Panel: Experiments, models, inference and algorithms: Learning from experts who do it all

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Microsoft Research
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Published on ● Video Link: https://www.youtube.com/watch?v=HqPcN5Chh2g


Duration: 27:57
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Moderator: Kristen Severson, Sr. Researcher, Microsoft Research New England

Speakers:
Ava Soleimany, Sr. Researcher, Microsoft Health Futures
Fei Chen, Assistant Professor, Broad Institute
Jabran Zahid, Researcher, Microsoft Health Futures
Noémie Elhadad, Associate Professor, Columbia University

Research in computational biology and healthcare benefits from the tight integration of data generation, model specification, and inference or learning. Many machine learning (ML) applications in these fields require data that is very expensive and/or requires an expert to acquire, have access to only small datasets, and target end-use in high-risk settings. These challenges lead to a greater importance of multidisciplinary teams and skills that inform the joint development of experimental data and computational approaches. Recent examples show the power of integration for achieving research impact while applications without integration serve as cautionary tales. In this talk, we give a brief description of exemplary project-combining experiments and computation, followed by a panel discussion on the difficulties and best practices surrounding these projects.

Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit



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Tags:
future of healthcare
healthcare
biology
biotechnology
precision medicine
AI in health
medical data
experimental data in healthcare
biological research
microsoft research summit