About Inam Khan
Dr. Inam Khan is a Postdoctoral Research Fellow at the O’Donnell Data Science and Research Computing Institute. At ODSRCI, his research focuses on quantitative analysis and evaluation methods for data science applications, including the development of Pareto-analysis tools for evaluating tradeoffs across multiple performance metrics. His current work also includes an experimental comparison of synthetic and real data, emphasizing statistical fidelity and reproducibility.
Prior to joining the O’Donnell Institute, Dr. Khan was a Postdoctoral Researcher at SMU’s Lyle School of Engineering, where his research included electric-vehicle range prediction and graph neural networks for analyzing innovation ecosystems. He previously held positions as a Research Associate at Lancaster University in the United Kingdom and an Assistant Professor at COMSATS University Islamabad. His published research includes a predictive analytics framework for benchmarking innovation productivity in U.S. cities and a data-driven framework for mapping regional innovation productivity.
Dr. Khan earned his Ph.D. from Lancaster University, his M.S. from COMSATS University Islamabad, and his B.S. from Federal Urdu University Islamabad.