معرفی
Dr. Shusen Pu is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida (UWF), within the Hal Marcus College of Science and Engineering. He joined UWF in 2022 after serving as a Lecturer at Vanderbilt University. His academic journey includes a Ph.D. in Applied Mathematics from Case Western Reserve University (2020), advised by Dr. Peter J. Thomas, and postdoctoral research with Dr. Christos Constantinidis.
Dr. Pu's educational background includes:
- Ph.D. in Applied Mathematics, Case Western Reserve University
- B.S. in Mathematics and Statistics, Beijing Normal University
His research spans computational neuroscience, mathematical statistics, data analytics, and deep learning. He investigates neural network models, working memory, stochastic processes in biological systems, and generalized statistical distributions. He collaborates with biologists to analyze experimental data and integrates deep learning into neuroscience applications. His work aims to bridge microscopic neural activities with macroscopic behaviors.
His recent publications (2024–2018) reflect a strong focus on both neuroscience modeling and statistical distribution theory. Articles in journals like Nature Communications, Entropy, and Biological Cybernetics demonstrate expertise in prefrontal neuronal dynamics, neural noise modeling, and novel distribution generators. The consistency in topics indicates a dual research thrust: understanding brain function through computational models and advancing statistical tools for real-world data analysis.
Scientific contributions include:
- Published in high-impact journals such as Nature Communications, Neural Computation, Eneuro, and Biological Cybernetics
- Reviewer for multidisciplinary journals including iScience (Cell Press)
- Active participant and presenter at international conferences in Canada, China, and the USA
- Organizer of academic conference sessions and workshops
Dr. Pu is deeply involved in academic mentoring and teaching. He advises students in capstone projects integrating mathematical modeling, data science, and statistical analysis. He teaches courses such as Analytic Geometry and Calculus, Linear Algebra, Deep Learning, and Mathematical Statistics. He emphasizes problem-solving methodology, collaborative learning, and real-world applications. He leads the Computational Statistics and Data Analytics (CSDA) Lab, hosting weekly lab meetings and Python/data science tutorials, fostering a collaborative research environment.
His lab and team activities include:
- Weekly lab meetings (Fridays 9:00–10:00, Building 4, Room 212)
- Weekly Python and Data Science Tutorials (Fridays 10:00–11:00, same location)
- Supervision of capstone projects in data science and mathematical modeling
- Active research in neural connectivity inference and statistical distribution development
