Presented By
O’Reilly + Intel AI
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, M.D. is an Instructor of Neurological Surgery in the Mount Sinai Health System and the Director of AISINAI, Mount Sinai’s artificial intelligence research group. He studied mathematics at Georgetown University with a focus on differential geometry. Prior to attending medical school, Dr. Oermann spent six months with the President’s Council on Bioethics studying human dignity under the mentorship of physician-philosopher Edmund Pellegrino. Dr. Oermann 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 fifty manuscripts spanning basic research on machine learning, tumor genetics, and the philosophy of medicine. As a PGY-2, Dr. Oermann was selected as one of Forbes Magazine’s 30 Under 30 for his work in applying machine learning to develop prognostic models for cancer patients. Dr. Oermann completed a postdoctoral fellowship at Google (Google Health / Verily Life Sciences). He is 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.


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 is a significant interest in applying deep learning based solutions to problems in medicine and healthcare. This talk will focus on identifying actionable medical problems, and then recasting them as tractable deep learning problems and the techniques to solve them. Read more.