Professor, UC Berkeley; Founder, Covariant.AI

Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Pieter’s work is frequently featured in the popular press, including New York Times, BBC, Bloomberg, Wall Street Journal, Wired, Forbes, Tech Review, NPR.

Chief Data Science Officer, Partners Healthcare; Chief Science Officer, ACR Data Science Institute

Keith J. Dreyer, DO, PhD, FACR, FSIIM, is Chief Data Science Officer and Vice President for Enterprise Medical Imaging for Partners Healthcare. He also holds the positions of Vice Chairman of Radiology at Massachusetts General Hospital, Chief Data Science and Information Officer for the Departments of Radiology at Massachusetts General Hospital and Brigham and Women’s Hospital, and Associate Professor of Radiology at the Harvard Medical School. He is ABR board certified in diagnostic radiology with a BS in Mathematics, MS in Image Processing, PhD in Computer Science and medical fellowships in Imaging Informatics and Magnetic Resonance Imaging from Harvard University at MGH. Dr. Dreyer is the Chief Science Officer for the American College of Radiology’s Data Science Institute and has held numerous board, chair, advisory and committee positions with the American College of Radiology, Radiological Society of North America, Society of Imaging Informatics in Medicine and numerous global healthcare corporations. He has authored hundreds of scientific papers, presentations, chapters, articles and books; lecturing worldwide on clinical data science, cognitive computing, clinical decision support, clinical language understudying, digital imaging standards, and implications of technology on the quality of healthcare and payment reform initiatives.

Professor and Chair of Genetics, Stanford University

Michael Snyder is the Stanford Ascherman Professor and Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. Dr. Snyder received his Ph.D. training at the California Institute of Technology and carried out postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has developed many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art “omics” technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a cofounder of several biotechnology companies, including Protometrix (now part of Life Tehcnologies), Affomix (now part of Illumina), Excelix, Personalis, Q Bio and he presently serves on the board of a number of companies.

Professor of Computer Science, Cornell Tech

Serge Belongie received a B.S. (with honor) from Caltech in 1995 and a Ph.D. from Berkeley in 2000. While at Berkeley, his research was supported by an NSF Graduate Research Fellowship. From 2001-2013 he was a professor in the Department of Computer Science and Engineering at University of California, San Diego. Since 2014 he has been a professor in the Department of Computer Science at Cornell University, and since 2019, an Associate Dean at Cornell Tech. His research interests include Computer Vision, Machine Learning, Human-in-the-Loop Computing, and Mixed Reality. He is also a co-founder of several companies including Digital Persona, Anchovi Labs and Orpix. He is a recipient of the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review “Innovators Under 35” Award and the Helmholtz Prize for fundamental contributions in Computer Vision.

Anne and Joel Ehrenkranz Dean, Icahn School of Medicine at Mount Sinai; President for Academic Affairs, Mount Sinai Health System

Dr. Charney was recruited to Icahn School of Medicine at Mount Sinai in 2004 as Dean of Research. In 2007, he became the Dean of the School and Executive Vice President for Academic Affairs of the Medical Center. In 2013, he was additionally named President for Academic Affairs for the Health System. Under Dr. Charney’s leadership, the Icahn School of Medicine has risen to a rank of 13th among U.S. medical schools in National Institutes of Health (NIH) funding, and the School currently ranks first in funding per faculty member from the NIH and other sources. With a long track record of strategic recruitments across the biomedical sciences and in genomics, computational biology, entrepreneurship, and information technology, Mount Sinai has cultivated a supercharged, Silicon Valley-like atmosphere in the academic setting. In 2009, the Icahn School of Medicine received the Spencer Foreman Award for Outstanding Community Service from the Association of American Medical Colleges.

Director, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai

Dr. Dudley is currently Associate Professor of Genetics and Genomic Sciences and Director of the Institute for Next Generation Healthcare at the Icahn School of Medicine at Mount Sinai. Prior to Mount Sinai, he held positions as Co-founder and Director of Informatics at NuMedii, Inc. and Consulting Professor of Systems Medicine in the Department of Pediatrics at Stanford University School of Medicine. His work is focused on developing and applying methods to integrate the digital universe of information to build better predictive models of disease, drug response, and scientific wellness. His work has been featured in the Wall Street Journal, Scientific American, and other popular media outlets, and he was named in 2014 as one of the 100 most creative people in business by Fast Company magazine. He is co-author of the book Exploring Personal Genomics from Oxford University Press. He received a BS in Microbiology from Arizona State University and an MS and PhD in Biomedical Informatics from Stanford University School of Medicine.

