Nightingale Open Science Launch

Download Press PACKET

NeurIPS 2021

Nightingale Open Science will host a full-day workshop at NeurIPS on December 14, 2021 from 10am - 5:10pm Eastern Standard Time. Detailed information on research submissions can be found on our Call for Participation:

Call for Participation

Further details can be found in our press packet. (PDF)

Confirmed Speakers:

Morning Program: New Datasets, New Research Questions 10am - 1:45pm

10am - 10:15: Welcome and Opening Remarks: Eric Schmidt 

10:15 - 10:20: Transition break

10:20 - 11:00 Panel 1: What are “meaningful” ML datasets and the opportunities and challenges in creating them?

  • Panelists: Judy Gichoya, Ari Robiscek, Elaine Nsoesie, Matt Lungren
  • Description: This panel will explore the challenges and opportunities of finding and building meaningful datasets within health systems through concrete examples, leveraging the panelists' experiences.

11:00 - 11:05: Transition break

11:05 - 12:00: Spotlight talks: new datasets and research finalists

  • Description: Talks will feature new datasets and selected research submissions. Features may include electrocardiogram waveform (ECG) datasets 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.

12:00 - 12:30: Lunch

Afternoon Program: How Academia, Government, Health Systems, and Funders Can Help 12:30 5:10pm

12:30 - 1:00 Panel 2: A conversation around medical mysteries, featuring Kevin Volpp and Eric Topol

  • Panelists: Kevin Volpp, Eric Topol
  • Description: Through a moderated conversation, panlists Eric Topol and Kevin Volpp will explore the biggest barriers to meaningful research on medical unknowns that we still face, and what can help.

1:00 - 1:15: Transition break

1:15 - 2:00 Panel 3: Data science for healthcare in academia and government

  • Panelists: Aneesh Chopra, Jennifer Chayes, Kate Baicker
  • Description: The 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.

2:00 - 2:15 Transition break

2:15 - 3:00 Panel 4: Data opportunities: unsolved medical problems and where new data can help

  • Panelists: Regina Barzilay, Marzyeh Ghassemi, Bin Yu, Emma Pierson
  • Description: This panel will begin by introducing three critical medical issues that drive mortality despite years of research: cancer, sudden cardiac death, and 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.

3:00 - 3:05: Transition break

3:05 - 3:45: Poster session

3:45 - 3:50: Transition break

3:50 - 4:20 Panel 5: What problems get funded in computational medicine?

  • Panelists: Daniel Yang, Katy Haynes
  • Description: This session will be a live conversation 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.

4:20 - 4:25: Transition break

4:25 - 4:55 Panel 6: Nightingale Open Science platform launch and video demonstration

  • Description: 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.

4:55 - 5:00: Transition break

5:00 - 5:10: Closing remarks