kirsanov

Machine Learning Software Engineer
Facebook
Danil Kirsanov works on ML problems in Facebook Spatial Computing group. He holds PhD in Applied Math from Harvard and prior to Facebook worked on scalable computing at Microsoft and MathWorks. His primary interests are in signal/image processing, numerical modeling and distributed machine learning applications.

24 May

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Sri Krishnamurthy

Founder
QuantUniversity
Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. He has also consulted with many organizations in establishing model governance practices. Prior to starting QuantUniversity, Sri has had significant analytical applications at Citigroup, Endeca, MathWorks and has consulted to more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School. Many of his students work in Data science roles at Fidelity, Santander, Wellington, GMO, State Street etc. Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College.

23 May

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Xin Wang

Senior Data Scientist
Fidelity Investments
Xin Wang is a Senior Data Scientist at Fidelity Investments. He works on developing and applying AI and Machine Learning methods to enhance customer experience and engagement. Previously, he worked as a Research Scientist at Philips Research North America, developing Deep Learning based approaches for Automatic Annotation of Chest X-ray Images and Concept Mining in Echocardiogram reports. He holds a Ph.D. in Computer Science and Engineering from University of Connecticut advised by Professor Jinbo Bi, where his study focused on building Machine Learning algorithms and systems in Learning from multiple annotators, Multi-instance Learning and Multitask Feature Learning.

24 May

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Joe Isaacson

VP of Engineering
Asimov
Joe is the VP of engineering at Asimov, a startup with the mission to program living cells with genetic circuits. We leverage techniques from synthetic biology, systems engineering and machine learning to continually improve the automation of genetic circuit design. Previous to Asimov, Joe lead machine learning teams at Quora, building recommendation systems to personalize content discovery and algorithms to optimize ad targeting. Previous to Quora, Joe lead the data science team at URX (acquired by Pinterest), a machine learning startup focused on leveraging information theory and knowledge bases to target advertisements.

24 May

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Ella Fejer

US Lead on Digital Futures
UK Science & Innovation Network
Lesia (“Ella”) Fejer serves as a Senior Science & Innovation Officer for the British Government. She is posted in Cambridge, Massachusetts with the UK Science & Innovation Network, an organization co-sponsored under the Foreign Office and the UK Department for Business, Energy & Industrial Strategy. There, she is charged with leading the UK’s strategy for engagement with the USA on emerging topics in Health & Life Sciences. Prior to serving as the lead for Health & Life Sciences, she led her organisation’s work on Digital, AI and Robotics. Combined, these have provided her with a unique insight into the convergence of Deep Learning & Healthcare. Recently, she authored a foresight piece for the British Government on The Future of Machine Learning & Healthcare.
Ella has a Masters in Foresight & Horizon Scanning from the University of Houston and a Masters in Science & Technology Policy from Texas A&M University. In her previous life as a scientist, Ella co-founded the first epigenetic lab in veterinary medicine in the USA. It was from this experience, working on the cutting-edge of science (nearly a decade ago), that she discovered her interest in combining her background in science with futures studies & public policy. Ella is passionate about the future of science, and a proponent of facilitating cooperation between government, academia, and industry to ensure the best possible future for science and society.

23 May

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Shuo Zhang

Senior Machine Learning Engineer
Bose Corporation
Shuo Zhang is a Senior Machine Learning Engineer (CED Applied Research) at Bose Corporation. His work encompasses natural language processing (NLP) and music information retrieval (MIR). Prior to Bose, he was a Researcher/Collaborator at the Music Technology Group (MTG), Department of Information and Communication Technologies (DTIC), Universitat Pompeu Fabra (UPF), Barcelona, Spain. Dr. Zhang serves on the program committees of conferences and workshops in communities such as ACL/NAACL/WWW/MASC-SLL. In 2016 he co-taught a tutorial on the application of NLP in MIR at the International Society of Music Information Retrieval (ISMIR) Conference at Columbia University in New York.

24 May

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Vijaya Kolachalama

Assistant Professor
Boston University School of Medicine
Vijaya Kolachalama is an Assistant Professor within the Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine. Research in his group is focused on developing deep learning algorithms for disease risk assessment as well as biomarker development and designing software technologies to assist therapeutic development and clinical decision-making. Current projects include development of fully convolutional networks and multimodal fusion models to predict the risk of Alzheimer’s disease and osteoarthritis using digital data such as MR imaging, and computer vision related tasks such as semantic segmentation, image classification and object detection for digital pathology applications. His group is also developing recurrent neural network approaches for protein sequence analysis. Before joining Boston University, Dr. Kolachalama held appointments as a Postdoctoral Associate at MIT, as an ORISE Fellow at the US Food and Drug Administration, and as a Principal Member of Technical Staff at the Charles Stark Draper Laboratory. He has a bachelor’s degree in Aerospace Engineering from the Indian Institute of Technology, Kharagpur, India and a PhD in Mechanical Engineering from the University of Southampton, UK. He recent accomplishments include recognition as Research Fellow and Junior Faculty Fellow by Boston University’s Hariri Institute of Computing and Fellow by Boston University’s Institute for Health System Innovation & Policy.

23 May

24 May

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Anthony Chang

Chief Intelligence and Innovation Officer
CHOC Children’s Orange County
Anthony Chang is currently the Chief Intelligence and Innovation Officer (CIIO) and Medical Director of the Heart Failure Program at Children’s Hospital of Orange County. He has also been named a Physician of Excellence by the Orange County Medical Association and Top Cardiologist, Top Doctor for many years as well as one of the nation’s Top Innovators in Healthcare.

24 May

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Sehyo Yune

Research Translation Director
Harvard Medical School
Dr. Yune is Research Translation Director of the Laboratory of Medical Imaging and Computation at Massachusetts General Hospital. She oversees all projects of the lab that include development of machine-learning models for medical images, natural language processing tools for analysis of electronic health record (EHR), and blockchain-based platform for health information exchange. She also actively works in developing new projects and implementing artificial intelligence technologies in the clinical workflow as clinical decision support tools.

23 May

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Charles Stromeyer

AI Researcher
A.I. Capital Management
Charles is an investor in capital markets, an AI researcher, and an advisor to startup companies, and he helped some with pioneering multiple new industries such as AI- based programmatic marketing, the “intercloud” that is a network of clouds interconnected via software- defined networks (SDN), and more. He has mentored startups before and after the top startup accelerators y combinator and Techstars, 25 winners of MassChallenge, a top prize winner of the MIT 100K Competition, and 12 individuals of the Forbes 30 Under 30. Startups helped have had 7 exits, including one IPO.

24 May

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Harshit Saxena

Chief of Product
Droice Labs

24 May

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Tasha Nagamine

Founder and Chief of AI
Droice Labs

24 May

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Mylene Yao

CEO
Univfy
Dr. Mylene Yao is cofounder and CEO at Univfy, a company that combines healthcare AI, fintech, and scientific validation to power women's access to fertility treatments that are safe and highly effective. Dr. Yao has over 20 years of experience in clinical and scientific research in reproductive medicine. She has led Univfy from founding through stages of technology invention and commercialization and now focuses on scaling Univfy's business.

