SINAInnovations 2019

About

The Icahn School of Medicine at Mount Sinai hosted its 8th annual SINAInnovations conference on October 15-16, 2019. This year’s theme of Artificial Intelligence focused on the explosive growth of AI in our society and in particular in medicine. The program included keynote addresses and panel discussions featuring international thought leaders across the range of relevant domains.

Sessions and lectures included: AI in imaging, Robotics AI, and the emergence of AI in our lives and medical decision making, as well as the ethics associated with AI.

The conference layed out the promise and challenges of artificial intelligence as it becomes integrated into all aspects of our lives, society and health.

Event Organizers: Joel Dudley, Zahi Fayad, Scott L. Friedman, Patricia Kovatch, David S. Mendelson, Eric Karl Oermann

If you have any questions, please contact us at sinainnovations@mssm.edu.

2019 KEYNOTE SPEAKERS

Pieter Abbeel

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.

Keith J. Dreyer

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.

Michael Snyder

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.

Suchi Saria

John C. Malone Assistant Professor, Johns Hopkins University

Suchi Saria is the John C. Malone Assistant Professor of computer science, statistics, and health
policy and the Director of the Machine Learning and Healthcare Lab at Johns Hopkins University.
She is also the founding Research Director of the Malone Center for Engineering in Healthcare at
Hopkins. Her research has pioneered the development of next generation diagnostic and treatment
planning tools that use statistical learning methods to individualize care. In sepsis, a life-threatening
condition, her team’s work first demonstrated the use of machine learning to integrate diverse
signals to make early detection possible (Science Trans. Med. 2015). In Parkinson’s, her team’s
work showed a first demonstration of using readily-available sensors to easily track and measure
symptom severity at home, which can serve to optimize treatment management (JAMA Neurology 2018).
Her work has received recognition in numerous forms including best paper awards at machine
learning, informatics, and medical venues, a Rambus Fellowship (2004-2010), an NSF Computing
Innovation Fellowship (2011), selection by IEEE Intelligent Systems to Artificial Intelligence’s “10 to
Watch” (2015), the DARPA Young Faculty Award (2016), MIT Technology Review’s ‘35 Innovators
under 35’ (2017), the Sloan Research Fellowship (2018), and the World Economic Forum Young
Global Leader (2018). In 2017, her work was among four research contributions presented by Dr.
France Córdova, Director of the National Science Foundation to Congress’ Commerce, Justice
Science Appropriations Committee. She was invited to join the National Academy of Engineering’s
Frontiers of Engineering in 2017 and more recently to the National Academy of Medicine’s
Emerging Leaders in Health and Medicine. Saria is on the editorial board of the Journal of Machine
Learning Research and ACM’s new journal Health. She joined Hopkins in 2012. Prior to that, she
received her PhD from Stanford University working with Prof. Daphne Koller.

Schedule 2019

Tuesday October 15, 2019

8:30 – 9:00 am: Breakfast/Registration
Location: Annenberg West Lobby

9:00 – 9:05 am: Welcome Remarks
Dennis S. Charney, Anne and Joel Ehrenkranz Dean, Icahn School of Medicine at Mount Sinai; President for Academic Affairs, Mount Sinai Health System

9:05 – 9:35 am: Keynote Address – Intro to AI
Introduction: Zahi Fayad, Director, Translational & Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai
Speaker: 
Michael Snyder, Professor and Chair of Genetics, Stanford University

9:40 – 9:50 am: Lightning Talk – Using Reason(ers): Classification of Patients for Diagnosis, Care Management, and Translational Research
Melissa Haendel, Director, Translational Science, Oregon State University; Director, Center for Data & Health, Oregon Health & Science University

9:50 – 10:00 am: Lightning Talk – Bias in Automated Decision Making
Suresh Venkatasubramanian, Professor, School of Computing, University of Utah

10:00 – 10:10 am: Lightning Talk – AI-Powered Electronic Medical Records
David Sontag, Associate Professor, Electrical Engineering and Computer Science, Massachusetts Institute of Technology

