Christopher Kulp

Christopher Kulp is the John P. Graham Teaching Professor of Physics in the Department of Astronomy and Physics at Lycoming College. He holds a B.A. from McDaniel College, and M.S. and Ph.D. from The College of William & Mary. Since joining Lycoming College in 2008, he has taught physics at all levels, along with courses in machine learning, data analysis, and mathematical modeling.

Education:

• B.A., McDaniel College
• M.S., The College of William & Mary
• Ph.D., The College of William & Mary

His research centers on nonlinear and complex systems, with a focus on nonlinear time series analysis, machine learning, and mathematical modeling. He applies these techniques to data from diverse fields including astrophysics, climatology, medicine, economics, biology, and combustion. His group emphasizes undergraduate research, and he has published over 30 peer-reviewed articles, book chapters, and conference papers.

The recent publications highlight a strong trend toward applying machine learning and agent-based modeling to socioeconomic systems, particularly in understanding wealth distribution and taxation impacts. Other works explore symbolic dynamics in chaotic systems, reflecting a deep engagement with nonlinear time series analysis and complex behavior characterization.

Scientific Awards:

• Mike Resnick Memorial Award for Best Science Fiction Short Story by a New Author

Kulp has secured research support enabling sustained undergraduate involvement in computational and theoretical projects. His advising emphasizes hands-on experience in data science and modeling. He is also a co-author of influential textbooks, including Classical Mechanics: A Computational Approach (2020) and Mathematical Methods using Python (2024), which integrate programming into physics education. His interdisciplinary approach bridges scientific rigor with creative expression.

He leads an active research group focused on complex systems and maintains a strong commitment to pedagogy and innovation in STEM education. His work often involves network representations of time series and intelligent agent design, fostering collaboration across disciplines.