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Center for Machine Learning and Health

2020 Fellows

collage of 2020 fellows

The 2020 CMLH fellows include (top, l-r) Dominik Bauer, Nate Breg and Jonathon Byrd (bottom, l-r) Aaron Rumack, Jennifer Williams and Tejas Zodage.

Meet the 2020 Fellows

Dominik Bauer is a Ph.D. student in the Robotics Institute, advised by Professor Nancy Pollard. His research focuses on creating affordable, customizable and highly dexterous soft robot manipulators that can be used as upper-limb prostheses. He is developing intelligent control strategies and working to automate the design process for customized prostheses. Prior to pursuing his Ph.D., he earned his bachelor's and master's degrees in mechanical engineering from Karlsruhe Institute of Technology, Germany.

Fellowship Research: A Soft and Dexterous Upper Limb Prosthesis: Optimizing Mechanical Design and Control

Nate Breg is a Ph.D. candidate in CMU's Heinz College. His research examines the determinants of the adoption of new medical technology — including surgical robots — and its impacts on medical labor markets and patients. More broadly, he applies approaches from labor economics and industrial organization to better understand health care provision. Before joining CMU, he earned a bachelor's degree in economics and history from Tufts University and assessed impacts of health policy at RTI International.

Fellowship Research: Causes of Adoption of Surgical Robots and Its Impact on Patients and Medical Labor Markets

Jonathon Byrd is a Ph.D. student in the Machine Learning Department, where he is advised by Assistant Professor Zachary Lipton. His research focuses on machine learning for complex decision-making tasks from missing data and off-policy data, and analyzing potential societal impacts and feedback mechanisms of statistical decision-making policies. Jonathon earned his bachelor's degree in mathematics and computer science from Morehead State University.

Fellowship Research: Learning Efficient and Equitable Liver Allocation Policies From Offline Data

Aaron Rumack is a Ph.D. student in the Machine Learning Department, advised by Professor Roni Rosenfeld. His research focuses on developing and applying machine learning and statistical methods to epidemiological forecasting and modeling. Before joining CMU, he received a bachelor's degree in computer science from Cornell University.

Fellowship Research: Modeling Spatiotemporal Dynamics of Influenza

Jennifer Williams is a Ph.D. student in the Computational Biology Department, advised by Assistant Professor Leila Wehbe. She earned a M.Sc. degree in integrated systems biology from the University of Luxembourg, a master's degree in natural science from the University at Buffalo, and a bachelor's degree in biology from Canisius College. Her research focuses on developing novel machine learning methods applied to neuroscience, with the goal of modeling individual differences in the brain to predict health issues.

Fellowship Research: Machine Learning Toward Personalized Treatment of Depression

Tejas Zodage is a master’s student in the Robotics Institute, working with Professor Howie Choset. Tejas obtained his bachelor's degree in mechanical engineering and a master’s in physics from the Birla Institute of Technology & Science, Goa Campus. His research focuses on developing deep-learning-based global point cloud registration algorithms. This research is applicable to various problems such as surgical robot localization, 3D reconstruction, virtual/mixed reality devices, etc.

Fellowship Research: Machine Learning Based Noisy Point Cloud Registration Algorithm for Mixed Reality Applications in Surgery