Bio: Hamsa Bastani is an Associate Professor of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, social good, and revenue management. Her work has received several recognitions, including the Wagner Prize for Excellence in Practice (2021), the Pierskalla Award for the best paper in healthcare (2016, 2019, 2021), the Behavioral OM Best Paper Award (2021), as well as first place in the George Nicholson and MSOM student paper competitions (2016). She previously completed her PhD at Stanford University, and spent a year as a Herman Goldstine postdoctoral fellow at IBM Research.
Bio: Samantha Kleinberg is an Associate Professor in the Computer Science department at Stevens Institute of Technology. After completing her PhD in Computer Science in 2010 at NYU, she spent two years as a postdoctoral Computing Innovation Fellow at Columbia University, in the Department of Biomedical Informatics. Before that she was an undergraduate at NYU in Computer Science and Physics, and more recently spent a year on sabbatical in the psychology department of University College London. Dr. Kleinberg has written an academic book, Causality, Probability, and Time, and another for a wider audience, Why: A Guide To Finding and Using Causes. She is the editor of Time and Causality Across the Sciences.
Bio: Deborah Raji is a Mozilla fellow and CS PhD student at University of California, Berkeley, who is interested in questions on algorithmic auditing and evaluation. In the past, she worked closely with the Algorithmic Justice League initiative to highlight bias in deployed AI products. She has also worked with Googleʼs Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on various projects to operationalize ethical considerations in ML engineering practice. Recently, she was named to Forbes 30 Under 30 and MIT Tech Review 35 Under 35 Innovators.
Bio: Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University. Koyejo was previously an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning, focusing on applications to neuroscience and healthcare. Koyejo completed a Ph.D. at the University of Texas at Austin, and postdoctoral research at Stanford University. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence, a Skip Ellis Early Career Award, a Sloan Fellowship, a Terman faculty fellowship, an NSF CAREER award, a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping. Koyejo spends time at Google as a part of the Brain team, serves on the Neural Information Processing Systems Foundation Board, the Association for Health Learning and Inference Board, and as president of the Black in AI organization.
Bio: Nils Gehlenborg is an Associate Professor of Biomedical Informatics At Harvard Medical School. The goal of Gehlenborg’s research is to improve human health by developing computational techniques and interfaces that enable scientists and clinicians to efficiently interact with biomedical data. He received his PhD from the University of Cambridge and was a predoctoral fellow at the European Bioinformatics Institute (EMBL-EBI). Gehlenborg is a co-founder and former general chair of BioVis, the Symposium on Biological Data Visualization, and co-founder of VIZBI, the annual workshop on Visualizing Biological Data. Occasionally, he contributes to the “Points of View” data visualization column in Nature Methods.
Bio: Noémie Elhadad is Associate Professor and Chair of the Department of Biomedical Informatics at Columbia University Vagelos College of Physicians and Surgeons. She is affiliated with Columbia’s Department of Computer Science and the Columbia Data Science Institute. Dr. Elhadad’s research lies at the intersection of artificial intelligence, human-centered computing, and medicine, with a focus on developing novel machine-learning methods. She creates methods and tools to support patients and clinicians in their information needs, with particular focus on ensuring that AI systems of the future are fair and just. She obtained her PhD in 2006 in Computer Science, focusing on multi-document, patient-specific text summarization of the clinical literature. She was on the Computer Science faculty at The City College of New York and the CUNY graduate center starting in 2006 before joining the Department of Biomedical Informatics at Columbia in 2007. Dr. Elhadad served as Chair of the Health Analytics Center at the Columbia Data Science Institute from 2013 to 2016
Bio: David O. Meltzer is Chief of the Section of Hospital Medicine, Director of the Center for Health and the Social Sciences, and Chair of the Committee on Clinical and Translational Science at the University of Chicago, where he is Professor in the Department of Medicine, and affiliated faculty at the University of Chicago Harris School of Public Policy and the Department of Economics. Dr. Meltzer’s research explores problems in health economics and public policy with a focus on the theoretical foundations of medical cost-effectiveness analysis and the cost and quality of hospital care. He is currently leading a Centers for Medicaid and Medicare Innovation Challenge award to study the effects of improved continuity in the doctor patient relationship between the inpatient and outpatient setting on the costs and outcomes of care for frequently hospitalized Medicare patients. He led the formation of the Chicago Learning Effectiveness Advancement Research Network (Chicago LEARN) that helped pioneer collaboration of Chicago-Area academic medical centers in hospital-based comparative effectiveness research and the recent support of the Chicago Area Patient Centered Outcomes Research Network (CAPriCORN) by the Patient Centered Outcomes Research Institute (PCORI).