10:10 – 10:45 am: Panel Discussion
Moderator: Zahi Fayad, Director, Translational & Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai
Panelists:
• Melissa Haendel, Director, Translational Science, Oregon State University; Director, Center for Data & Health, Oregon Health & Science University
• Eric Karl Oermann, Director, AISINAI, Icahn School of Medicine at Mount Sinai
• Michael Snyder, Professor and Chair of Genetics, Stanford University
• David Sontag, Associate Professor, Electrical Engineering and Computer Science, Massachusetts Institute of Technology
• Suresh Venkatasubramanian, Professor, School of Computing, University of Utah

10:45 – 11:00 am: Break

11:00 – 11:30 am: Keynote Address – AI Imaging
Introduction: David S. Mendelson, Professor, Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai
Speaker: Keith J. Dreyer, Chief Data Science Officer, Partners Healthcare; Chief Science Officer, ACR Data Science Institute

11:30 – 11:45 am: Lightning Talk – What the Deep Learning Revolution Means for the Medical Imaging of the Future
Greg Zaharchuk, Professor, Radiology/Neuroimaging and Neurointervention, Stanford University

11:45 am – 12:00 pm: Lightning Talk – Building a Healthy AI Ecosystem for Medical Imaging
Curtis P. Langlotz, Professor, Radiology and Biomedical Informatics, Stanford University

12:00 – 12:45 pm: Panel Discussion
Moderator: David S. Mendelson, Professor, Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai
Panelists:
• Keith J. Dreyer, Chief Data Science Officer, Partners Healthcare; Chief Science Officer, ACR Data Science Institute
• Curtis P. Langlotz, Professor, Radiology and Biomedical Informatics, Stanford University
• Greg Zaharchuk, Professor, Radiology/Neuroimaging and Neurointervention, Stanford University

Wednesday October 16, 2019

9:30 – 10:00 am: Breakfast/Registration

Location: Annenberg West Lobby

10:00 – 10:30 am: Keynote Address
Introduction: Eric Karl Oermann, Director, AISINAI, Icahn School of Medicine at Mount Sinai
Speaker: Suchi Saria, John C. Malone Assistant Professor, Johns Hopkins University

10:30 – 10:40 am: Lightning Talk – Privacy-Preserving Deep Learning NLP Solutions in Population Cancer Surveillance

Georgia Tourassi, Director, Health Data Sciences Institute, Oak Ridge National Laboratory

10:40 – 10:50 am: Lightning Talk – Towards AI-driven Precision Medicine

Olivier Elemento, Director, Englander Institute for Precision Medicine; Associate Director, Institute for Computational Biomedicine, Weill Cornell Medicine

10:50 – 11:00 am: Lightning Talk – Zooming (Way) Out: AI for Studying the Earth and All That Lives on It

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

11:00 – 11:40 am: Panel Discussion

Moderator: Patricia Kovatch, Senior Associate Dean for Scientific Computing and Data Science, Icahn School of Medicine at Mount Sinai
Panelists:
• Pieter Abbeel, Professor, UC Berkeley; Founder, Covariant.AI
• Olivier Elemento, Director, Englander Institute for Precision Medicine; Associate Director, Institute for Computational Biomedicine, Weill Cornell Medicine
• Heather J. Lynch, Associate Professor of Ecology & Evolution and the Institute for Advanced Computational Science, Stony Brook University
• Georgia Tourassi, Director, Health Data Sciences Institute, Oak Ridge National Laboratory

11:40 am – 12:00 pm: Break

12:00 – 12:30 pm: Keynote Address – Robotics AI

Introduction: Christoph Lippert, Professor, Chair of Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering
Speaker: Pieter Abbeel, Professor, UC Berkeley; Founder, Covariant.AI

12:30 – 12:45 pm: Lightning Talk – From Visipedia to PointAR
Serge Belongie, Professor of Computer Science, Cornell Tech

12:45 – 1:00 pm: Lightning Talk – Semantics-Aware Machine Learning for Personalization in Health
Mohammed J. Zaki, Professor, Computer Science, Rensselaer Polytechnic Institute

