My current research is focused on the following inter-related core areas:
The proliferation and AI and machine learning in almost all facets of daily life has brought to the fore, the problems of algorithmic decision making, unintended consequences of automation of decision making and nudging, bias and discrimination among other issues. My current research is focused on Responsible AI, especially as it pertains to ethics, accountability, fairness and transparency in AI and machine learning.
- 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
- Ahmad, Muhammad Aurangzeb, Arpit Patel, Carly Eckert, Vikas Kumar, and Ankur Teredesai. “Fairness in Machine Learning for Healthcare.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3529–30, 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, Carly Eckert, and Ankur Teredesai. “The Challenge of Imputation in Explainable Artificial Intelligence Models.” ArXiv Preprint ArXiv:1907.12669, 2019.Details
- Ahmad, Muhammad Aurangzeb, Carly Eckert, and Ankur M Teredesai. “Interpretable Machine Learning in Healthcare.” In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 559–60, 2018.Details
I am greatly interested in the real world use of AI and machine learning models to improve quality of life and clinical outcomes.
- Eckert, Carly, Neris Nieves-Robbins, Elena Spieker, Tom Louwers, David Hazel, James Marquardt, Keith Solveson, et al. “Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital.” Applied Clinical Informatics 10, no. 2 (2019): 316.Details
- Eckert, C, M Ahmad, K Zolfaghar, G McKelvey, C Carlin, and D Lowe. “S45 Predicting Likelihood of Emergency Department Admission Prior to Triage: Utilising Machine Learning within a COPD Cohort.” BMJ Publishing Group Ltd, 2018.Details
I am greatly interested in the foundations of artificial intelligence and machine learning from a cross-discilinary perspective i.e., how can physics, cognitive science and neuroscience can help us understand the foundations of Artificial Intelligence.
Physics and AI
- 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
For a summary of research projects from the past check out the following: Previous research