Meltzer received his MD and PhD in economics from the University of Chicago and completed his residency in internal medicine at Brigham and Women’s Hospital in Boston. Meltzer is the recipient of numerous awards, including the Lee Lusted Prize of the Society for Medical Decision Making, the Health Care Research Award of the National Institute for Health Care Management, and the Eugene Garfield Award from Research America. Meltzer is a research associate of the National Bureau of Economic Research, elected member of the American Society for Clinical Investigation, and past president of the Society for Medical Decision Making. He has served on several IOM panels, include one examining U.S. organ allocation policy and the recent panel on the Learning Health Care System that produced Best Care at Lower Cost. He also has served on the DHHS Secretary’s Advisory Committee on Healthy People 2020, the Patient Centered Outcomes Research Institute (PCORI) Methodology Committee, as a Council Member of the National Institute for General Medical Studies, and as a health economics advisor for the Congressional Budget Office.
Bio: Kyra Gan is an Assistant Professor in the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. Her research interests include adaptive/online algorithm design in personalized treatment (including micro-randomized trials and N-of-1 trials) under constraint settings, computerized/automated inference methods (e.g., targeted learning with RKHS), robust causal discovery in medical data, and fairness in organ transplants. More broadly, she is interested in bridging the gap between research and practice in healthcare.
Prior to Cornell Tech, she was a postdoctoral fellow at the Statistical Reinforcement Lab at Harvard University. She received her Ph.D. in Operations Research in 2022 from Carnegie Mellon University at the Tepper School of Business. She received her B.A.s in Mathematics and Economics from Smith College in 2017. She is a recipient of the 2021 Pierskalla Best Paper Award and the 2021 CHOW Best Student Paper Award in the Category of Operations Research and Management Science.
Bio: Dr. Dempsey is an Assistant Professor of Biostatistics and an Assistant Research Professor in the d3lab located in the Institute of Social Research at the University of Michigan. His research focuses on Statistical Methods for Digital and Mobile Health. His current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures such as interaction networks. Prior to joining, I was a postdoctoral fellow in the Department of Statistics at Harvard University. His fellowship was in the Statistical Reinforcement Learning Lab under the supervision of Susan Murphy. He received my PhD in Statistics at the University of Chicago under the supervision of Peter McCullagh.
Bio: F. Perry Wilson, MD, MSCE, is a nephrologist who treats patients in Yale New Haven Hospital who have kidney issues or who developed one while hospitalized for another problem. He is also an epidemiologist and a prolific researcher focused on studying ways to improve patient care. An associate professor at Yale School of Medicine, Dr. Wilson is director of the Yale Clinical and Translational Research Accelerator and codirector of the Yale Section of Nephrology’s Human Genetics and Clinical Research Core. He is the creator of the popular online course Understanding Medical Research: Your Facebook Friend Is Wrong" on the Coursera platform."
Bio: Girish N. Nadkarni, MD, MPH, is the Irene and Dr. Arthur M. Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai. As an expert physician-scientist, Dr. Nadkarni bridges the gap between comprehensive clinical care and innovative research. He is the System Chief of the Division of Data Driven and Digital Medicine (D3M), the Co-Director of the Mount Sinai Clinical Intelligence Center (MSCIC) and the Director of Charles Bronfman Institute for Personalized Medicine.
