Alexander Schubert is a Ph.D. Student in Computational Precision Health at UC Berkeley and UCSF. His research interests focus on the application of machine learning as a tool to improve decision-making in health and to ensure that healthcare innovations are accessible and equally impactful to everyone. A particular focus of his work lies on applications of artificial intelligence to cardiovascular disease, where he aims to leverage unstructured data modalities such as ECG waveforms to improve the assessment and understanding of cardiovascular risk in order to better target medical...
Zhongyuan Liang is currently a Ph.D. student in computational precision health at the University of California, Berkeley. His research interests lie in in-context learning, distribution shift, interpretability, fairness.
Nikita Mehandru is currently a Ph.D. student at the UC Berkeley School of Information, and a member of Berkeley AI Research (BAIR). She is interested in building reliable machine learning systems to support clinician needs and predict the onset of chronic diseases. Her research leverages electronic health records and multimodal medical data using techniques in clinical natural language processing, human-centered AI, causal inference, and machine learning. She received her master’s degree from the University of Pennsylvania, and bachelor’s degree from Claremont McKenna College. Her...
