Colloquium: Incorporating Medical Insight into Machine Learning Algorithms for Learning, Inference, and Model Explanation

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Kayhan Batmanghelich (University of Pittsburgh), will be giving a talk titled "Incorporating Medical Insight into Machine Learning Algorithms for Learning, Inference, and Model Explanation".

Abstract

The healthcare industry is arriving at a new era where the medical communities increasingly employ computational medicine and machine learning. Despite significant progress in the modern machine learning literature, adopting the new approaches has been slow in the biomedical and clinical research communities due to the lack of explainability and limited data. Such challenges present new opportunities to develop novel methods that address AI's unique challenges in medicine.  
In this talk, we show examples of incorporating medical insight to improve the statistical power of association between various data modalities, design a novel self-supervised learning algorithm, and develop a context-specific model explainer. This general strategy can be employed to integrate other biomedical data, an exciting future research direction discussed briefly.

Biography

Kayhan Batmanghelich is an Assistant Professor of the Department of Biomedical Informatics and Intelligent Systems Program with secondary appointments in the Computer Science Department at the University of Pittsburgh and an adjunct faculty in the Machine Learning Department at the Carnegie Mellon University. He received his Ph.D. from the University of Pennsylvania (UPenn) under the supervision of Prof. Ben Taskar and Prof. Christos Davatzikos. He spent three years as a postdoc in Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. Polina Golland. His research is at the intersection of medical vision, machine learning, and bioinformatics. His group develops machine learning methods that address the interesting challenges of AI in medicine, such as explainability, learning with limited and weak data, and integrating medical image data with other biomedical data modalities. His research is supported by awards NIH and NSF, as well as industry-sponsored projects.