Before completing his medical degree at one of the top-ranked medical colleges in India, Dr. Nadkarni received training in mathematics. He then received a master’s degree in public health at the Johns Hopkins Bloomberg School of Public Health, and then was a research associate at the Johns Hopkins Medical Institute. Dr. Nadkarni completed his residency in internal medicine and his clinical fellowship in nephrology at the Icahn School of Medicine at Mount Sinai. He then completed a research fellowship in personalized medicine and informatics.
Dr. Nadkarni has authored more than 240 peer-reviewed scientific publications, including articles in the New England Journal of Medicine, the Journal of the American Medical Association, the Annals of Internal Medicine and Nature Medicine. Dr. Nadkarni is the principal or co-investigator for several grants funded by the National Institutes of Health focusing on informatics, data science, and precision medicine. He is also one of the multiple principal investigators of the NIH RECOVER consortium focusing on the long-term sequelae of COVID-19. He has several patents and is also the scientific co-founder of investor-backed companies—one of which, Renalytix, is listed on NASDAQ. In recognition of his work as an active clinician and investigator, he has received several awards and honors, including the Dr. Harold and Golden Lamport Research Award, the Deal of the Year award from Mount Sinai Innovation Partners, the Carl Nacht Memorial Lecture, and the Rising Star Award from ANIO.Bio: Roy Perlis, MD MSc is Associate Chief for Research in the Department of Psychiatry and Director of the Center for Quantitative Health at Massachusetts General Hospital. He is Professor of Psychiatry at Harvard Medical School and Associate Editor at JAMA's open-access journal, JAMA Network - Open. Dr. Perlis graduated from Brown University, Harvard Medical School and Harvard School of Public Health, and completed his residency, chief residency, and clinical/research fellowship at MGH before joining the faculty. Dr. Perlis's research is focused on identifying predictors of treatment response in brain diseases, and using these biomarkers to develop novel treatments. Dr. Perlis has authored more than 350 articles reporting original research, in journals including Nature Genetics, Nature Neuroscience, JAMA, NEJM, the British Medical Journal, and the American Journal of Psychiatry. His research has been supported by awards from NIMH, NHGRI, NHLBI, NICHD, NCCIH, and NSF, among others. In 2010 Dr. Perlis was awarded the Depression and Bipolar Support Alliance's Klerman Award; he now serves as a scientific advisor to the DBSA.
Bio: Dr. Ashley Beecy is the Medical Director of Artificial Intelligence (AI) Operations at NewYork-Presbyterian (NYP). She is a core member of NYP’s AI leadership team partnering with clinical, administrative and research leaders across the enterprise to drive digital transformation and deliver on NYP’s data and AI strategy. Dr. Beecy provides leadership in key areas including the governance, processes, and infrastructure to ensure the responsible and agile deployment of AI. She is responsible for NYP’s largest enterprise-wide AI initiative in collaboration with Cornell Tech and Cornell University. She is a thought leader and serves as a subject matter expert on multiple national AI collaboratives.
Bio: Isaac (Zak) Kohane, MD, PhD is the inaugural Chair of the Department of Biomedical Informatics and the Marion V. Nelson Professor of Biomedical Informatics at Harvard Medical School. He develops and applies computational techniques to address disease at multiple scales: from whole healthcare systems as “living laboratories” to the functional genomics of neurodevelopment with a focus on autism. Kohane earned his MD/PhD from Boston University and then completed his post-doctoral work at Boston Children’s Hospital, where he has since worked as a pediatric endocrinologist. Kohane has published several hundred papers in the medical literature and authored the widely-used books Microarrays for an Integrative Genomics(2003) and The AI Revolution in Medicine: GPT-4 and Beyond(2023). He is also Editor-in-Chief of NEJM AI.
Bio: Kyunghyun Cho is a professor of computer science and data science at New York University and a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He is also a CIFAR Fellow of Learning in Machines & Brains and an Associate Member of the National Academy of Engineering of Korea. He served as a (co-)Program Chair of ICLR 2020, NeurIPS 2022 and ICML 2022. He is also a founding co-Editor-in-Chief of the Transactions on Machine Learning Research (TMLR). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio. He received the Samsung Ho-Am Prize in Engineering in 2021.
