About
Muhammad Aurangzeb Ahmad is the Principal Research Scientist at KenSci Inc. and Affiliate Assistant Professor in the Department of Computer Science at University of Washington Tacoma. His previous academic appointments were at the Department of Computer Science at University of Minnesota, Center for Cognitive Science at University of Minnesota, Faculty of Engineering at Istinye University and the Indian Institute of Technology – Kanpur. He also has wide experience in applied machine learning across several industries.
Research fairness, accountability, transparency, ethics (fate) in AI, explainable AI, interpretable machine learning, ai in healthcare
News
Date | Venue | Details |
---|---|---|
2020/10 | AAAI | I have two papers accepted at the AAAI Fall Symposium |
2020/10 | fairMLHealth | We just released a new python library fairMLHealth for measuring fairness in machine learning models in healthcare |
2020/05 | KDD | I have two tutorials accepted at ACM KDD 2020 Conference. i) Fairness in Machine Learning for Healthcare ii) Physics Inspired Models in Artificial Intelligence |
2020/05 | KDD | I am part of the senior committee member of KDD 2020’s AI for COVID workshop |
2020/05 | PAKDD | I am giving a tutorial on Deep Explanations in Machine Learning via Interpretable Visual Methods with Boris Kovalerchuk and Ankur Teredesai at PAKDD on May 11, 2020 |
Additional news can be accessed at the News page
Latest Publications
- Padthe, Karthik K., Vikas Kumar, Carly M. Eckert, Nicholas M. Mark, Anam Zahid, Muhammad Aurangzeb Ahmad, and Ankur Teredesai. “Emergency Department Optimization and Load Prediction in Hospitals.” AAAI Fall Symposium 2020 on AI for Social Good, November 13, 2020.Details
- Ming Yuan, Muhammad Aurangzeb Ahmad, Vikas Kumar, and Ankur Teredesai. “Fairness in Classification Parity of Machine Learning Models in Healthcare.” AAAI Fall Symposium 2020 on AI for Social Good, November 13, 2020.Details
- Ahmad, Muhammad Aurangzeb, Ankur Teredesai, and Carly Eckert. “Fairness, Accountability, Transparency in AI at Scale: Lessons from National Programs.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 690–90, 2020.Details
- Kovalerchuk, Boris, Muhammad Aurangzeb Ahmad, and Ankur Teredesai. “Survey of Explainable Machine Learning with Visual and Granular Methods beyond Quasi-Explanations.” ArXiv Preprint ArXiv:2009.10221, 2020.Details
- Ahmad, Muhammad Aurangzeb, and Şener Özönder. “Physics Inspired Models in Artificial Intelligence.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3535–36, 2020.Details