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“We will study natural and artificial intelligence in order to improve Medicine, understand human intelligence, and to build better artificial intelligence.”

-- EKO

While we believe in building end-to-end solutions with minimal hardcoding of features, we also realize that this often not optimal, particularly in a live clinical environment where the signals are noisy,  the data is dirty, and there are almost innumerable potential inputs. People are at the heart of what we do, and we believe that to bring AI to the clinic it begins with educating clinicians to better understand AI. In the ideal case, we hope to not only make physicians fluent in machine learning, but to help train a new generation of physicians who can program their own machine learning and deep learning solutions to solve the problems they encounter in everyday medical practice. 

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Eric Karl Oermann, MD

Instructor,

Neurological Surgery

Director & Chief Scientist, AISINAI

EKO has a background in differential geometry and deep learning. His education in artificial neural networks began at Georgetown University and turned towards medical problems while a Doris Duke fellow at the University of North Carolina at Chapel Hill. He was listed in Forbes Magazine's 2016 "30 Under 30" for his work on applying machine learning to predict patient survival and disease progression. He recently was a postdoc at Verily (Google Life Sciences). Currently he is the lead AI researcher for the AI Consortium as well as a practicing clinician in the Department of Neurological Surgery.

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Samuel Cho, MD

Associate Professor,

Department of Orthopedics

Department of Neurosurgery

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.

 
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Anthony B. Costa, PhD

Assistant Professor, Neurological Surgery

Director,

Sinai Biodesign

Dr. Costa 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.

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Joseph Titano, M.D.

House Officer,

Department of Radiology

Dr. Titano has a B.S. in Mathematics from the University of Pennsylvania. He subsequently worked for Teach for America prior to studying medicine at Drexel University. He has a clinical background in Radiology and machine learning, and is specifically interested in the development of machine learning and deep learning algorithms for the analysis of medical imaging data.

 
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Brett Marinelli, MD

Houseofficer,

Department of Radiology