I am a Postdoctoral Fellow at the Vector Institute, in Toronto, Ontario where I primarily work with Anna Goldenberg, Marzyeh Ghassemi, and Frank Rudzicz. I received my PhD in Electrical and Computer Engineering Department at the University of Texas at Austin (UT, Austin). I was advised by Dr. Joydeep Ghosh, director of IDEAL at UT, Austin. I was also affliciated with WNCG. Prior to joining UT, I worked with Amazon Lab126 for two years, prototyping and developing interesting application and platform software using Machine Learning, Image Processing and Computer Vision for next generation consumer products. I completed my Masters from University of California, San Diego, majoring in Computer Engineering in 2011. My research is broadly focused on building reliable, fair, interpretable clinical machine learning models. For more details, my CV is available here.
03/17/20: Guest Lecture: (on Fairness, Explainability in ML) AI and Society Class at McMaster University
01/23/20: Guest Lecture: (on Causal Inference) Machine Learning for Health Graduate Class of UofT, CS
11/26/19: Guest Lecture: AI and Ethics Graduate Class of UofT, CS
11/22/19: Invited Talk: Healthcare Disparity and AI with the 99 AI Challenge cohort with the University of Toronto Libraries
11/08/19: Invited Talk: Explainability and Fairness in Health at the CSI Departmental Seminar, Emory University
10/11/19: Invited Talk: Data & Society Meeting on Evaluating Fairness for ML in Health
10/01/19: 3 Workshop papers accepted at the NeurIPS ML4H and Fair ML for Health workshops
08/26/19: I gave a talk at the launch of the Schwartz Reisman Institute for Technology and Society (SRIT&S) on fairness and explainability in health
07/23/19: Co-chairing the Fair ML for Health workshop at NeurIPS 2019.
shalmali at vectorinstitute dot ai
Conference and Journal
Peer reviewed workshops