Bio: Leo Anthony Celi has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. As clinical research director and principal research scientist at the MIT Laboratory of Computational Physiology (LCP), he brings together clinicians and data scientists to support research using data routinely collected in the intensive care unit (ICU). His group built and maintains the Medical Information Mart for Intensive Care (MIMIC) database. This public-access database has been meticulously de-identified and is freely shared online with the research community. It is an unparalleled research resource; over 2000 investigators from more than 30 countries have free access to the clinical data under a data use agreement. In 2016, LCP partnered with Philips eICU Research Institute to host the eICU database with more than 2 million ICU patients admitted across the United States. The goal is to scale the database globally and build an international collaborative research community around health data analytics.
Leo founded and co-directs Sana, a cross-disciplinary organization based at the Institute for Medical Engineering and Science at MIT, whose objective is to leverage information technology to improve health outcomes in low- and middle-income countries. At its core is an open-source mobile tele-health platform that allows for capture, transmission and archiving of complex medical data (e.g. images, videos, physiologic signals such as ECG, EEG and oto-acoustic emission responses), in addition to patient demographic and clinical information. Sana is the inaugural recipient of both the mHealth (Mobile Health) Alliance Award from the United Nations Foundation and the Wireless Innovation Award from the Vodafone Foundation in 2010. The software has since been implemented around the globe including India, Kenya, Lebanon, Haiti, Mongolia, Uganda, Brazil, Ethiopia, Argentina, and South Africa.
He is one of the course directors for HST.936—global health informatics to improve quality of care, and HST.953—secondary analysis of electronic health records, both at MIT. He is an editor of the textbook for each course, both released under an open access license. The textbook Secondary Analysis of Electronic Health Records came out in October 2016 and was downloaded over 48,000 times in the first two months of publication. The course “Global Health Informatics to Improve Quality of Care” was launched under MITx in February 2017.
Leo was featured as a designer in the Smithsonian Museum National Design Triennial “Why Design Now?” held at the Cooper-Hewitt Museum in New York City in 2010 for his work in global health informatics. He was also selected as one of 12 external reviewers for the National Academy of Medicine 2014 report “Investing in Global Health Systems: Sustaining gains, transforming lives”.Invited Talk on Research and Top Recent Papers from 2020-2022
Bio: Suchi Saria, PhD, holds the John C. Malone endowed chair and is the Director of the Machine Learning, AI and Healthcare Lab at Johns Hopkins. She is also is the Founder and CEO of Bayesian Health. Her research has pioneered the development of next generation diagnostic and treatment planning tools that use statistical machine learning methods to individualize care. She has written several of the seminal papers in the field of ML and its use for improving patient care and has given over 300 invited keynotes and talks to organizations including the NAM, NAS, and NIH. Dr. Saria has served as an advisor to multiple Fortune 500 companies and her work has been funded by leading organizations including the NIH, FDA, NSF, DARPA and CDC.Dr. Saria’s has been featured by the Atlantic, Smithsonian Magazine, Bloomberg News, Wall Street Journal, and PBS NOVA to name a few. She has won several awards for excellence in AI and care delivery. For example, for her academic work, she’s been recognized as IEEE’s “AI’s 10 to Watch”, Sloan Fellow, MIT Tech Review’s “35 Under 35”, National Academy of Medicine’s list of “Emerging Leaders in Health and Medicine”, and DARPA’s Faculty Award. For her work in industry bringing AI to healthcare, she’s been recognized as World Economic Forum’s 100 Brilliant Minds Under 40, Rock Health’s “Top 50 in Digital Health”, Modern Healthcare’s Top 25 Innovators, The Armstrong Award for Excellence in Quality and Safety and Society of Critical Care Medicine’s Annual Scientific Award.
