Shibo Li’s primary research area is AI for science, which integrates traditional computational physics with modern machine learning to analyze physical systems. This complementary approach uses physical insights to improve data-driven methods' efficacy and offers a flexible, efficient computing infrastructure for augmenting traditional research with statistical insights derived from abundant data.

Rittwika Kansabanik, a statistics researcher at FSU, develops scalable machine learning methods for high-dimensional data. Her work focuses on feature selection for latent factor models, enabling class-incremental feature selection and guaranteed feature recovery. This research provides more efficient and trustworthy AI systems for large-scale data.

Dr. Adrian Barbu is a professor in the Department of Statistics at FSU, holding PhDs in Mathematics and Computer Science. His expertise spans computer vision, machine learning, and medical imaging, resulting in over 90 papers and 25 patents. He is a recipient of the Thomas A. Edison Patent Award and the FSU Graduate Faculty Mentor Award.

Dr. Russell Almond is a statistician specializing in applying Bayesian networks and Partially Observed Markov Decision Processes to educational measurement. His work focuses on scoring game and simulation-based assessments. The author of two books on the topic, he also developed the “Peanut” system for building and scoring assessments with Bayesian networks.