Director, Translational & Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai

Dr. Fayad serves as professor of Radiology and Medicine (Cardiology) at the Mount Sinai School of Medicine. He is the founding Director of the Translational and Molecular Imaging Institute; Vice chair for Research, Department of Radiology at the Icahn School of Medicine at Mount Sinai. Dr. Fayad’s interdisciplinary and discipline bridging research – from engineering to biology and from pre-clinical to clinical investigations – has been dedicated to the detection and prevention of cardiovascular disease with many seminal contributions in the field of multimodality biomedical imaging (MR, CT, PET and PET/MR) and nanomedicine. His work has recently expanded in understanding the effect of stress on the immune system and cardiovascular disease. He has authored more than 300 peer-reviewed publications (h-index of 71 accessed 01/02/2017 on Thomson Reuters Web of Science), 50 book chapters, and over 500 meeting presentations. He is currently the Principal Investigator (PI) of 5 federal grants (4 R01s and 1 P01) funded by the National Institutes of Health’s National Heart, Lung and Blood Institute and National institute of Biomedical Imaging and Bioengineering. He is also PI on three NIH sub-contracts with UCSD, Columbia and the Brigham and Women’s Hospital. In addition, he serves as Principal Investigator of the Imaging Core of the Mount Sinai National Institute of Health (NIH)/Clinical and Translational Science Awards (CTSA).

Dean for Therapeutic Discovery; Chief of the Division of Liver Diseases, Icahn School of Medicine at Mount Sinai

Dr. Scott L. Friedman is the Dean for Therapeutic Discovery and Chief of the Division of Liver Diseases, at the Icahn School of Medicine at Mount Sinai. He has performed pioneering research into the underlying causes of scarring, or fibrosis associated with chronic liver disease, affecting millions worldwide. Dr. Friedman was among the first to isolate and characterize the hepatic stellate cell, the key cell type responsible for scar production in liver. His work has spawned an entire field that is now realizing its translational and therapeutic potential, with new anti-fibrotic therapies for liver disease reaching clinical trials. Dr. Friedman’s work has been continuously funded by the NIH since 1985; he was awarded his first faculty NIH grant (RO1) in 1986 at the age of 31.

Director, Translational Science, Oregon State University; Director, Center for Data & Health, Oregon Health & Science University

Melissa Haendel is the Director of the Center for Data to Health (CD2H) at Oregon Health & Science University, and the Director of Translational Data Science at Oregon State University. Her background is in both wet and dry lab translational science, with a focus over the past decade on the development of ontologies, semantic engineering technologies, and open science infrastructure programs. Dr. Haendel’s vision is to weave together healthcare systems, basic science research, and patient generated data through development of data integration technologies and innovative data capture strategies. Dr. Haendel co-leads the Monarch Initiative, an international consortium dedicated to utilizing model organism genotype-phenotype data, deep phenotyping, and graph-based integration techniques to improve rare disease diagnosis. She also coleads the NCATS Data Translator, which aims to integrate hundreds of data resources for mechanism and drug discovery. The CD2H is tasked with coordinating informatics across 59 Clinical and Translational Science Award Institutes, and is focused on implementation of cloud and information architecture, clinical data model interoperability, and precision-medicine focused terminology development. Dr. Haendel is the co-lead for the GA4GH Clinical and Phenotypic workstream, where she supports cross-disciplinary international teams, development of standards for clinical genetics, and improving access to data across the world.