24 May

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Fidelity Investments

Recruitment Partner
At Fidelity, our goal is to make financial expertise broadly accessible and effective in helping people live the lives they want. We do this by focusing on a diverse set of customers: - from 23 million people investing their life savings, to 20,000 businesses managing their employee benefits to 10,000 advisors needing innovative technology to invest their clients’ money. We offer investment management, retirement planning, portfolio guidance, brokerage, and many other financial products.

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Tata Consultancy Services

Silver
Tata Consultancy Services is an IT services, consulting and business solutions organization that has been partnering with many of the world’s largest businesses in their transformation journeys for the last fifty years. TCS offers a consulting-led, Cognitive powered, integrated portfolio of IT, Business & Technology Services, and engineering. This is delivered through its unique Location Independent Agile delivery model, recognized as a benchmark of excellence in software development. A part of the Tata group, India's largest multinational business group, TCS has over 417,000 of the world’s best-trained consultants in 46 countries. The company generated consolidated revenues of US $19.09 billion for year ended March 31, 2018 and is listed on the BSE (formerly Bombay Stock Exchange) and the NSE (National Stock Exchange) in India. TCS' proactive stance on climate change and award winning work with communities across the world have earned it a place in leading sustainability indices such as the Dow Jones Sustainability Index (DJSI), MSCI Global Sustainability Index and the FTSE4Good Emerging Index. For more information, visit us at www.tcs.com.

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Mirada Medical

Bronze
Mirada Medical is a leading international brand in medical imaging. The company develops advanced technology applications that help healthcare professionals use medical images more effectively and efficiently to improve cancer care. Mirada’s products are used across diagnostic radiology, molecular imaging, radiation oncology, medical oncology, tumor board and elsewhere. The company specializes in simplifying technically complex image processing tasks, allowing clinicians to more confidently diagnose disease, assess response to treatment, and plan radiation therapy or surgical intervention. Mirada’s advanced technology products are available throughout the world under its own brand, and on an OEM basis through a select number of the world’s leading healthcare companies.

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Change

Bronze
Change Healthcare is one of the largest, independent healthcare IT companies, focused on inspiring a better healthcare system. To do that, we provide use technology and provide solutions to address the three greatest needs in healthcare today – reducing costs, achieving better outcomes, and removing fragmentation from the system

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mfine

Startups
Mfine is an app-based, on-demand healthcare service that provides its users access to online consultations and care programmes from the country’s top hospitals. The AI-driven digital health platform partners with leading and trusted hospitals instead of aggregating individual doctors. The company aims to make access to trusted healthcare simple, fast and proactive. Users can consult doctors from their preferred hospitals via chat or video to get prescriptions and/or routine care. mfine’s AI driven platform helps improve the quality of diagnosis and promotes better outcomes with better engagement.

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Fidelity Investments

Deep Learning Summit
At Fidelity, our goal is to make financial expertise broadly accessible and effective in helping people live the lives they want. We do this by focusing on a diverse set of customers: - from 23 million people investing their life savings, to 20,000 businesses managing their employee benefits to 10,000 advisors needing innovative technology to invest their clients’ money. We offer investment management, retirement planning, portfolio guidance, brokerage, and many other financial products.

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DataRobot

Deep Learning Summit
DataRobot is the category creator and leading provider of automated machine learning. Organizations worldwide use DataRobot to empower the teams they have in place to rapidly build and deploy machine learning models and create advanced AI applications. With a library of hundreds of the most powerful open source machine learning algorithms, the DataRobot platform encapsulates every best practice and safeguard to accelerate and scale data science capabilities while maximizing transparency, accuracy and collaboration. By making data scientists more productive and enabling the democratization of data science, DataRobot helps organizations transform into AI-driven enterprises. Visit www.datarobot.com, and follow us on Twitter and LinkedIn.

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Zepl

Deep Learning Summit
Zepl is a data science and analytics platform that provides enterprise-grade data exploration, collaboration and governance to help companies become model-driven enterprises.

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FalconAI

Deep Learning Summit
One Cycle enables the transformation from the traditional reporting paradigm to an “Answers” paradigm. Our proactive metrics management products transform business interactions by providing managers, executives and staff with direct access to the answers they need – anytime and from anywhere.
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Bola AI

Deep Learning in Healthcare Summit
Bola is a AI–Voice Assistant company, that develops solutions for doctors, healthcare providers and researchers to conduct procedures, protocols and examinations, hands-free and without the aid of an assistant. Our mission is to reduce the time and friction created by digital documentation requirements and allow professionals to spend more time doing what only they can do.

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Valohai

Deep Learning Summit
Valohai is a Deep Learning Management Platform that automates deep learning infrastructure so companies can concentrate on data science. Scale models to hundreds of CPUs or GPUs at the click of a button. Create an audit trail and reproduce any previous run with built-in version control for input data, hyperparameters, training algorithms and environments. Manage your entire deep learning pipeline with automatic coordination from feature extraction and training to inference.

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H1

Deep Learning Summit
H1 is the first company to arm healthcare and life science companies with on-demand, live insights from across the data universe to accelerate the discovery and development of therapies to fight diseases. The company provides real-time data to support the end-to-end therapeutic development process from fundraising to product development to product launch, helping companies make smarter scientific decisions.
Working with medical affairs and strategy teams who span all phases of the development lifecycle, H1 provides the complete picture of institutions, experts, scholarly content, markets, competitors and new opportunities through research grounded in actual data and clinical findings.
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Frase

Deep Learning Summit
Frase develops AI-driven question answering systems for websites and intranets.
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Deep Learning in Healthcare Summit
Deep Learning Summit

Registration & Light Breakfast

08:15 AM 09:00 AM Foyer Area

Registration will open from 8am, please have your registration details to hand on your device. A light breakfast, tea and coffee will be available for you to help yourself!
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Deep Learning in Healthcare Summit

Welcome Note - Deep Learning in Healthcare Summit

09:00 AM 09:15 AM Deep Learning in Healthcare Summit

A note from the compère to recap on the highlights of Day 1 and share more on what is in store for Day 2!

Speakers

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Deep Learning Summit

Welcome Note - Deep Learning Summit

09:00 AM 09:15 AM Deep Learning Summit

A welcome speech and introduction to the event from the compère to explain what is in store over the next 2 days

Speakers

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Deep Learning Summit

What Do Your Neural Networks Learn? A Peek Inside the Black Box - iROBOT

09:15 AM 09:40 AM Deep Learning Summit

Deep neural networks are famously difficult to interpret. We'll take a tour of their inner workings to build an intuition of what's inside the black box and how all those cogs fit together. Then we'll use those insights as we step through a image processing problem with deep learning, showing at every step what the neural network is "thinking".