1:00 – 1:30 pm: Panel Discussion

Moderator: Christoph Lippert, Professor, Chair of Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering

Panelists:
• Serge Belongie, Professor of Computer Science, Cornell Tech
• Eric Karl Oermann, Director, AISINAI, Icahn School of Medicine at Mount Sinai
• Mohammed J. Zaki, Professor, Computer Science, Rensselaer Polytechnic Institute

1:30 – 1:35 pm: Special Announcement
Keystone for Incubating Innovation in Life sciences Network (KiiLN) Presentation

1:35 – 1:40 pm: Closing Remarks

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

Speakers 2019

Serge Belongie

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.

Dennis S. Charney

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.

Joel Dudley

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.

Olivier Elemento

Director, Englander Institute for Precision Medicine; Associate Director, Institute for Computational Biomedicine, Weill Cornell Medicine

I direct the Englander Institute for Precision Medicine, an Institute that focuses on using genomics and informatics to make medicine more individualized. My research group and I combine Big Data with experimentation and genomic profiling to accelerate the discovery of cancer cures. In cancers, we are elucidating the patterns of aberrant pathway activities, rewiring of regulatory networks and cancer mutations that have occurred in cancer cells. We are also trying to understand how tumors evolve at the genomic and epigenomic level. We use high-throughput sequencing (ChIP-seq, RNA-seq, bisulfite conversion followed by sequencing – specifically RRBS-, ATAC-seq, exome capture and sequencing, single cell RNAseq using DropSeq) to decipher epigenetic mechanisms and regulatory networks at play in malignant cells and study how they affect gene expression. Our research has led to the development of the first New York State approved whole exome sequencing test for oncology, which is now used routinely on patients treated at Weill Cornell Medicine/NewYork Presbyterian Hospital. I have had the privilege to mentor over 15 Weill Cornell graduate students and postdoctoral fellows. Subsequent to mentorship, a former student went on to find OneThree Biotech, a biotechnology startup developing artificial intelligence platforms to accelerate drug development by efficiently predicting drug targets and clinical effects. Two were chosen as the 30 under 30 in Healthcare by Forbes Magazine. I have also enjoyed many productive partnerships with my Weill Cornell colleagues over the years and look forward to continued collaboration.

Zahi Fayad

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).

Scott L. Friedman

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.

Melissa Haendel

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.

Patricia Kovatch

Senior Associate Dean for Scientific Computing and Data Science, Icahn School of Medicine at Mount Sinai

Patricia Kovatch is the Senior Associate Dean for Scientific Computing and Data Science at the Icahn School of Medicine at Mount Sinai (ISMMS), founding the division in October 2011. Prior to joining ISMMS, Kovatch directed NSF’s supercomputer center at the University of Tennessee, Knoxville, located at DOE’s Oak Ridge National Laboratory. There she deployed the world’s third fastest supercomputer in 2009 (a $75M Cray XT3). In her work at ISMMS and in her national and international collaborations, she emphasizes a collaborative approach, partnering computational and data experts with basic and translational scientists to tackle complex scientific questions to better diagnose and treat disease. To these ends, she established a scalable and sustainable high-performance computing infrastructure and scientific support staff, and oversees the Mount Sinai Data Warehouse that houses clinical records on over 8 million patients and clinical database groups. She is PI for the Big Omics Data Engine equipment grant. She provides vision and strategy for data science and sharing at ISMMS, and nationally as the Director for the Data Repository and Management Core for NIEHS’s Children’s Health Exposure Analysis Resource, and as the Data Sharing Lead for Sinai’s Cancer Immune Monitoring and Analysis Core, funded by NCI.

Curtis P. Langlotz

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.

Christoph Lippert

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.

Heather J. Lynch

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.

