Tianshu Feng’s work is data-driven and centers on the systematic approach to processing, visualizing, analyzing, modeling, and examining data with complex features. This involves developing and applying novel, flexible, and reliable models via interdisciplinary collaborations in various areas, such as transportation, bioinformatics, healthcare, and finance. His research interests include machine learning and statistical modeling, explainable AI, model fairness and robustness, data exploration, and active learning.
Prior to joining Mason, Tianshu was a Quantitative Analytics Specialist at Wells Fargo. He received his Ph.D. degree in Industrial Engineering from the University of Washington and his Bachelor’s degree in Statistics from the University of Science and Technology of China.