Speakers

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Deep Learning in Healthcare Summit

The “Why” Behind Barriers to Better Health Behaviours

09:15 AM 09:40 AM Deep Learning in Healthcare Summit

Speakers

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Deep Learning Summit

AI Neuroscience: Can we Understand the Neural Networks we Train? - UBER AI LABS

09:40 AM 10:05 AM Deep Learning Summit

Deep neural networks have recently made a bit of a splash, enabling machines to learn to solve problems that had previously been easy for humans but difficult for computers, like playing Atari games or identifying lions and jaguars in photos. But how do these neural nets actually work? What concepts do they learn en route to their goals? We built and trained the networks, so on the surface these questions might seem trivial to answer. However, network training dynamics, internal representations, and mechanisms of computation turn out to be surprisingly tricky to study and understand, because networks have so many connections — often millions or more — that the resulting computation is fundamentally complex. This high fundamental complexity enables the models to master their tasks, but we find now that we need something like neuroscience just to understand the AI that we’ve constructed! As we continue to train more complex networks on larger and larger datasets, the gap between what we can build and what we can understand will only grow wider. This gap both inhibits progress toward more competent AI and bodes poorly for a society that will increasingly be run by learned algorithms that are poorly understood. In this talk, we’ll look at a collection of research aimed at shrinking this gap, with approaches including interactive model exploration, optimization, and visualization.

Speakers

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Deep Learning in Healthcare Summit

Distributed Tensorflow: Scaling Model Training to Multiple GPUs - MGH & BWH CENTER FOR CLINICAL DATA SCIENCE

09:40 AM 10:05 AM Deep Learning in Healthcare Summit

While offering state-of-the-art performance across a variety of tasks, deep learning models can be time-consuming to train, thus hindering the exploration of model architectures and hyperparameter configurations. However, this bottleneck can be greatly reduced by leveraging the near-linear speedups afforded by multi-GPU training. In this talk, we will explore the different manners in which Tensorflow supports training to be distributed across a collection of GPUs.

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Deep Learning Summit

Visualizing and Understanding Generative Adversarial Networks - MIT CSAIL

10:05 AM 10:30 AM Deep Learning Summit

The remarkable success of Generative Adversarial Networks in generating nearly photorealistic images leads to the question: how do they work? Are GAN just memorization machines, or do they learn semantic structures? What do these networks learn? I introduce the method of Network Dissection to test the semantics captured by neurons in the middle layers of a network, and show how recent state-of-the-art GANs learn a remarkable amount of structure. Even without any labels in the training data, neurons in a GAN trained to draw scenes will separately code for objects such as trees, furniture, and other meaningful objects. The causal effects such neurons are strong enough that we can add and remove objects and paint pictures directly by manipulating the neurons of a GAN. These methods provide insights about the a GAN's errors as well as the contextual relationships learned by a GAN. By cracking open the black box, we can see how deep networks learn meaningful structure, and we can gain understandable insights about a network’s inner workings.

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Deep Learning in Healthcare Summit

Applications of a Deep Learning Model for Clinical Optimization and Population Health Management - CVS

10:05 AM 10:30 AM Deep Learning in Healthcare Summit

Current deep learning applications in health care tend to focus on natural language processing and computer vision using unstructured data. However, building deep learning models on structured data, such as administrative insurance claims, has received far less attention and holds untapped potential. In this talk I will discuss real-life applications of predicting two costly clinical outcomes from health insurance claims data: the likelihood of future back surgery and kidney failure. To accomplish this task with deep learning sequence models, I will cover three methods to improve model performance: i) embedding medical codes for input to the model, ii) transfer learning from a pre-trained general language model to improve model performance on small, context-specific data sets, and iii) using an attention mechanism to make neural networks more transparent. These methods have been implemented to train deep learning models on massive claims datasets and currently used in practice by one of the largest payers in the health insurance industry.

Speakers

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Deep Learning in Healthcare Summit
Deep Learning Summit

Coffee Break

10:30 AM 11:20 AM Foyer Area

Help your self to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors!
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Deep Learning Summit

Deep Learning Based Visual Scene and Object Recognition in Machine and Human Visual Systems - UNIVERSITY OF MASSACHUSETTS BOSTON

11:15 AM 11:40 AM Deep Learning Summit

In this talk, we present deep learning solutions for three visual scene perception and object recognition problems. The goal is to investigate to which extent deep convolutional neural networks resemble the human visual system for scene perception and object recognition: (1) classification of scenes based on their global properties, (2) deploying multi-resolution technique for object recognition, and (3) evaluating the influence of the high-level context of scene grammar for object and scene recognition. The first problem proposes to drive global properties of a scene as high-level scene descriptions from deep features of convolutional neural networks in scene classification tasks. The second problem shows that fine-tuning the Faster-RCNN to multi-resolution data inspired by human multi-resolution visual system improves the network performance and robustness over a range of spatial frequencies. Finally, the third problem studies the effects of violating the high level scene syntactic and semantic rules on human eye-movement behavior and deep neural scene and object recognition networks.

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Deep Learning in Healthcare Summit

Applying AI in Early Clinical Development of New Drugs - ROCHE INNOVATION CENTER

11:20 AM 11:40 AM Deep Learning in Healthcare Summit

AI is transforming many industries including healthcare and pharma. Where are the opportunities for AI in the early clinical development of new drugs, where scientific hypotheses first meet real patients in clinical trials? Can AI generate new insights to inform translational research or improve the efficiency of clinical trials? In this talk, I will highlight opportunities created by big data and AI, e.g., digital biomarkers for neurological diseases, and share my thoughts on what it will take to operationalize AI in drug development.

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Deep Dive Track

DEEP DIVE: Deep Reinforcement Learning in Robotic Simulation Environments - UBER AI LABS

11:20 AM 12:20 PM Deep Dive Track

Deep Reinforcement Learning (Deep RL) is presently one of the hottest and fastest-paced application areas of deep learning and machine learning as a whole. This intense focus is driven by the potential of importing the ground-breaking accuracy improvements of deep neural networks seen in large-scale supervised learning benchmarks, into the world of optimal decision making and control. Arguably one of the most important components of Deep RL is the simulation environment. Simulation environments play the key role of providing a benchmarking platform for comparing the cornucopia of different RL algorithms, hence giving researchers and practitioners crucial feedback on how effective their ideas are. Ultimately, the end goal of RL research is to build agents/robots which can interact effectively within real-world environments. The applications are limitless, ranging from autonomous vehicles to drones which can deliver packages.

The physical world presents plenty of highly challenging environments to navigate and agent-centric input data to process. Almost all real-world agents have multiple sensors recording stimuli in parallel. For example, autonomous vehicles receive, in real-time, data from odometric sensors, GPS, IMU, LIDAR, RADAR, SONAR and cameras. Data from all these sensors need to be fused effectively to form a state representation useful for the task at hand.

