Dennis Wei

Dennis Wei is a Senior Research Scientist in the Trustworthy AI department, IBM Research, based in the San Francisco Bay Area. His research interests lie broadly in machine learning, signal processing, optimization, and statistics. Current interests center around trustworthy machine learning, particularly interpretability/explainability of LLMs and other machine learning models from various perspectives (input, data, model internals). He is a co-lead developer of the In-Context Explainability 360 (ICX360) open-source toolkit as well as its predecessor, the AI Explainability 360 (AIX360) open-source toolkit. Past research areas include ML fairness, causal inference, graphical models, health insurance, adaptive sampling, and sparse filter design.

He received S.B. degrees in electrical engineering and in physics in 2006, the M.Eng. degree in electrical engineering in 2007, and the Ph.D. degree in electrical engineering in 2011, all from the Massachusetts Institute of Technology. From 2011 to 2013 he was a Post-Doctoral Research Fellow in the EECS Department at the University of Michigan.

Dennis was a co-winner of the FICO Explainable Machine Learning Challenge in 2018. He received Notable Paper Awards at the 2023 and 2013 International Conference on Artificial Intelligence and Statistics (AISTATS), a Best Paper Honorable Mention at the 2015 SIAM International Conference on Data Mining (SDM), and co-authored a Best Student Paper at the 2013 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). He serves as an action editor for the Transactions on Machine Learning Research (TMLR) and as an area chair for ML conferences. He is a senior member of IEEE.

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