Statistics & Data Science
Director, HETU Lab
Jake develops scalable statistical methods for high-dimensional and dependent data. His research spans causal inference, structural changes, and interdisciplinary applications in public health, finance, education, and AI assessment.
Major Collaborator, HETU LabAssistant Professor, University of South Carolina
Ting Fung is an Assistant Professor in the Department of Statistics at the University of South Carolina. He develops scalable statistical methods for complex data, including spatial and spatio temporal data, and collaborates on multidisciplinary projects that involve large and dependent datasets.
Collaborator (Data Scientist), HETU Lab
Erica Yang is an independent researcher and data scientist who earned her Ph.D. in Statistics from the University of South Carolina. Her research interests include statistical machine learning, Bayesian hierarchical modeling, and rearrangement-based clustering of multi-view ordinal data, with an emphasis on theoretical recovery guarantees and robust statistical inference. She also has industry experience developing predictive machine learning models and evaluating their reliability in real-world applications.
Graduate Researcher
PhD Student, University of South Carolina
Fanghua is a PhD student in Statistics at University of South Carolina. His research interests are approximate and scalable inference on spatiotemporal statistics and high-dimensional data analysis. He has strong skills in statistical theory development, especially in convex optimization analysis of machine learning and data science applications.
Graduate Researcher
PhD Student, University of South Carolina
Lu Zhou is a third-year Ph.D. student in Statistics at the University of South Carolina. She earned her M.S. in Statistics from the University of Minnesota Twin Cities. Her research focuses on statistical inference, computational statistics, and machine learning, with particular interests in neural networks, multitask learning, and change point detection for complex temporal and longitudinal data. Her interdisciplinary work includes applying statistical and computational methods to finance, public health, environmental science, and human-centered data.
Graduate Researcher
PhD Student, Texas State University
Adebayo Owoade is a PhD student in Mathematics with a concentration in Statistics at Texas State University. His research interests include Bayesian statistics, causal inference, nonparametric methods, statistical and machine learning, statistical genetics, genomics, and spatiotemporal modeling, with applications across interdisciplinary areas. As a Researcher, he works on developing statistical and computational methodologies for estimating intervention effects, identifying structural changes, and analyzing complex longitudinal data.
Graduate Researcher
Master Student, Texas State University
Mason is a Master student in Mathematics with a Statistics Concentration at Texas State University. His research interests are Data Science and Machine Learning as well as some methodology. He is engaged in research involving the public sentiment of AI use compared to various demographics.
Undergraduate Researcher
Undergraduate Student, University of South Carolina
Wendan is an undergraduate student in Psychology at University of South Carolina. She serves as a researcher since 2023, holding a patent in quantitative education.
Undergraduate Researcher
Undergraduate Student, Texas State University
He is currently pursuing a Bachelor of Science in Mathematics at Texas State, with plans to eventually pursue a Master's Degree. His research interest is utilizing mathematical and statistical methods to track how the Italian and Spanish languages evolved during times of political instability.
Undergraduate Researcher
Undergraduate Student, Texas State University
Junhao is an undergraduate student majoring in Computer Science with a minor in Mathematics at Texas State. He is interested in statistical learning, online learning, sequential decision-making, and reinforcement learning. He contributes to computational research on linguistic change using corpus analysis, matrix representation and word co-occurrence networks.