We discuss algorithms and simulation environments currently used by industry labs and show an example of how Deep RL can be connected to robotic simulation environments via our tool Benchmark Of Behavior in the Robot Operating System (BOB-ROS). 

Speakers

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Deep Learning Summit

Predicting What Drives Human Attention in Photographs, Visualizations, and Graphic Designs - ADOBE RESEARCH

11:40 AM 12:05 PM Deep Learning Summit

Knowing where a person looks in an image can provide us with important clues about what captures their attention and what may eventually enter their memory. Aggregating the attention patterns of a group of people can help us make conclusions about the effectiveness of a design. Computational models of attention help guide image processing algorithms like automatic image resizing and thumbnailing, they can direct a model to compose more meaningful image captions, and they can be used to provide feedback within graphic design tools. In this talk, I will cover what we know about human attention and how we capture human attention and interest in images at a large data scale using novel crowdsourcing interfaces. I will then demonstrate how we use this data to build computational models of attention for photographs, visualizations, and graphic designs, along with the applications that these models make possible.

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Deep Learning in Healthcare Summit

AI for Immuno Oncology on Liquid Biopsies - ASTRAZENECA

11:40 AM 12:00 PM Deep Learning in Healthcare Summit

Immuno-Oncology tries to develop treatments and drugs that help the immune system to fight cancer. This approach has gained a lot of attention in recent years due to the success of stories of some cancer immunotherapies and checkpoint inhibitor drugs. Nevertheless, immuno-oncology does not work on every patient. We explore some challenges and opportunities when using ML and AI, particularly in the context of NGS Sequencing from ctDNA (liquid biopsies) to select biomarkers that help us understand which patient would benefit using immuno-oncology drugs.

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Deep Learning in Healthcare Summit

Natural Language Processing for Healthcare - CODAMETRIX

12:00 PM 12:20 PM Deep Learning in Healthcare Summit

With recent advancements in Deep Learning followed by successful deployment in natural language processing (NLP) applications such as language understanding, modeling, and translation, the general hope was to achieve yet another success in healthcare domain. Given the vast amount of healthcare data captured in Electronic Medical Records (EMR) in an unstructured fashion, there is an immediate high demand for NLP to facilitate automatic extraction and structuring of clinical data for decision support. Nevertheless, the performance of off-the-shelf NLP on healthcare data has been disappointing. Recently, tremendous efforts have been dedicated by NLP research pioneers to adapt general language NLP for healthcare domain. This talk aims to review current challenges researchers face, and furthermore, reviews some of the most recent success stories.

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Deep Learning Summit

Childhood's End: Maturation of Deep Speech and Common Voice - MOZILLA

12:05 PM 12:30 PM Deep Learning Summit

We’ll talk about the blossoming of Deep Speech, an open deep learning based speech-to-text engine, and Common Voice, an open crowd-sourced speech corpora. We will cover recent Deep Speech advancements (streaming, small platform support, and product integrations) as well as Common Voice advancements (multi-language support, multi-language corpora, and profiles). Also we’ll give a overview of future plans and how to get involved.

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Lunch

12:20 PM 01:35 PM Foyer Area

A hot, 3-course, lunch buffet will be served in the foyer area. A great time for networking and to get to know your fellow participants or you can join the Lunch & Learn Session taking place in the Deep Dive Track and take a seat at one of the tables to hear more from the speakers.
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Deep Dive Track

DEEP DIVE: Lunch & Learn

12:30 PM 01:20 PM Deep Dive Track

Grab your lunch from the foyer and head to the Deep Dive session room to join some of todays speakers to discuss their focus areas and ask your questions. Including: University of Massachusetts Boston, Adobe Research, CODAMETRIX, Mozilla, Philips Research & CVS. Please note spaces are limited

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Deep Learning in Healthcare Summit

A Domain Knowledge-Enhanced Deep Learning Model for Disease Named Entity Recognition - PHILIPS RESEARCH

01:30 PM 01:50 PM Deep Learning in Healthcare Summit

Disease named entity recognition (NER) is a critical task for most biomedical natural language processing (NLP) applications. For example, extracting diseases from clinical trial text can be helpful for patient profiling and other downstream applications such as matching clinical trials to eligible patients. Similarly, disease annotation in biomedical articles can help information search engines to accurately index them such that clinicians can easily find relevant articles to enhance their knowledge. In this talk, I will discuss about our recently proposed domain knowledge-enhanced long short-term memory network-conditional random field (LSTM-CRF) model for disease named entity recognition, which also augments a character-level convolutional neural network (CNN) and a character-level LSTM network for input embedding. Experimental results demonstrate that our proposed model achieves new state-of-the-art results in disease named entity recognition on a scientific article dataset.ge caption generation.

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Deep Dive Track

DEEP DIVE: Ethics Analysis in Product Development - AI ETHICS LAB

01:30 PM 02:45 PM Deep Dive Track

An interactive, discussion-based workshop that helps participants identify and think through ethical issues that arise in developing an AI product.Working in groups, participants will apply core ethical concepts to a particular product in development. Groups will focus on different stages of the development process, identifying the relevant ethical concerns, revealing the value trade-offs, and devising solutions or safeguards to mitigate risks. This exercise introduces participants to ethics analysis during & of AI products while utilizing their own expertise in the process.

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Deep Learning Summit

Neural Network Force Field for Molecular Dynamics of Multi-Element Atomistic System - ROBERT BOSCH RTC

01:35 PM 02:00 PM Deep Learning Summit

Neural network-based force field has recently emerged as a way to bypass expensive quantum mechanics calculation in molecular dynamics simulation, which enables us to study material properties and physical mechanisms at the atomistic level. Despite fundamental advances in rotation-invariant symmetry function “fingerprint” data representation, the derivative fingerprints required for the atomic force calculation significantly increases the training and execution runtime required in this approach. In this talk, we present an algorithm to bypass the need for fingerprint derivatives and perform direct atomic force prediction which significantly reduces the computation efforts required for training and executing the neural network force field for molecular dynamics simulations.

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Deep Learning in Healthcare Summit

Deep Learning for the Assessment of Knee Pain

01:50 PM 02:10 PM Deep Learning in Healthcare Summit

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Deep Learning Summit

Robot Learning via Human Adversarial Games - UNIVERSITY OF SOUTHERN CALIFORNIA

02:00 PM 02:25 PM Deep Learning Summit

Much work in robotics has focused on “human-in-the-loop” learning techniques that improve the efficiency of the learning process. However, these algorithms have made the strong assumption of a cooperating human that assists the robot. In reality, people tend to act also in an adversarial manner towards deployed robotic systems. We show that this can in fact improve the robustness of the learned models by proposing a physical framework that leverages perturbations applied by a human adversary, guiding the robot towards more robust models. In a manipulation task, we show that grasping success improves significantly when the robot trains with a human adversary as compared to training in a self-supervised manner. This work opens a range of exciting potential applications in other domains as well, such as in autonomous driving.

