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Talk Videos from Machine Learning for Healthcare

August 7 - 8, 2020. Online, Los Angeles, CA, USA
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299 Speakers
79 Talks
2 Days
10 Hours of content
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About the Event

MLHC is an annual research meeting that exists to bring together two usually insular disciplines: computer scientists with artificial intelligence, machine learning, and big data expertise, and clinicians/medical researchers. MLHC supports the advancement of data analytics, knowledge discovery, and meaningful use of complex medical data by fostering collaborations and the exchange of ideas between members of these often completely separated communities. To pursue this goal, the event includes invited talks, poster presentations, panels, and ample time for thoughtful discussion and robust debate.
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Featured Speakers

Madeleine Clare Elish

Senior Research Scientist at Google
Madeleine Clare Elish is a cultural anthropologist whose work examines the social impacts of AI and automation on society. She recently joined Google as a Senior Research Scientist working on the Ethical AI team. Previously, she co-founded and led the AI on the Ground Initiative at Data & Society Research Institute, which uses social science research to inform future design, use, and governance of automated and AI systems.

She has conducted field work across varied industries and communities, ranging from the Air Force, the driverless car industry, and commercial aviation to precision agriculture and emergency healthcare. Her research has been published and cited in scholarly journals as well as publications including The New York Times, Wired, The Guardian, MIT Tech Review, Vice, and USA Today. She holds a PhD in Anthropology from Columbia University and an S.M. in Comparative Media Studies from MIT.

Ricardo Henao

Assistant Professor in Biostatistics and Bioinformatics at Duke University
Ricardo is a respected expert in machine learning. He serves as Assistant Professor in Biostatistics and Bioinformatics at Duke University and is a member of the Duke Clinical Research Institute. He also regularly serves as a reviewer for journals such as the Journal of Machine Learning Research, Journal of Biomedical Informatics, and many others.He has coauthored more than 50 academic papers in fields such as machine learning, biostatistics, neuroscience, cancer and infectious disease.

Mark Sendak

Population Health & Data Science Lead at Duke Institute for Health Innovation
Mark Sendak is the Population Health & Data Science Lead at the Duke Institute for Health Innovation, where he leads interdisciplinary teams of data scientists, clinicians, and machine learning experts to build technologies that solve real clinical problems within Duke Health. He has built tools to support Duke's first adult high-utilizerprogram, chronic disease management within a Medicare Accountable Care Organization, and inpatient deterioration management. He leads a medical student innovation scholarship and is focused on building the technology infrastructure and training the workforce required to bring medicine into a new digital age.
He has published in both technical venues, including Uncertainty and Artificial Intelligence and the Journal of Machine Learning Research, as well as clinical venues, including eGEMS, PlOS Medicine, and the Journal of Applied Clinical Informatics. He has worked with the National Institutes of Health (NIH) to develop a financial model for using health information technology to improve chronic kidney disease management as well as the American Medical Association (AMA) to advise on reimbursement mechanisms for artificial intelligence in healthcare. He obtained his Bachelors of Science in Mathematics from UCLA, Masters of Public Policy with a focus on health policy from Duke, and MD from Duke University School of Medicine.
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Featured Sessions

August 7
August 8
Emma Brunskill
Assistant professor of computer science at Stanford University
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Emma Brunskill: Learning from Little Data to Robustly Make Good Decisions

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Leora Horwitz
Director, Center for Healthcare Innovation and Delivery Science at NYU Langone Health
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Leora Horwitz: A clinician's perspective on machine learning in healthcare

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Besmira Nushi
Principal Researcher at Microsoft
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Besmira Nushi: The Unpaved Path of Deploying Reliable and Human-Centered Machine Learning Systems

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David Sontag
PhD, Associate Professor of Electrical Engineering and Computer Science at MIT
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David Sontag: Machine Learning to Guide Treatment Suggestions

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Madeleine Clare Elish
Senior Research Scientist at Google
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Madeleine Clare Elish: Repairing Innovation: The Labor of Integrating New Technologies

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Nicholson Price
Professor of Law at University of Michigan Law School
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Nicholson Price: Legal Regimes & the Spectrum of Medical AI/ML

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