Invited Talk on Recent Deployments and Real-world Impact
Bio: Karandeep Singh, MD, MMSc, is an Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information at the University of Michigan. He directs the Machine Learning for Learning Health Systems (ML4LHS) Lab, which focuses on translational issues related to the implementation of machine learning (ML) models within health systems. He serves as an Associate Chief Medical Information Officer for Artificial Intelligence for Michigan Medicine and is the Associate Director for Implementation for U-M Precision Health, a Presidential Initiative focused on bringing research discoveries to the bedside, with a focus on prediction models and genomics data. He chairs the Michigan Medicine Clinical Intelligence Committee, which oversees the governance of machine learning models across the health system. He teaches a health data science course for graduate and doctoral students, and provides clinical care for people with kidney disease. He completed his internal medicine residency at UCLA Medical Center, where he served as chief resident, and a nephrology fellowship in the combined Brigham and Women’s Hospital/Massachusetts General Hospital program in Boston, MA. He completed his medical education at the University of Michigan Medical School and holds a master’s degree in medical sciences in Biomedical Informatics from Harvard Medical School. He is board certified in internal medicine, nephrology, and clinical informatics.
Invited Talk on Under-explored Research Challenges and Opportunities
Bio: Dr. Nigam Shah is Professor of Medicine at Stanford University, and Chief Data Scientist for Stanford Health Care. His research group analyzes multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. At Stanford Healthcare, he leads artificial intelligence and data science efforts for advancing the scientific understanding of disease, improving the practice of clinical medicine and orchestrating the delivery of health care. Dr. Shah is an inventor on eight patents and patent applications, has authored over 200 scientific publications and has co-founded three companies. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and was inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.
Network studies: As many databases as possible or enough to answer the question quickly?
Bio: Dr. Chute is the Bloomberg Distinguished Professor of Health Informatics, Professor of Medicine, Public Health, and Nursing at Johns Hopkins University, and Chief Research Information Officer for Johns Hopkins Medicine. He is also Section Head of Biomedical Informatics and Data Science and Deputy Director of the Institute for Clinical and Translational Research. He received his undergraduate and medical training at Brown University, internal medicine residency at Dartmouth, and doctoral training in Epidemiology and Biostatistics at Harvard. He is Board Certified in Internal Medicine and Clinical Informatics, and an elected Fellow of the American College of Physicians, the American College of Epidemiology, HL7, the American Medical Informatics Association, and the American College of Medical Informatics (ACMI), as well as a Founding Fellow of the International Academy of Health Sciences Informatics; he was president of ACMI 2017-18. He is an elected member of the Association of American Physicians. His career has focused on how we can represent clinical information to support analyses and inferencing, including comparative effectiveness analyses, decision support, best evidence discovery, and translational research. He has had a deep interest in the semantic consistency of health data, harmonized information models, and ontology. His current research focuses on translating basic science information to clinical practice, how we classify dysfunctional phenotypes (disease), and the harmonization and rendering of real-world clinical data including electronic health records to support data inferencing. He became founding Chair of Biomedical Informatics at Mayo Clinic in 1988, retiring from Mayo in 2014, where he remains an emeritus Professor of Biomedical Informatics. He is presently PI on a spectrum of high-profile informatics grants from NIH spanning translational science including co-lead on the National COVID Cohort Collaborative (N3C). He has been active on many HIT standards efforts and chaired ISO Technical Committee 215 on Health Informatics and chaired the World Health Organization (WHO) International Classification of Disease Revision (ICD-11).
Network studies: As many databases as possible or enough to answer the question quickly?
Bio: Robert Platt is Professor in the Departments of Epidemiology, Biostatistics, and Occupational Health, and of Pediatrics, at McGill University. He holds the Albert Boehringer I endowed chair in Pharmacoepidemiology, and is Principal Investigator of the Canadian Network for Observational Drug Effect Studies (CNODES). His research focuses on improving statistical methods for the study of medications using administrative data, with a substantive focus on medications in pregnancy. Dr. Platt is an editor-in-chief of Statistics in Medicine and is on the editorial boards of the American Journal of Epidemiology and Pharmacoepidemiology and Drug Safety. He has published over 400 articles, one book and several book chapters on biostatistics and epidemiology.