David S. Mendelson

Professor, Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai

Dr. Mendelson is Professor of Radiology and Vice Chair of Radiology Information Technology for The Mount Sinai Health System. In addition he is Associate CMIO of the Mount Sinai Doctors Faculty Practice. He has had an active career in Health Information Technology, being involved with the selection and implementation of many systems including 2 generations of EMR products and leading the recent deployment of our single PACS across the entire merged Mount Sinai enterprise. On national and international levels, Dr. Mendelson holds several leadership positions including Co-Chair of Integrating the Healthcare Enterprise International, Co-Chair of the HANYS HIT committee and is a member of the Advisory Council of Carequality (The Sequoia Project). In these positions he focuses on interoperability and has participated in shaping national and international standards. He is a long standing member of the RSNA Radiology Informatics Committee.

Eric Karl Oermann

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.

David Sontag

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.

Georgia Tourassi

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.

Suresh Venkatasubramanian

Professor, School of Computing, University of Utah

Suresh Venkatasubramanian is a professor at the University of Utah. His background is in algorithms and computational geometry, as well as data mining and machine learning. His current research interests lie in algorithmic fairness, and more generally the problem of understanding and explaining the results of black box decision procedures. Suresh was the John and Marva Warnock Assistant Professor at the U, and has received a CAREER award from the NSF for his work in the geometry of probability, as well as a test-of-time award at ICDE 2017 for his work in privacy. His research on algorithmic fairness has received press coverage across North America and Europe, including NPR’s Science Friday, NBC, and CNN, as well as in other media outlets. He is a member of the Computing Community Consortium Council of the CRA, a member of the board of the ACLU in Utah, and a member of  New York City’s Failure to Appear Tool (FTA) Research Advisory Council.

Greg Zaharchuk

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.

Mohammed J. Zaki

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.

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The Mount Sinai Hospital, Stern Auditorium is located at:
1468 Madison Avenue. New York, New York 10029

Discoveries

Sinai Discoveries: 1887 – Present

1887

1887 — Bernard Sachs and Tay-Sachs Disease

Bernard Sachs first described “Familial Amaurotic Idiocy” – in U.S., a condition later named Tay-Sachs Disease.

 

1929

1929 — Arthur Master and the “Master Two-Step”

Arthur Master devised prototype for today’s cardiac “Master Two-Step.”

1932

1932 — Crohn's Disease

Leon Ginzberg, Gordon Oppenheimer, and Burrill B. Crohn formulated the first description of “regional ileitis,” later known as Crohn’s Disease.

1947

1947 — Kidney Dialysis

Mount Sinai performed the country’s first kidney dialysis.

1959

1959 — Immunoassay

Solomon Berson and Rosalyn Yalow noted the conditions necessary for performing immunoassay, which later garnered Dr. Yalow a Nobel Prize.

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1962

1962 — Greenspan and Fieber

Ezra Greenspan and M. Fieber used a sequential combination regimen of chemotherapy for treatment of ovarian and breast cancers (1962-63).

1964

1964 — Irwin Selikoff

Irvin Selikoff and colleagues showed link between asbestos exposure and the formation of neoplasms.

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1969

1969 — Genetically Engineered Vaccine

Edwin Kilbourne created the first genetically engineered vaccine

1971

1971 — Cancer Therapy Inroads

Charlotte Friend and colleagues demonstrated that dimethyl sulfoxide could induce cancer cells, opening the way to less toxic cancer therapy.

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1974

1974 — Alcohol and Liver Disease

Emanuel Rubin and Charles Lieber showed that alcohol is toxic to the liver when the conventional wisdom was that poor nutrition, not alcohol, caused liver disease.

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1980

1980 — Immunosuppressive Agents and IBD

Daniel Present and colleagues established immunosuppressive agents as the first line therapy for IBD.

1982

1982 — Davis and Mohs Treat Alzheimer's Disease

Ken Davis and Richard Mohs used a specific cholinesterase inhibitor to treat Alzheimer’s Disease.

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1991

1991 — Marfan Syndrome Gene Identified

Francesco Ramirez and colleagues identified the gene for Marfan Syndrome.

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1995

1995 — East Harlem Visiting Doctors

Mount Sinai and Visiting Doctors brought medical care to homebound patients in East Harlem.

