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Bridging the Divide: Machine Learning in Medicine 2022

In person!
Weill Cornell Medicine, NYC
June 6-7


The third MLiM inter-campus symposium will be held in person on Weill Cornell Medicine's campus in NYC, with a virtual option. This symposium aims to bring together researchers and clinicians to present recent work and initiate collaborations. Participants from Ithaca will arrive by bus around lunchtime on the first day, which will include faculty presentations, a keynote lecture, an evening trainee poster session and reception followed by a formal dinner. The second day will include presentations and afternoon break-out sessions to initiate and encourage new collaborations. Ithaca participants will return via bus on the evening of the second day. Trainees may submit abstracts and present posters that have been peer-reviewed and approved. Cornell-affiliated individuals may attend at no cost - but registration, which is limited, is required. A tentative agenda is available here.

Confirmed speakers: Thomas Campion, Curtis Cole (w/ Jack Morris), Tanzeem Choudhury, Olivier Elemento, Daniel Gardner, Logan Groesnick, Iman Hajirasouliha, Amy Kuceyeski, Volodymyr Kuleshov, Cem Meyden, Yang Ning, Jyothishman Pathak, Yifan Peng, Emma Pierson, Jonathan Power, Anais Rameau, Mert Sabuncu, Fei Wang, Haiyuan Yu, Jinwei Zhang, Yiye Zhang

Poster abstract submission: Please email one-page abstracts (including figures and references) to by Monday, May 16th. Prizes will be awarded for the top poster presentations.

Keynote Speaker


Thomas Fuchs, Dr. Sc.

Co-Director of the Hasso Plattner Institute for Digital Health

Dean of Artificial Intelligence (AI) and Human Health

Professor of Computational Pathology and Computer Science

Icahn School of Medicine at Mount Sinai


Thomas J. Fuchs, Dr.Sc, is a scientist in the groundbreaking field of Computational Pathology, focused on the use of artificial intelligence to analyze images of tissue samples to identify disease, recommend treatment and predict outcome. In his role at Mt. Sinai, he will lead the next generation of scientists and clinicians to use artificial intelligence and machine learning to develop novel diagnostics and treatments for acute and chronic disease. Dr. Fuchs’s work includes developing novel methods for analysis of digital microscopy slides to better understand genetic mutations and their influence on changes in tissues. He has been recognized for developing large-scale systems for mapping the pathology, origins, and progress of cancer. This breakthrough was achieved by building a high-performance compute cluster to train deep neural networks at petabyte scale. Before joining Mount Sinai, Dr. Fuchs was Director of the Warren Alpert Center for Digital and Computational Pathology at Memorial Sloan Kettering Cancer Center (MSK) and Associate Professor at Weill Cornell Graduate School for Medical Sciences. At MSK he led a laboratory focused on computational pathology and medical machine learning. Dr. Fuchs co-founded Paige.AI in 2017 and led its initial growth to the leading AI company in pathology. He is a former research technologist at NASA’s Jet Propulsion Laboratory and visiting scientist at the California Institute of Technology. Dr. Fuchs holds a Doctor of Sciences from ETH Zurich in Machine Learning and a MS in Technical Mathematics from Graz Technical University in Austria.


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Greater Data Science Cooperative Institute

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