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Deep Learning in Healthcare Summit

Learning How the Genome Folds in 3D - BAYLOR COLLEGE OF MEDICINE

02:10 PM 02:30 PM Deep Learning in Healthcare Summit

Since the human genome project, we've known the linear sequence of human DNA. However, the promised revolution in medicine is still yet to come; there is still much we do not know about how DNA regulates cell function. At Aiden lab, we explore how the two-meter long DNA molecule folds inside the cell. Our assay uses proximity ligation to determine which loci in the 1D genome are close together in 3D. We use deep learning to find "peaks" in the resulting contact maps. These peaks turn out to correspond to loops mediated by the protein CTCF, and link promoters and enhancers, correlating with gene activation.

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Deep Learning Summit

Uncertainty - Aware Robot Learning - ROBOTICS INSTITUTE, CMU

02:25 PM 02:50 PM Deep Learning Summit

Robots today are typically confined to operate in relatively simple, controlled environments. One reason for these limitations is that current methods for robotic perception and control tend to break down when faced with occlusions, viewpoint changes, poor lighting, unmodeled dynamics, and other challenging but common situations that occur when robots are placed in the real world. I argue that, in order to handle these variations, robots need to learn to understand how the world changes over time: how the environment can change as a result of the robot’s own actions or from the actions of other agents in the environment. I will show how we can apply this idea of understanding changes to a number of robotics problems, such as object tracking and safe robot learning. By learning how the environment can change over time, we can enable robots to operate in the complex, cluttered environments of our daily lives.

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Deep Learning in Healthcare Summit

Predicting the Effects of Genetic Medicines Using Transfer Learning - DEEP GENOMICS

02:30 PM 02:50 PM Deep Learning in Healthcare Summit

Genetic medicines promise the ability to precisely target the root causes of disease. At Deep Genomics, we are developing machine learning systems to predict the properties of these medicines, including activity and safety. A fundamental problem in doing so is that large collections of therapeutic data is infeasible to collect. Using transfer learning allows us to fuse large amounts of inexpensive biology data with small amounts of therapeutic data. I will discuss how we have successfully used transfer learning to predict the on-target activity of genetic medicines, enabling us to test five times fewer compounds for some of our targets.

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Coffee Break

02:50 PM 03:40 PM Foyer Area

Help your self to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors. Are you looking for a new job opportunity or is your company looking to hire? Head to the Deep Dive Track and join the Talent & Talk session to hear some of the roles available as well as what skills some of our attendees have to offer!
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Deep Dive Track

DEEP DIVE: Talent & Talk

02:50 PM 03:40 PM Deep Dive Track

Are you recruiting and want to share positions with leading minds in AI? Or looking for a new role and want to explore the options? This quick pitch session allows you 1-Minute to share details of what/who you are looking for or skills you have to share and network with the right people. Interested? Email charlie@re-work.co to sign up!
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Deep Learning Summit

How Chick-fil-A uses AI to Spot Food Safety Trends in Social Media - CHICK-FIL-A

03:40 PM 04:00 PM Deep Learning Summit

Social media is an amazing way for companies to connect directly to their customers. At Chick-fil-A, it’s also one of many tools we use to help improve food safety at more than 2,000 locations around North America. To derive food safety insights from the hundreds of customer reviews received each day, we had to address a number of challenges inherent in analyzing social media data. After all, social media often contains broken grammar, mixed sentiments, and off-topic musings. To address these challenges, we developed a cloud-based service that uses artificial intelligence to help spot potential food safety issues from restaurant level customer review data. In this presentation, we’ll cover how the service works, using natural language processing (NLP) in combination with common serverless cloud computing tools. We’ll also cover how the data collected from social media can be used with other data sets (e.g. health department data) to help show possible correlations to better identify food safety trends.

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Deep Learning in Healthcare Summit

A Novel Deep Learning Method for Predicting Epilepsy - GEORGIA STATE UNIVERSITY

03:40 PM 04:00 PM Deep Learning in Healthcare Summit

Deep learning has been successfully used in many applications such as computer vision, automatic speech recognition, natural language processing, audio recognition, and medical imaging processing and disease diagnosis. Recently, our group has designed a method to detect a type of epilepsy - benign epilepsy with centrotemporal spikes, which is the most popular epilepsy with children. In our method, we use three sources of data: hand-crafted features from MRI images based on doctors’ knowledge, 3D MRI images and 4D functional MRI images. The final prediction decision is obtained by fusing the three prediction results through another neural network. Our idea is to take advantages of all three data sources which have different strengths and important features to achieve the best prediction results. We have done many experiments which show that the proposed method is truly better than any existing prediction method. Future improvement including how to use more data sources will also be outlined in this talk.

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Deep Learning Summit

The New Social Contract - Humanizing Artificial Intelligence - AFFECTIVA

04:00 PM 04:20 PM Deep Learning Summit

Artificial Intelligence is quickly becoming mainstream, engrained in the fabric of our lives, acting on our behalf – helping us get things done faster, more efficiently, giving us deeper insights, maybe even helping us be happier and healthier. AI is taking on tasks that were traditionally done by humans – from acting as our personal assistants and hiring our next co-worker, to driving our cars and assisting with our healthcare. But AI today has high IQ but no EQ, no emotional intelligence. We’re forging a new kind of partnership with technology. A new social contract that is based on mutual trust. In this talk, Dr. el Kaliouby will discuss the 5 tenets of this new social contract including how to build AI that has empathy, the ethical considerations of AI and the importance of guarding against data and algorithmic bias.

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Deep Learning in Healthcare Summit

The Key to Adoption of Deep Learning for Medical Imaging: Interpretability and Comprehensiveness

04:00 PM 04:20 PM Deep Learning in Healthcare Summit

During the past two years, many deep-learning based algorithms that detect critical conditions in various medical images have been approved by the Food and Drug Administration to be used in clinical practice. However, many clinicians remain skeptical that these algorithms would reduce their workload and ensure patient safety as many optimists claim. For deep learning algorithms to be rapidly adopted and accepted by clinicians, they need to be interpretable and comprehensive. We will share experience and approach to develop clinically relevant deep learning algorithms.

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Deep Dive Track

DEEP DIVE: Model Governance in the Age of Data Science and AI - QUANTUNIVERSITY

04:00 PM 05:00 PM Deep Dive Track

With innovations in hardware, algorithms and large datasets, the use of Data Science and Machine learning in finance is increasing. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling models. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms exploring novel modeling methods to augment their traditional investment and decision workflows. While there is significant enthusiasm, model risk professionals and risk managers are concerned about the onslaught of new technologies, programming languages and data sets that are entering the enterprise. With very little guidance from regulators on how to govern the tools and the processes, organizations are developing their own home-cooked methods to address model governance challenges. In this workshop, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies.

