I am a PhD student in the Department of Computer Science at Purdue University, where I have also completed my MS, and I am fortunate to be advised by Drs. Ming Yin and Rajiv Khanna. Along the way, I have had the opportunity to broaden my experience through internships at Microsoft. Before joining Purdue, I earned my undergraduate degree from LUMS in Pakistan, and had the privilege of collaborating with researchers from TUKL-NUST R&D Lab, University of Virginia and University of Notre Dame.

I am broadly interested in human-centered AI/ML, drawing insights from not only machine learning and optimization theory but also human-computer interaction and cognitive psychology, with an interdisciplinary agenda of enabling AI systems to become effective, trusted partners in complex human workflows. My current research focus is on advancing human-AI collaboration and eXplainable AI (XAI). Specifically, I investigate how data asymmetries (differences in informativeness and value across datasets) and cognitive asymmetries (heterogeneity in human expertise, confidence, and biases) can be systematically modeled and leveraged—instead of being treated as obstacles—to design adaptive and data-efficient AI systems that complement human strengths, foster trust and enhance decision-making.