Dr. Hailey Kuang is an assistant professor of measurement and statistics whose work advances computational psychometrics by integrating machine learning, deep learning, and LLMs into psychometric methods. She has published widely and teaches courses on machine learning for social sciences, as well as descriptive and inferential statistics.

Zina Ward is an assistant professor of philosophy with research in general philosophy of science, philosophy of cognitive science, and philosophy of machine learning. She is the 2022 winner of the Popper Prize.

Dr. Uejio's research develops novel societal determinants of health datasets. His team is constructing the nation's first property-level residential housing quality dataset using property records and explainable machine learning (XGBoost/SHAP) for nationwide estimates. They also map local Adverse Childhood Experiences (ACEs) using electronic health records.

Dr. Abdulrahman Takiddin is an assistant professor of electrical and computer engineering at the FAMU-FSU College of Engineering. His research interests focus on machine learning and its applications to cyber-physical systems, including the smart grid and smart transportation. He is a prolific author with over 40 publications and more than 880 citations.

Dr. Shanbhag works at the interface of soft matter and numerical algorithms. Over the past couple of years, he has started to apply machine learning methods to suitable problems in the area.

Dr. Aidan Milliff is an assistant professor of political science at FSU. His research combines computational social science, machine learning, and causal inference with qualitative methods to study how people interpret and respond to political violence. His work focuses on South Asia, forced migration, and political psychology.

Dr. Miao's research focuses on developing methods to learn interpretable patterns and mechanisms for population and individual health improvement. His expertise spans clinical trial design, machine learning of neuroimaging and multimodal data, and big healthcare data analysis. Applications include cancer, neural disorders, infectious diseases, and digital health.

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.

Dr. Sanghyun Lee is an associate professor of mathematics at FSU. His research develops high-fidelity numerical methods and scientific machine learning for multiphysics and multiscale PDEs, focusing on subsurface applications like poroelasticity and fracture. He has published widely on finite element methods and is a co-PI on a DOE project for subsurface energy systems.

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.