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Deep Learning Summit

PANEL: Machine Learning: Is it Ethical?

04:20 PM 05:00 PM Deep Learning Summit

Hear from experts in the fied of AI, ethics and regulations on their perceptions of what the importance of ethical considerations and how these are impacting the progression and adoption of machine learning in real-world settings.

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Deep Learning in Healthcare Summit

PANEL: The Impacts of Machine Learning in Mental Health Care

04:20 PM 05:00 PM Deep Learning in Healthcare Summit

Approaches to mental health care are chaning in the digital age but how is machine learning being used to help? Is the technology the best way forward? Hear from professionals in the field on their thoughts on how machine learning can help and the steps needed to advance its adoption.

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Networking Drinks

05:00 PM 06:00 PM Foyer Area

Join us in the foyer area and grab a drink to celebrate the end of Day 1 and continue to network with attendees.
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Registration & Light Breakfast

08:15 AM 09:00 AM Foyer Area

Registration will open from 8am, please ensure you have your namebadge visable when returning for Day 2. If you did not attend Day 1, please head to the registration desk and have your registration details to hand on your device. A light breakfast, tea and coffee will be available for you to help yourself!
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Deep Learning Summit

Welcome Note - Deep Learning Summit

09:00 AM 09:15 AM Deep Learning Summit

A note from the compère to recap on the highlights of Day 1 and share more on what is in store for Day 2!

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Deep Learning in Healthcare Summit

Welcome Note - Deep Learning in Healthcare Summit

09:00 AM 09:15 AM

A note from the compère to recap on the highlights of Day 1 and share more on what is in store for Day 2!

Speakers

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Deep Learning Summit

ML on the Edge: Hardware and Models for Machine Learning on Constrained Platforms - ARM

09:15 AM 09:40 AM Deep Learning Summit

Deep neural networks are a key technology at the core of advanced audio and video applications. As these applications begin to migrate from large servers executing in the cloud to mobile and embedded platforms, they place significant demands on the underlying hardware platform. This talk will review the key properties of these models and how these properties are leveraged to deliver efficient inference on energy, compute, and space constrained platforms.

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Deep Learning in Healthcare Summit

AI Based Multi-Modal Inferential System for Differential Diagnosis in Healthcare - MFINE

09:15 AM 09:35 AM Deep Learning in Healthcare Summit

For a system to be truly artificially intelligent, it should either think like a human; act like a human; think rationally or act rationally. To deliver quality care and to be accepted by us humans, it is highly important for an AI system to act humanly in healthcare. For a system to act humanly, Alan Turing proposes that it should have the following capabilities: (a). Natural Language Processing; (b). Knowledge representation; (c). Automated reasoning; (d). Machine learning; (e). Computer vision; and (f). Robotics. In the quest of developing an AI system that can act humanly, we have built a multi-modal inferential system for differential diagnosis that satisfies five of the six capabilities enlisted above as a part of the Turing’s test. At the core of our system is a novel Siamese Bayesian Network (SBN) for knowledge representation and automated reasoning for differential diagnosis. A Natural Language Processor (NLP), that extracts the context of a disease from the textual inputs by the patients, and a Computer vision system (CVS), that infers the findings from lab reports and medical images, feed into the SBN for better convergence of differential diagnosis. While the NLP uses a combination of SVM’s and LSTM’s for inference from text, the CVS employs mid level computer vision and deep mobile nets and inception nets for inference from images. In its current state, the system is capable of inferring from text, natural images (lab reports) and chest X-Rays. This talk summarizes the algirithms in use at delivering healthcare at scale in India.

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Deep Learning in Healthcare Summit

Multilingual NLP for Clinical Text: Impacting Healthcare with Big Data, Globally - DROICE LABS

09:35 AM 09:55 AM Deep Learning in Healthcare Summit

At Droice, we leverage massive repositories of clinical text to build deep learning/NLP solutions to help clinicians make better decisions for individual patients. With the widespread adoption of electronic medical records (EMRs) and recent advances in machine learning, natural language processing has come to the forefront in clinical AI. Despite the challenges of working with unstructured text, doctors’ notes and other clinical text contains some of the richest information about a patient. However, building systems that can work with clinical text in languages other than English remains a challenge to this day. In this talk, we will present several real-world use cases of NLP-powered solutions in several languages.

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Deep Learning Summit

Deep Learning for the Future Enterprise - TATA CONSULTANCY SERVICES

09:40 AM 10:05 AM Deep Learning Summit

The initial wave of deep learning breakthroughs led to an explosion of exciting new applications based on superior solutions to problems in vision, speech and text. However, the initial solutions were largely focused on end user applications such as music recommendations and photo tagging, and the impact did not immediately translate to applications in Banking, Healthcare, Retail, and Manufacturing. There are multiple challenges that impede the effective application of deep learning to real world problems such as machine health monitoring, container stowage planning and healthcare recommendations. Insufficient and noisy data, compliance with privacy regulations, interpretability requirements on predictions, incorporation of domain knowledge during learning, contingency planning for model failure and an inflexible enterprise culture are some of the prominent obstacles that deep learning has to overcome for effective application to enterprise problems. Slowly but surely, companies are finding solutions to these problems and the impact of deep learning is now percolating to enterprises in many different sectors. As one of the worlds largest IT consulting firms, TCS has nurtured a dedicated team of deep learning researchers to provide solutions to these problems for use cases across sectors ranging from manufacturing and shipping to healthcare and finance.

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Deep Learning in Healthcare Summit

Programming Living Organisms Through Targeted Machine Learning

09:55 AM 10:15 AM Deep Learning in Healthcare Summit

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Deep Learning Summit

ML @Twitter: An Inside Look at Recommendations - TWITTER

10:05 AM 10:30 AM Deep Learning Summit

The cold start problem for new users is a classic challenge for recommender systems. In this talk, I will discuss some deep learning approaches that can be used to address this problem, including using neural networks to train co-embeddings of new users and items, and serving them in an efficient way at runtime via approximate nearest neighbor algorithms like LSH or HNSW. I will also touch on some of the difficulties of evaluating such models both offline and online in the context of A/B tests.

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Deep Learning in Healthcare Summit

Redefining the IVF Experience for Patients & Providers Using AI - UNIVFY

10:15 AM 10:35 AM Deep Learning in Healthcare Summit

Millions of women and couples in the U.S. are affected by infertility, but only 3% of them use in vitro fertilization (IVF), the most effective medical treatment. The high cost and the uncertainty of the outcome presents significant financial risks and emotional stress, limiting access to IVF treatment for many individuals. Univfy uses AI/ML to improve patients’ experience by providing personalized counseling and accurate IVF success predictions to help women and their partners understand their potential to succeed with IVF. AI supports transparency in treatment outcomes and a true value-based financial program to maximize patients’ probability of having a baby while minimizing or capping financial risks. As importantly, there is significant potential to use AI to address the emotional needs of fertility patients to provide the support needed to navigate treatment.

