Put AI to Work
April 15-18, 2019
New York, NY
Eric Oermann

Eric Oermann
Instructor of Neurological Surgery, Mount Sinai Health System

Eric Karl Oermann is an instructor of neurological surgery in the Mount Sinai Health System and the director of AISINAI, Mount Sinai’s artificial intelligence research group. Prior to attending medical school, Eric spent six months with the President’s Council on Bioethics studying human dignity under the mentorship of physician-philosopher Edmund Pellegrino. He has won numerous awards for his scholarship, including fellowships from the American Brain Tumor Association and Doris Duke Charitable Research Foundation, where he was first exposed to neural networks and deep learning. He has published over 50 manuscripts spanning basic research on machine learning, tumor genetics, and the philosophy of medicine. As a PGY-2, he was selected as one of Forbes’s “30 under 30” for his work in applying machine learning to develop prognostic models for cancer patients. He’s interested in weakly supervised learning, reinforcement learning with imperfect information and in building artificial neural networks that more accurately model biological neural networks. As an actively practicing neurosurgeon, he is also interested in the application of deep learning to solve a wide range of problems in the medical sciences and improving clinical care. He holds an MD and studied mathematics at Georgetown University with a focus on differential geometry; he completed a postdoctoral fellowship at Google (Google Health/Verily Life Sciences).

Sessions

1:50pm2:30pm Thursday, April 18, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Secondary topics:  AI case studies, Computer Vision, Health and Medicine
Eric Oermann (Mount Sinai Health System), Katie Link (Allen Institute for Brain Science)
There's significant interest in applying deep learning-based solutions to problems in medicine and healthcare. Eric Oermann and Katie Link identify actionable medical problems, recast them as tractable deep learning problems, and discuss techniques to solve them. Read more.