Professor, Radiology and Biomedical Informatics, Stanford University

Curtis P. Langlotz, MD, PhD is Professor of Radiology and Biomedical Informatics and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center) at Stanford University. As Associate Chair for Information Systems and a Medical Informatics Director for Stanford Health Care, he is responsible for the computer technology that supports the Stanford Radiology practice. The AIMI Center develops artificial intelligence methods that enable computer systems to draw precise and complex inferences directly from image information and associated clinical data, augmenting the skills of human imaging professionals. Dr. Langlotz has authored over 100 scholarly publications and the book, “The Radiology Report: A Guide to Thoughtful Communication for Radiologists and Other Medical Professionals”. He led the development of the RadLex standard terminology for radiology report information, a national standard for imaging exam codes, and a library of radiology report templates that have been downloaded over 5 million times. Dr. Langlotz is a past president of the Radiology Alliance for Health Services Research (RAHSR) and the Society for Imaging Informatics in Medicine (SIIM), and a former board member of the Association of University Radiologists (AUR), the American Medical Informatics Association (AMIA) and the Society for Medical Decision Making (SMDM). He currently serves on the Board of Directors of the Radiological Society of North America (RSNA). Dr. Langlotz has founded 3 health care information technology companies, the most recent of which was acquired by Nuance Communications in 2016.

Professor, Chair of Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering

As a Professor at the Hasso Plattner Institute for Digital Engineering in Potsdam, I lead the research group on Digital Health & Machine Learning (ML). We are working on the interface of ML and statistics, developing methods for the analysis of phenotypic measurements collected from imaging, sequencing and other high-throughput sensors. We focus on methods, all the way from the theoretical foundations, model and algorithm development, to applications in analyses of cohort studies.
After my PhD in Machine Learning (ML) and Bioinformatics at the Max Planck Institutes in Tübingen, I have been a Researcher at Microsoft Research in Los Angeles, CA, where I have been developing statistical models for quantitative genetics. From 2015-2017 I have been a Data Scientist at Human Longevity, Inc. in Mountain View, CA, a healthcare venture founded by Dr. J. Craig Venter, building the analytical methods for the interpretation of population-scale genome-sequencing data and on ML for digital phenotyping with applications in healthcare and forensics. In 2017, I have moved to the Max Delbrück Center for Molecular Medicine in Berlin.

Associate Professor of Ecology & Evolution and the Institute for Advanced Computational Science, Stony Brook University

Heather J. Lynch, Ph.D. is an Associate Professor in the Department of Ecology & Evolution at Stony Brook University and holds a joint appointment in Stony Brook’s Institute for Advanced Computational Science. Prof. Lynch received an A.B. in Physics from Princeton University, an M.A. in Physics from Harvard University, and a Ph.D. in Organismic and Evolutionary Biology from Harvard University. Prior to joining the faculty at Stony Brook, Lynch was a Postdoctoral Researcher and Research Scientist in the University of Maryland’s Department of Biology. Lynch’s research is focused on the ecology and population dynamics of Antarctic wildlife, where she runs a major field research program that is the basis for her work with remote sensing and advanced computing and statistics methodologies. For more than a decade, Lynch’s research team has been focused on developing satellite imagery interpretation as a means to survey Antarctic wildlife such as penguins, seals, and whales; more recently this work has focused on the acceleration of imagery interpretation with machine learning and techniques drawn from computer vision. Lynch was recently awarded a National Geographic/Microsoft AI for Earth award, which she is using to improve classification accuracy by integrating deep learning with prior knowledge and domain expertise.

Director, AISINAI, Icahn School of Medicine at Mount Sinai

Eric Karl Oermann, M.D. is an Instructor of Neurological Surgery in the Mount Sinai Health System and the Director of AISINAI, Mount Sinai’s artificial intelligence research group. He studied mathematics at Georgetown University with a focus on differential geometry. Prior to attending medical school, Dr. Oermann spent six months with the President’s Council on Bioethics studying human dignity under the mentorship of physician-philosopher Edmund Pellegrino, M.D.. Dr. Oermann has won numerous awards for his scholarship including fellowships from the American Brain Tumor Association and Doris Duke Charitable Research Foundation where he was first exposed to neural networks and deep learning. He has published over eighty manuscripts spanning basic research on machine learning, deep learning, and the philosophy of medicine.

