Eric Karl Oermann, M.D. (@ekoermann) is the Director of AISINAI and an Instructor of Neurological Surgery in the Mount Sinai Health System. 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 seventy manuscripts spanning basic research on machine learning, deep learning, and the philosophy of medicine. Dr. Oermann was selected as one of Forbes Magazine’s 30 Under 30 for his work on using machine learning to develop prognostic models for cancer patients. Dr. Oermann completed a postdoctoral fellowship at Verily (Google 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 a 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.
Dr. Costa (@anthonycosta) is an accomplished computational scientist with a highly interdisciplinary background. Dr. Costa is the Director of the Neurosurgery Simulation Core for the Department of Neurosurgery at the Icahn School of Medicine where he drives the development of novel, high-fidelity virtual-reality neurosurgery simulation and modeling technologies. Dr. Costa's doctoral and postdoctoral work included study in diverse fields such as fluid dynamics, multivariate statistics and machine learning methods for biomedical image analysis, and statistical and quantum mechanics. He has a strong foundation in the design and implementation of parallel scientific methods on high performance computing systems, and he builds and maintains the unique HIPAA compliant compute resource that make the AI Consortium's research possible.
Dr. Samuel Cho is a graduate of the University of Virginia where he studied Economics and was inducted into Phi Beta Kappa and was a Rhodes Scholar regional finalist. He earned his medical degree from Washington University School of Medicine and completed an orthopaedic surgery residency at the New York Orthopaedic Hospital/Columbia University Medical Center where he was honored with multiple research grants and awards including the prestigious Frank E. Stinchfield Award. Dr. Cho remains active in both basic science and clinical research and has presented at national and international meetings on topics ranging from bone biology to complex spinal reconstructions for severe spinal deformity. Dr. Cho is a nationally recognized leader in spinal surgery and the use of large clinical datasets to predict patient and surgical outcomes. Dr. Cho leads the consortium's efforts to deploy machine learning on large clinical datasets and registries.