Workshop
Machine Learning for Health (ML4H): What makes machine learning in medicine different?
Andrew Beam · Tristan Naumann · Brett Beaulieu-Jones · Madalina Fiterau · Irene Y Chen · Samuel Finlayson · Emily Alsentzer · Adrian Dalca · Matthew McDermott

Fri Dec 13th 08:00 AM -- 06:40 PM @ West Ballroom A
Event URL: https://ml4health.github.io/ »

The goal of the NeurIPS 2019 Machine Learning for Health Workshop (ML4H) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. Attendees at this workshop can also expect to broaden their network of collaborators to include clinicians and machine learning researchers who are focused on solving some of the most import problems in medicine and healthcare. The organizers of this proposal have successfully run NeurIPS workshops in the past and are well-equipped to run this year’s workshop should this proposal be accepted.

This year’s theme of “What makes machine learning in medicine different?” aims to elucidate the obstacles that make the development of machine learning models for healthcare uniquely challenging. To speak to this theme, we have received commitments to speak from some of the leading researchers and physicians in this area. Below is a list of confirmed speakers who have agreed to participate.

Luke Oakden-Raynor, MBBS (Adelaide)
Russ Altman, MD/PhD (Stanford)
Lilly Peng, MD/PhD (Google)
Daphne Koller, PhD (in sitro)
Jeff Dean, PhD (Google)

Attendees at the workshop will gain an appreciation for problems that are unique to the application of machine learning for healthcare and a better understanding of how machine learning techniques may be leveraged to solve important clinical problems. This year’s workshop builds on the last two NeurIPS ML4H workshops, which were both attended by more than 500 people each year, and helped form the foundations of an emerging research community.

Please see the attached document for the full program.

08:45 AM Daphne Koller Talk (Keynote)
09:15 AM Emily Fox Talk (Presentation) Emily Fox
10:15 AM Luke Oakden-Rayner Talk (Presentation) Luke Oakden-Rayner
10:45 AM Paper spotlight talks (Spotlight talks)
11:15 AM Poster Session I (Poster session)
Shuangjia Zheng, Arnav Kapur, Umar Asif, Eyal Rozenberg, Cyprien Gilet, Oleksii Sidorov, Yogesh Kumar, Tom Van Steenkiste, Willie Boag, David Ouyang, Paul Jaeger, Sheng Liu, Aparna Balagopalan, Deepta Rajan, Marta Skreta, Nikhil Pattisapu, Jann Goschenhofer, Viraj Prabhu, Di Jin, Laura-Jayne Gardiner, Irene Li, sriram kumar, Isabelle Hu, Mehul Motani, Justin Lovelace, Usman Roshan, Lucy Lu Wang, Ilya Valmianski, Hyeonwoo Lee, Sunil Mallya, Elias Chaibub Neto, Jonas Kemp, Marie Charpignon, Amber Nigam, Wei-Hung Weng, Sabri Boughorbel, Alexis Bellot, Lovedeep Gondara, Haoran Zhang, Taha Bahadori, John Zech, Rulin Shao, Edward Choi, Laleh Seyyed-Kalantari, Emily Aiken, Ioana Bica, Yiqiu Shen, Kieran Chin-Cheong, Subhrajit Roy, Ioana Baldini, Tiffany Min, Dirk Deschrijver, Pekka Marttinen, Damian Pascual Ortiz, Supriya Nagesh, Niklas Rindtorff, Andriy Mulyar, Kathi Hoebel, Martha Shaka, Pierre Machart, Leon Gatys, Nathan Ng, Matthias Hüser, Devin Taylor, Dennis Barbour, Natalia L Martinez, Clara McCreery, Ben Eyre, Vivek Natarajan, Ren Yi, Ruibin Ma, Chirag Nagpal, Nan Du, Andy Gao, Anup Tuladhar, Sam Shleifer, Jason Ren, Pouria Mashouri, Max Lu, Farideh Bagherzadeh-Khiabani, Olivia Choudhury, Maithra Raghu, Scotty Fleming Fleming, Mika Jain, GUO YANG, Alena Harley, Stephen Pfohl, Elisabeth Rumetshofer, Alex Fedorov, Saloni Dash, Jacob Pfau, Sabina Tomkins, Colin Targonski, Mike Brudno, Xinyu Li, Yiyang Yu
02:45 PM Lily Peng talk (Presentation) Lily Peng
03:15 PM Anna Goldenberg Talk (Presentation) Anna Goldenberg
03:45 PM Poster Session II (Poster session)
04:45 PM Deepmind Talk (Presentation) Nenad Tomasev
05:15 PM Panel Discussion (Panel)
06:15 PM Message from sponsor (Presentation) Max Baranov, Yuge Ji

Author Information

Andrew Beam (Harvard)
Tristan Naumann (Microsoft Research)
Brett Beaulieu-Jones (Harvard Medical School)
Madalina Fiterau (CMU)
Irene Y Chen (MIT)

Irene is a PhD student at MIT focusing on applications on health care and fairness. She did her undergrad at Harvard where I studied applied math and computational engineering. Before starting at MIT, she worked for two years at Dropbox as a data scientist and machine learning engineer.

Sam Finlayson (Harvard Medical School)

Samuel Finlayson is a MD-PhD Candidate studying jointly at Harvard Medical School and Massachusetts Institute of Technology. His research focuses on developing machine learning methods for precision medicine. Current applications focus on neurological diseases and extend techniques from computer vision, natural language processing, and single-cell genomics. Previously, he studied Biomedical Informatics at Stanford University.

Emily Alsentzer (MIT)
Adrian Dalca (MIT, HMS)
Matthew McDermott (MIT)

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