Associate Professor, Electrical Engineering and Computer Science, Massachusetts Institute of Technology

David Sontag is an Associate Professor at MIT in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Medical Engineering and Science (IMES). David is also Chief Health Strategist at ASAPP, an AI startup in New York City, building a product to empower patients with chronic disease through the lens of their health data. At MIT, Dr. Sontag leads the Clinical Machine Learning group that seeks to advance the state-of-the-art in safe, robust, and fair machine learning. Motivated by the goal of translating machine learning into health care, the group studies how to learn using little labeled training data (e.g., for electronic phenotyping), infer causality from observational data (e.g., for recommending treatments), perform unsupervised learning from high-dimensional data (e.g., for disease subtyping), understand natural language (e.g., clinical text), and model heterogeneous longitudinal data (e.g., of disease progression). Recent research from the MIT Clinical Machine Learning group has focused on integrating machine learning into electronic health records to improve clinical decision support and documentation in the emergency department (with BIDMC), recommend antibiotics in outpatient settings (with MGH/BWH), and find patients with undiagnosed Type 2 diabetes (with Independence Blue Cross). Another major effort has been to develop machine learning algorithms for longitudinal data from disease registries, clinical trials, and health records to better understand multiple myeloma, rheumatoid arthritis, Parkinson’s, and heart failure.

Director, Health Data Sciences Institute, Oak Ridge National Laboratory

Georgia (Gina) Tourassi received a B.S. degree in physics from the Aristotle University of Thessaloniki, Greece and a Ph.D. in biomedical engineering from Duke University. She joined ORNL in 2011 as the director of the Biomedical Sciences and Engineering Center after a long academic career in the department of radiology and the medical physics graduate program at Duke University Medical Center. She is currently distinguished research scientist, director of the Health Data Sciences Institute (HDSI), and group leader of the Biomedical Science, Engineering, and Computing (BSEC) group at ORNL. As institute director, Dr. Tourassi develops and manages its strategic agenda, scientific priorities, and roadmap while still leading her independent research activities. In addition, she is adjunct professor of radiology at Duke University and the University of Tennessee Graduate School of Medicine, joint UT-ORNL faculty of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee at Knoxville and the Bredesen Center.

Professor, Radiology/Neuroimaging and Neurointervention, Stanford University

Dr. Zaharchuk is Professor of Radiology at Stanford University and the co-founder of the AI software company Subtle Medical, whose goal is to improve the speed, safety, and value of medical imaging using deep learning. He is an active clinical neuroradiologist and leads the Stanford Center for Advanced Functional Neuroimaging. His research focuses on advanced MRI and PET/MRI techniques and their application to alleviate neuro¬logical disease. The majority of this effort currently is put towards developing and evaluating AI methods, particularly deep learning, to apply to these tasks. His group has shown the value of AI to improve perfusion imaging, reduce MR contrast dose, reduce PET radiation dose, and to predict outcomes in acute ischemic stroke. He is currently the PI of multiple NIH R01 projects in addition to academic and industry grants. He is the vice-president of the American Society of Functional Neuroradiology (ASFNR) and an editorial board member for Radiology and Journal of Magnetic Resonance Imaging. He is most proud of the accomplishments of his students and collaborators, who are responsible for the vast majority of the work, and who have taken leadership positions in academics and in industry.

Professor, Computer Science, Rensselaer Polytechnic Institute

Mohammed J. Zaki is a Professor of Computer Science at RPI. He is also the associate department head and the graduate program director for the CS department at RPI. He received his Ph.D. degree in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining and machine learning techniques, especially for applications in text mining, bioinformatics, and personal health. He has over 250 publications, including the Data Mining and Analysis textbook published by Cambridge University Press, 2014. He is the founding co-chair for the BIOKDD series of workshops. He is currently an associate editor for Data Mining and Knowledge Discovery, and he has also served as Area Editor for Statistical Analysis and Data Mining, and an Associate Editor for ACM Transactions on Knowledge Discovery from Data, and Social Networks and Mining. He was the program co-chair for SDM’08, SIGKDD’09, PAKDD’10, BIBM’11,CIKM’12, ICDM’12, IEEE BigData’15, and CIKM’18. He is currently serving on the Board of Directors for ACM SIGKDD. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He received an HP Innovation Research Award in 2010, 2011, and 2012, and a Google Faculty Research Award in 2011. He is an ACM Distinguished Scientist and a Fellow of the IEEE. His research is supported in part by NSF, NIH, DOE, IBM, Google, HP, and Nvidia.