Data Heterogeneity: More Heterogeneous Data or Less Homogeneous Data?
Bio: Tianxi Cai is John Rock Professor of Translational Data Science at Harvard, with joint appointments in the Biostatistics Department and the Department of Biomedical Informatics. She directs the Translational Data Science Center for a Learning Health System at Harvard Medical School and co-directs the Applied Bioinformatics Core at VA MAVERIC. She is a major player in developing analytical tools for mining multi-institutional EHR data, real world evidence, and predictive modeling with large scale biomedical data. Tianxi received her Doctor of Science in Biostatistics at Harvard and was an assistant professor at the University of Washington before returning to Harvard as a faculty member in 2002.
Data Heterogeneity: More Heterogeneous Data or Less Homogeneous Data?
Bio: Dr. Yong Chen is Professor of Biostatistics at the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania (Penn). He directs a Computing, Inference and Learning Lab at University of Pennsylvania, which focuses on integrating fundamental principles and wisdoms of statistics into quantitative methods for tackling key challenges in modern biomedical data. Dr. Chen is an expert in synthesis of evidence from multiple data sources, including systematic review and meta-analysis, distributed algorithms, and data integration, with applications to comparative effectiveness studies, health policy, and precision medicine. He has published over 170 peer-reviewed papers in a wide spectrum of methodological and clinical areas. During the pandemic, Dr. Chen is serving as Director of Biostatistics Core for Pedatric PASC of the RECOVER COVID initiative which a national multi-center RWD-based study on Post-Acute Sequelae of SARS CoV-2 infection (PASC), involving more than 13 million patients across more than 10 health systems. He is an elected fellow of the American Statistical Association, the American Medical Informatics Association, Elected Member of the International Statistical Institute, and Elected Member of the Society for Research Synthesis Methodology.
Differential Privacy vs. Synthetic Data
Bio: Dr. Khaled El Emam is the Canada Research Chair (Tier 1) in Medical AI at the University of Ottawa, where he is a Professor in the School of Epidemiology and Public Health. He is also a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute and Director of the multi-disciplinary Electronic Health Information Laboratory, conducting research on privacy enhancing technologies to enable the sharing of health data for secondary purposes, including synthetic data generation and de-identification methods. Khaled is a co-founder of Replica Analytics, a company that develops synthetic data generation technology, which was recently acquired by Aetion. As an entrepreneur, Khaled founded or co-founded six product and services companies involved with data management and data analytics, with some having successful exits. Prior to his academic roles, he was a Senior Research Officer at the National Research Council of Canada. He also served as the head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. He participates in a number of committees, number of the European Medicines Agency Technical Anonymization Group, the Panel on Research Ethics advising on the TCPS, the Strategic Advisory Council of the Office of the Information and Privacy Commissioner of Ontario, and also is co-editor-in-chief of the JMIR AI journal. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement. He held the Canada Research Chair in Electronic Health Information at the University of Ottawa from 2005 to 2015. Khaled has a PhD from the Department of Electrical and Electronics.
Differential Privacy vs. Synthetic Data
Bio: Li Xiong is a Samuel Candler Dobbs Professor of Computer Science and Professor of Biomedical Informatics at Emory University. She held a Winship Distinguished Research Professorship from 2015-2018. She has a Ph.D. from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from the University of Science and Technology of China. She and her research lab, Assured Information Management and Sharing (AIMS), conduct research on algorithms and methods at the intersection of data management, machine learning, and data privacy and security, with a recent focus on privacy-enhancing and robust machine learning. She has published over 170 papers and received six best paper or runner up awards. She has served and serves as associate editor for IEEE TKDE, IEEE TDSC, and VLDBJ, general co-chair for ACM CIKM 2022, program co-chair for IEEE BigData 2020 and ACM SIGSPATIAL 2018, 2020, program vice-chair for ACM SIGMOD 2024, 2022, and IEEE ICDE 2023, 2020, and VLDB Sponsorship Ambassador. Her research is supported by federal agencies including NSF, NIH, AFOSR, PCORI, and industry awards including Google, IBM, Cisco, AT&T, and Woodrow Wilson Foundation. She is an IEEE felllow.
