Explainable AI for Health: Recent Episodes
The University of Texas at Austin AI Heath Lab
Join us as we delve into the world of user-centered explainable AI through conversations with leading researchers, designers, and entrepreneurs. We explore how explainability techniques bridge the “last-mile” between model predictions and actionable insights for patients, caregivers, and healthcare providers to use AI in a trustworthy, fair, and workflow-compatible manner.
The University of Texas at Austin's AI Health Lab is led by Prof. Ying Ding, PhD from the School of information and Prof. Justin Rousseau, MD, MMSc, from the UT Austin Dell Medical School. We focus on cutting-edge research on AI in health and data-driven science of science. On this channel, we interview the foremost experts on Explainable AI in Health.
Hosted by PhD candidate Madalyn Rosenthal and honors neuroscience undergraduate Ian Alrahwan. Produced by Ian Alrahwan.
Dr. Jun Deng, Yale University School of Medicine's Professor of Therapeutic Radiology and Director of Physics Research, talks to us about his new initiative, the Digital Twins for Health Consortium (DT4H for short). Check out DT4H at http://dt4h.org
Dr. Durrett is an assistant professor of Computer Science at UT Austin. His current research focuses on making natural language processing systems more interpretable, controllable, and generalizable, spanning application domains including question answering, textual reasoning, summarization, and information extraction. His work is funded by a 2022 NSF CAREER award and other grants from agencies including the NSF, DARPA, Salesforce, and Amazon.
As of our recording, Dr. Glicksberg is an assistant professor at the Icahn School of Medicine at Mount Sinai in the departments of AI in Human Health; Medicine; and Genetics and Genomic Sciences, and a member of the Hasso Plattner Institute for Digital Health. His research uses machine learning to couple multi-modal patient health data to forward personalized medicine.
In this episode we interview Dr. Prithwish Chakraborty! Dr. Chakraborty is currently a Research Staff Member and global sub-theme lead at IBM Research in the Center of Computational Health at the IBM T.J. Watson Research Center, NY. His work focuses on applications of data science towards patient health characterization and risk modeling. Broadly, his research interests are temporal data mining, machine learning and causal inference. Please checkout his work at: https://prithwi.github.io/