Welcome and Opening Remarks: Eric Schmidt

This panel will explore the challenges and opportunities of finding and building meaningful datasets within health systems leveraging concrete examples the panelists have in their experience of building and working with healthcare datasets. Featured panelists include: Judy Gichoya, Assistant Professor of Radiology and Imaging, Emory; Matt Lungren, Director of the Stanford Center for Artificial Intelligence in Medicine & Imaging; Ari Robiczek, Chief Medical Analytics Officer, Providence St. Joseph Health

Talks will feature new datasets and selected research submissions. Features may include electrocardiogram waveform (ECG) dataset for heart attack prediction and prediction of sudden cardiac death, and a dataset of breast cancer biopsy slides for insight into the cancer staging process.

Through a moderated conversation, panlists Eric Topol and Kevin Volpp will explore the biggest barriers to meaningful research for medical unknowns that we still face, and what can help. This panel will touch on Kevin Volpp's story; learn more here:

This focus of this session will be twofold, leveraging the panelists’ experience: 1) the importance of well-structured data-science programs within academia as a way to ensure a well-trained, robust pipeline of up-and-coming researchers, and provide the data infrastructure necessary to support ground breaking research and 2) examine what can be done at the federal level to encourage better data sharing and research.

Featured panelists include Aneesh Chopra, President of CareJourney, first U.S. Chief Technology Officer; Jennifer Chayes, Associate Provost, Computing, Data Science, and Society, UC Berkeley; Kate Baicker, Dean and Emmett Dedmon Professor, Chicago Harris School of Public Policy

This panel will begin by introducing three critical medical issues that drive mortality despite years of research: cancer, sudden cardiac death, maternal mortality. The moderator will facilitate a group conversation about how the panelists approach working on issues like these in their research, as well as challenges and opportunities in applying new data and ML tools to similar issues in medicine.

Featured panelists include Regina Barzilay, Distinguished Professor for AI and Health, EECS, MIT; Marzyeh Ghassemi, Assistant Professor, EECS and IMES, MIT, Faculty Member, The Vector Insitute Elaine Nsoesie, Assistant Professor, Boston University School of Public Health; Emma Pierson, Assistant Professor of Computer Science, Jacobs Technion-Cornell Institute at Cornell Tech

Poster session featuring accepted papers

This session will be a conversation-style panel focusing on how funding decisions are made in computational medicine, what funders and other stakeholders can do to increase data sharing and research, and will culminate with an announcement of a new computational medicine funding collaborative.

This session will announce the launch of Nightingale Open Science and feature a demonstration of the product. Today, health data are mostly locked up in small sandboxes, controlled by a handful of private companies or well-resourced researchers. Nightingale Open Science aims to unlock those data, securely and ethically, and make them available for the public good. Just as ImageNet jumpstarted the field of machine vision, Nightingale seeks to build a community of researchers working in a new scientific field: ‘computational medicine.’ Nightingale OS datasets are curated around medical mysteries—heart attack, cancer metastasis, cardiac arrest, bone aging, Covid-19—where machine learning can be transformative.