In General
Dr. Haifeng Wang is an Associate Professor and Co-Graduate Coordinator in the Department of Industrial and Systems Engineering at Mississippi State University. He received his Ph.D. and M.S. in Industrial and Systems Engineering from the State University of New York at Binghamton and his B.S. in Industrial Engineering from Southeast University, China. His research focuses on developing optimization-based frameworks to improve the interpretability, robustness, and transferability of machine learning models, with applications in medical image analysis, neurological disorder diagnosis, precision agriculture, and other large-scale heterogeneous data problems. Dr. Wang has authored over 45 refereed journal articles, received more than 2,000 citations, and secured over $3.2M in external research funding from competitive agencies including NIH, NSF, and USDA-NIFA. His work has appeared in leading journals such as EJOR, IISE Transactions, Computers and Electronics in Agriculture, Journal of Computational and Applied Mathematics, Neurocomputing, and Medical Physics, as well as in premier conference proceedings including IEEE ISBI, IEEE BIBM, and ACM BCB. He serves on the editorial board of Scientific Data (Nature), has been a guest editor for multiple journals, and holds leadership roles in IISE’s QCRE and DAIS divisions. He is a member of the IEEE, IISE, INFORMS, and ASEE, a certified Lean Six Sigma Black Belt, and a member of Alpha Pi Mu. He is the recipient of the 2025 New Faculty Research Award from the ASEE Southeastern Section and the 2025 Early Career Award from the Mississippi Academy of Sciences.


Health Informatics Focus
Dr. Haifeng Wang is an Associate Professor and Co-Graduate Coordinator in the Department of Industrial and Systems Engineering at Mississippi State University. He received his Ph.D. and M.S. in Industrial and Systems Engineering from the State University of New York at Binghamton and his B.S. in Industrial Engineering from Southeast University, China. His research focuses on developing optimization-based frameworks to improve the interpretability, robustness, and transferability of machine learning models, with applications in various medical decision-making processes. Dr. Wang has over 10 years of experience with computer-aided diagnosis. His lab has been working on ADHD diagnosis, head & neck cancer, lung cancer, retinal disorder, breast cancer, and thyroid cancer diagnosis using different imaging modalities, such as EEG, PSG, CT, PET, OCT, MRI, fMRI, and SRS. He has over 70 publications and his papers have appeared in journals such as Nature Scientific Data, Medical Physics, Neurocomputing, and Biomedical Signal Processing and Control. Dr. Wang's research has been funded by different state and federal agencies including NIH, NSF, and USDA NIFA.