Bio: Katie Link is a Machine Learning Engineer at Hugging Face, where she leads healthcare and life sciences applications of artificial intelligence. Her work at Hugging Face aims to democratize the responsible research and development of biomedical machine learning artifacts, including models and datasets. Prior to Hugging Face, she led artificial intelligence research at NYU Langone Hospital, creating the largest open dataset of magnetic resonance imaging (MRI) for brain metastases and developing novel deep learning algorithms for tracking cancer progression. She is currently based in New York City and is on leave as a medical student at the Icahn School of Medicine at Mount Sinai. In her spare time, she also works on AI education initiatives for medical trainees and physicians. Prior to medical school, she was an AI Resident at Google X and a data analyst at the Allen Institute for Brain Science. She holds a bachelor’s degree in Neuroscience with a minor in Computer Science from Johns Hopkins University.
Bridging the gap between the business of value-based care and the research of health AI
Value-Based Care (VBC) is getting its momentum. The Centers for Medicare and Medicaid Services (CMS) is pushing to have all Medicare fee-for-service beneficiaries under a care relationship with accountability for quality and total cost of care by 2030. However, the business of VBC is more complex and is different from other businesses as it needs to satisfy three-part aims simultaneously; they are 1) better care for individuals, 2) better health for populations, and 3) lower cost. Meeting all three aims is challenging, and the details and implications of these aims are not well-known for healthcare machine learning researchers. Therefore, we want to pick a few papers from this and past years' CHIL proceedings. Then, we would like to brainstorm and discuss how those ideas in the papers can be deployed in practice, what are the barriers to the deployment/sales, what are the hidden or visible incentives for adopting such ideas, how the government and policymakers should incentivize to achieve the three-part aims of CMS while encouraging the adoption of such technologies.
Bio: Yubin Park, Ph.D., is Chief Data and Analytics Officer at Apollo Medical Holdings, Inc. (ApolloMed, NASDAQ: AMEH). He oversees value-based care analytics, remote patient monitoring, and partnerships with third-party data vendors in his current position. Yubin started his career by founding a healthcare analytics start-up after obtaining his Ph.D. degree in Machine Learning at the University of Texas at Austin in 2014. His first start-up, Accordion Health, provided an AI-driven Risk Adjustment and Quality analytics platform to Medicare Advantage plans. In 2017, Evolent Health (NYSE: EVH) acquired his company, and there, he led various clinical transformation/innovation projects. In 2020, he then founded his second start-up, Orma Health. The company built a virtual care and analytics platform for payers and providers in value-based care, e.g., Direct Contracting Entities and Accountable Care Organizations. At Orma, he worked with many sizes of risk-bearing primary care and specialty groups, helping them connect with patients through virtual care technologies. ApolloMed acquired Orma Health in 2022.
Auditing Algorithm Performance and Equity
Machine learning algorithms should be easy to evaluate for performance and equity: they generate quantitative predictions that can be compared to their intended target, both in the general population and in under-served groups. But the scarcity of data means that, for most algorithms, we have no idea how they perform, and how much bias they contain. Concretely, there is no way for algorithm developers or potential users to answer the simple question: does this algorithm do what it’s supposed to do? This roundtable will focus on the opportunities and challenges of auditing algorithm performance and equity.
Bio: Dr. Johnson is a Scientist at the Hospital for Sick Children. He received his Bachelor of Biomedical and Electrical Engineering at McMaster University and successfully read for a DPhil at the University of Oxford. Dr. Johnson is most well-known for his work on the MIMIC-III Clinical database, a publicly available critical care database used by over 30,000 researchers around the world. His research focuses on the development of new database structures tailored for healthcare and machine learning algorithms for natural language processing, particularly focusing on the deidentification of free-text clinical notes.