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Coffee Break

10:30 AM 11:20 AM Foyer Area

Help your self to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors. Are you looking for a new job opportunity or is your company looking to hire? Head to the Deep Dive Track and join the Talent & Talk session to hear some of the roles available as well as what skills some of our attendees have to offer!
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Deep Learning in Healthcare Summit

Using Deep Learning to Understand Lack of Exercise - AETNA

11:15 AM 11:40 AM Deep Learning in Healthcare Summit

Exercise is generally considered a healthy activity. For example, walking can reduce risk of a cardiovascular event, such as a heart attack. Unfortunately, studies report that roughly half of those over 65 years or older do not regularly exercise. To address this issue, Aetna has programs to encourage exercising. In support, we have built a deep learning model that predicts the probability that a member is exercising and, more importantly, to provide clues as to why a person is not exercising. A sequential model with an attention mechanism estimates the probability of exercise as well as highlights prior events in a person's medical record that most contributed to the estimate. The findings demonstrate good predictive performance, such as 77.2% positive predictive value among the top scoring 10%. Commonly found drivers of not exercising include chronic obstructive pulmonary disease, asthma medication, and depression. Examination of individual cases often reveals clear narratives that help to tailor the exercise program, such as special dance classes for those with a portable oxygen concentrator. More broadly, the work shows how deep learning finds revealing events in a person's healthcare journey with an eye towards providing more effective care.

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Deep Dive Track

DEEP DIVE: Investment Drivers for AI Startups - GLASSWING VENTURES

11:15 AM 12:00 PM Deep Dive Track

What are the technical and business expectations of investors and venture capitalists from AI startups and soon-to-be startups? In this interactive session, Sarah Fay, Managing Director and Vlad Sejnoha, Venture Partner of Glasswing Ventures, an early-stage venture capital firm investing in the next generation of intelligent enterprise, cybersecurity, and frontier tech startups, provide hands on guidance based on their expertise and host an attendee Q&A on the key aspects VCs consider when looking to invest, why these are important and the repercussions of what not having these could mean.

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Deep Learning Summit

Machine Learning Applications in Mapping and Satellite Image Processing - FACEBOOK

11:20 AM 11:45 AM Deep Learning Summit

In this presentation, we overview the ML-assisted techniques for the satellite image processing: improving the open source OpenStreetMap and generating the state-of-the-art population density map. The key part of our pipelines are classification and segmentation deep neural networks.

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Deep Learning in Healthcare Summit

Deploying AI in the Clinic: Thinking About the Box - MIRADA MEDICAL

11:40 AM 12:05 PM Deep Learning in Healthcare Summit

As machine learning scientists working in healthcare, we get very excited about both the potential of AI technology and the results that can be achieved with it currently. However, good performance does not guarantee clinical use. In this talk, I will present some considerations that must be addressed in translating technical research into clinical products. While many of the challenges remain the same regardless of the technology used, I will focus specifically on the impact that AI has on reaching the clinic, giving examples from our experience at Mirada in commercialising deep learning-based autocontouring.

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Deep Learning Summit

Building Visual Search at Salesforce - SALESFORCE

11:45 AM 12:05 PM Deep Learning Summit

Fine-grain recognition remains an unsolved problem at in the general case, indeed, it may even be as difficult as self-driving cars. There are many technical challenges in achieving accurate production-level image retrieval at web scale (handling catalogs of tens of millions of items). This talk details the steps and highlights the hurdles in building such a search platform. At Commerce Cloud Einstein, we have developed a custom multi-stage pipeline of deep metric learning models for product detection and recognition. Our networks are trained to discover a manifold representing the space of all consumer products. We will present the current architectures in our embedding networks, i.e. the mapping from consumer images to the product feature space, as well as the most promising research directions. Implementation level details will be covered insofar as they make efficient fine-grain retrieval possible, and performance evaluation (both statistical as well as qualitative) measures will be described.

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Deep Learning in Healthcare Summit

Can AI Understand Doctor’s Notes? - CHANGE HEALTHCARE

12:05 PM 12:25 PM Deep Learning in Healthcare Summit

As a result of the transition to electronic health records, the US healthcare system now generates massive amounts of rich health information. For example, one hospital stay might generate 100 pages of data. This information can be used to dramatically improve diagnostics, provide better treatments or and optimize the US healthcare system. However, extracting knowledge from such a vast amount of unstructured information is far from easy. In this talk, we will discuss the biggest opportunities and the latest AI techniques for extracting actionable insights from healthcare information.

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Deep Learning Summit

Bayesian Deep Learning Based Exploration-Exploitation for Personalized Recommendations - FIDELITY INVESTMENTS

12:05 PM 12:25 PM Deep Learning Summit

Personalized Recommendation Systems offer a fundamental capability to identify the most appropriate content at the best time for the right individual. At Fidelity Investments, we consider personalized engagement across multiple channels as a natural extension of our deep client relationship. From a practical perspective, applications of recommender systems require an effective technique to balance exploration and exploitation. For that purpose, in this talk we will present a novel approach based on Bayesian Deep Learning. For exploitation, we show how to capture rich contextual information, and for exploration, we demonstrate how to quantify uncertainty stemming from machine learning models as well as the underlying data.

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Lunch

12:20 PM 01:20 PM Foyer Area

A hot, 3-course, lunch buffet will be served in the foyer area. A great time for networking and to get to know your fellow participants or you can join the Lunch & Learn Session taking place in the Deep Dive Track and take a seat at one of the tables to hear more from the speakers.
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Deep Dive Track

DEEP DIVE: Lunch & Learn

12:20 PM 01:20 PM Deep Dive Track

Grab your lunch from the foyer and head to the Deep Dive session room to join industry leaders to discuss their focus areas and ask your questions. Including: Falcon.Ai, Fidelity Investments, Deep Genomics & Blue River Technology Please note spaces are limited
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Deep Learning Summit

Deep Learning in Precision Agriculture for Reducing Herbicide - BLUE RIVER TECHNOLOGY

01:20 PM 01:40 PM Deep Learning Summit

The use of herbicide in agriculture has skyrocketed in the past few decades. This trend has largely been caused by new genetically modified, herbicide resistant crops. Combating the ecological side-effects of chemical overspray as well as easing the economic burden of costly herbicides is where John Deere's Blue River Technology comes in. Blue River’s flagship product is See & Spray. An intelligent machine that utilizes deep learning to automatically detect and classify crops and weeds on-the-fly and uses precision sprayers to selectively spray weeds, saving vast quantities of chemicals in the process. This presentation will cover pixelwise semantic segmentation of imagery collected real-time in the field by See & Spray machines and how that information feeds is used for targeted spraying of unwanted weeds. On the fly detection of spray allows for a closed feedback loop control system where a GPU accelerated semantic autoencoder model works in tandem with the mechanically actuated sprayer system to achieve precision farming.