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1996

1996 — Children's Vulnerability to Toxins

Philip Landrigan and colleagues documented the unique vulnerability of infants and children to pesticides and other toxic chemicals in the environment.

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1999

1999 — Creation of First Palliative Care Institute

The Hertzberg Palliative Care Institute created as the first and only one of its kind to combine clinical, educational, and research activities together with a national dissemination platform.

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2002

2002 — Mount Sinai World Trade Center Health Program

Stephen Lewis and colleagues organized the Mount Sinai World Trade Center Health Program in the aftermath of the September 11, 2001 terrorist attacks.

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2003

2003 — Peanut Allergy Treatment

Hugh Sampson and colleagues developed a treatment for peanut allergy.

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2003

2003 — Fabry's Disease Treatment Developed

Robert Desnick and colleagues developed fabrazyme to treat individuals suffering from Fabry’s Disease.

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2004

2004 — Common Human Tumor Advancements

Stuart Aaronson and colleagues showed that a Wnt autocrine mechanism is responsible for activation of Wnt canonical signaling in common human tumors.

2004

2004 — Autism Gene Identified

Joseph Buxbaum and colleagues identified first common gene variant linked to autism.

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2005

2005 — First Successful Composite Tracheal Transplant

Eric Genden performed the world’s first successful composite tracheal transplant.

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2010

2010 — First Cardiovascular Disease Model

Ihor Lemischka and Bruce Gelb developed the first cardiovascular disease model using human induced pluripotent stem cells.

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2011

2011 — Bacteria Receptor System Identified

Julie Blander identified a receptor system for detecting bacterial viability.

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2012

2012 — New Cancer Treatment Approaches

Ross Cagan developed a cancer model in drosphila (fruit fly), and used it to create a whole new approach to the discovery of cancer treatments.

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2012

2012 — Personal SNP Profile Developed

Eric E. Schadt and colleagues developed a technique for generating a personal SNP profile, or a DNA “bar code.”

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2012

2012 — New Molecular Pathway Discovered

Jilian Shapiro discovered a new molecular pathway that will advance selective gene delivery for many diseases.

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2014

2014 — Depression, PTSD and Ketamine

Adriana Feder and James Murrough found the drug ketamine could provide significant relief to patients with major depressive disorder and PTSD.

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2014

2014 — Schizophrenia and Gene Mutations Link Discovered

Pamela Sklar and Shaun Purcell discovered that rare mutations in specific sets of genes may increase one’s risk of developing schizophrenia.

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2014

2014 — Mount Sinai Institute of Technology Established

Mount Sinai Institute of Technology to Become Hub of Innovation.

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2016

2016 — Diabetes Discoveries

Use of Big Data Leads to Discovery in Diabetes.

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2016

2016 — New Aortic Aneurysm Treatments

Mount Sinai Vascular Surgeons Among First in Nation To Treat Complex Aortic Aneurysm With New Device.

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2016

2016 — Transgender Medicine and Surgery Center Opens

Mount Sinai Opens a Comprehensive Center For Transgender Medicine and Surgery.

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2016

2016 — First HIV-Positive Kidney Transplant Performed

Mount Sinai Surgeons Are First in New York State To Perform HIV-Positive Kidney Transplant.

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2016

2016 — Public-Private Cancer Discovery Collaboration Forged

Unique Public-Private Collaboration with Celgene To Accelerate Important Cancer Discoveries.

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2017

2017 — Mount Sinai Announces New Capital Campaign

New Capital Campaign to Bring Transformational Growth.

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2017

2017 — Genetic Variation Predictors

Novel Tool Finds Genetic Variations That Predict Disease.

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2017

2017 — New Institute for Exposomic Research

New Institute for Exposomic Research Will Study Lifelong Effects of Environmental Exposures.

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2017

2017 — New Cancer Cell Findings

Surprising New Evidence of How Cancer Cells Spread.

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2017

2017 — New Peanut Allergy Guidelines Developed

Mount Sinai Physicians Help Develop New Allergy Guidelines Urging Early Introduction of Peanuts.

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