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, M.D.. 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 eighty manuscripts spanning basic research on machine learning, deep learning, and the philosophy of medicine.
As a resident Dr. Oermann was selected as one of Forbes Magazine’s "30 Under 30" for his work in using 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 (“machines that think like humans”). As a physician, he is also interested in the application of deep learning to solve a wide range of practical problems in the medical sciences for improving clinical care.
The Stacy L. Rollins Jr. MD Award
Georgetown University Department of Neurosurgery
For outstanding academic performance
The Excellence in Education and Research Award
Georgetown University Department of Radiation Medicine
For outstanding academic performance
Doris Duke Clinical Research Fellowship
American Brain Tumor Association Fellowship
American Brain Tumor Association
Pi-Epsilon Mu- National Mathematics Honor Society
Artificial intelligence and human intelligence
I'm interested in the basic computational mechanisms behind machine learning, and particularly how insights from human learning and neuroscience might advance the field of deep learning. I'm also interested in the converse, and hope that as we better understand deep learning we'll also understand human learning and human intelligence. Our group utilizes deep learning, mathematics, and neuroscience to tackle two questions: (1) What are the basic mechanisms responsible for human intelligence? (2) How can we build better artificial intelligence?
Philosophy of medicine
I'm interested in the core philosophical foundations of medical practice. Particularly ontological ethical theories.
Machine learning and Precision Medicine
I'm interested in making Medicine better utilizing machine learning models and massive medical datasets. "Precision Medicine" is Medicine, and the proper application of modern machine learning can move us away from a paradigm of treating individuals based upon population level insights, to treating individuals based upon individualized evidence and insights.
Physicians and scientists on the faculty of the Icahn School of Medicine at Mount Sinai often interact with pharmaceutical, device and biotechnology companies to improve patient care, develop new therapies and achieve scientific breakthroughs. In order to promote an ethical and transparent environment for conducting research, providing clinical care and teaching, Mount Sinai requires that salaried faculty inform the School of their relationships with such companies.
Below are financial relationships with industry reported by Dr. Oermann during 2019 and/or 2020. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.
- Google Inc.
Equity (Stock or stock options valued at greater than 5% ownership of a publicly traded company or equity of any value in a privately held company)
- Whiteboard Coordinator LLC
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