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Deep Learning in Healthcare Summit

Using Artificial Intelligence to Solve Diabetes and Diabetic Retinopathy - RETINA-AI

01:25 PM 01:45 PM Deep Learning in Healthcare Summit

Diabetes is a devastating chronic disease that affects up to 35 million Americans, and up to 500 million people worldwide. The disease resulted in up to 1.6 million deaths worldwide in 2016. Early detection is the key to preventing the many morbid complications of diabetes, and key to decreasing the rate of mortality. There are too few physicians in the world to address the problem of diabetes, and furthermore the cost of relying on humans is significantly prohibitive. Artificial Intelligence (AI) presents a compelling and necessary approach to curb the effects of diabetes and thereby save millions of lives annually; while also improving the quality of life of half a billion people suffering with the disease. In this talk, I will discuss the progress RETINA-AI Health Inc is making in developing and deploying Artificial Intelligence systems to screen people around the world for diabetic retinopathy.

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Deep Learning Summit

Application of Machine Learning for Oil Production Forecasting - ANDARKO PETROLEUM

01:40 PM 02:00 PM Deep Learning Summit

One of the central questions in science is forecasting: based on the past history, how well can we predict the future? In many domains with complex multivariate correlation structures and nonlinear dynamics, forecasting is highly challenging. In the oil and gas industry, conventional approaches such as the modified hyperbolic method, have been utilized to analyze the production decline curve. Forecasting decline curves is an important component for E&P companies in business planning, asset evaluation, and decision making. Here we introduce a machine learning approach to tackle the problem, and to be more specific, an LSTM approach (LSTM stands for Long Short Term Memory, which is one kind of recurrent neural network). Compared with the hyperbolic approach, where the problem has been reduced to an over-simplified curve and essentially determined by a global curvature structure, the LSTM model is more dynamic and has a better chance of capturing non-linear events. In time series prediction, one main difficulty is how to stabilize the solution, as the error can easily accumulate over time. One way to make the algorithm more robust is through feature engineering, and here we leverage historical data from other wells, which improves our prediction significantly. We also build the prediction model from the accumulated curve domain, and eventually ensemble multiple models to reduce the variance. Given the fact that the model is only trained on the first 3 months of data (around 10% of the data), the oil rate prediction for the first 2 years shows great promise.

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Deep Dive Track

DEEP DIVE: Rising Stars

01:40 PM 02:50 PM Deep Dive Track

Learn more from young prospectives leaders of the AI world as they present their latest research across NLP, Genomics, GANs and more!

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Deep Learning in Healthcare Summit

Clinical Considerations in Implementing AI Solutions for Healthcare - PARTNERS HEALTHCARE

01:45 PM 02:05 PM Deep Learning in Healthcare Summit

AI solutions developed for healthcare are often driven by considerations around maximizing technical performance. Technical performance is a necessary but far-from-sufficient consideration in evaluating the clinical utility of AI solutions. The Data Science and AI team at Partners Healthcare Pivot Labs invests a great deal of time thinking about the right questions, working out potential pitfalls and developing best practices in evaluating AI solutions for healthcare. This presentation will share insights obtained from real projects.

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Deep Learning Summit

Optimizing the Manufacturing Process - ADVANCED ROBOTICS FOR MANUFACTURING

02:00 PM 02:20 PM Deep Learning Summit

Arnie will discuss a concept for a program/manufacturing line managers decision and reporting aid. The concept involves natural language spoken interfaces, machine learning, and inference engines to accomplish the collection, discussion, analysis, and reporting of weekly management metrics. During the analysis, trends, anomalies, and similarities are identified analyzed, subjects of concern, inferred causes, and suggestions for remediation. The goal of the system is to enable managers to have more time (and funding for staff) to spot problems as they evolve to prevent or solve them.

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Deep Learning in Healthcare Summit

AI Assisted Radiology Image Quality Assessment - GE HEALTHCARE

02:05 PM 02:25 PM Deep Learning in Healthcare Summit

Continuous quality control (QC) in the diagnostic imaging workflow is vital to maintain a high quality radiology department. Effective QC efforts often require time consuming, laborious work by medical physicists as they collect and analyze hundreds of images from multiple imaging systems. An example of an inefficiency that can be addressed with deep learning (DL) is in the detection of repeated and rejected x-ray images. DL algorithms were developed to perform automatic QC checks on chest x-ray images to minimize the effort and improve the accuracy of QC programs, enabling the delivery of efficient and quality care to patients.

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Deep Learning Summit

Natural Language Processing for Music Information Technology - BOSE CORPORATION

02:20 PM 02:40 PM Deep Learning Summit

A vast amount of music information available on social media, web pages, online forums, and digital libraries, etc., is represented in natural language. Making sense of this information is challenging due to the unstructured nature of the data. Music and language data also share many similarities such as its sequential nature. With machine learning based natural language processing (NLP) technology, we attempt to tackle the rich complexity of human languages in order to extract useful insights for tasks such as music information retrieval (MIR) and audio AI.  In this talk, I discuss the application of NLP in music information technology in the light of the latest transformations brought about by deep learning, enabling machines to make sense of the world through multimodal music and sound data. I conclude the talk by identifying emerging areas of interesting challenges and parallel trends at the intersection of these two exciting fields.

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Deep Learning in Healthcare Summit

PANEL: The Future of Healthcare - What Can We Expect?

02:25 PM 03:00 PM Deep Learning in Healthcare Summit

With so many advancements in machine learning in the health field coming to light, how can we expect to see the future healthcare landscape change? How quickly will their impacts affect outcomes and what will hinder the progress? Learn more from a range of experts in the field on their thoughts.

Speakers

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Networking Drinks

02:30 PM 03:30 PM Foyer Area

Join us in the foyer area to end the summit with a drink and a chance to reflect on the 2 days and make final connections. 
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Deep Learning Summit

Future Directions for Deep Learning in Financial Portfolio Optimization - A.I. CAPITAL MANAGEMENT

02:40 PM 03:00 PM Deep Learning Summit

First, Deepmind’s AlphaGo algorithm was applied to quantitative trading of the largest and most liquid market which is foreign exchange (FX or currencies trading) with good results. In this talk, we focus on new research directions for the use of deep learning in financial portfolio optimization. This new research is inspired by recent results in human neuroscience, and proposes using algorithms such as OpenAI Five, Meta-Learning Shared Hierarchies (MLSH) but with neural nets instead of sub-routines, and more.

Speakers

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End of Summit

03:30 PM 03:31 PM

Thank you for attending the Deep Learning Summit & Deep Learning in Healthcare Summit! You will receive access to presentation slides in the next week and access to the Video Hub will be shared the week following. We hope to see you at a future event soon!