Sam Behseta, Ph.D.

Professor of Mathematics

Office: MH-157/157B, Lab: MH-531

Phone: 657-278-8560

Sam Behseta is a Professor of Mathematics and Statistics at Cal State Fullerton. His main area of research is statistics and machine learning applications in neuroscience. Behseta has also published many articles in a diverse field of applications, including epidemiology, evolutionary biology, biometeorology, and statistics education. His work has appeared in some of the most prestigious journals in Statistics and Machine Learning. Behseta is a Fellow of the American Statistical Association and Cal State Fullerton’s 2022 Outstanding Professor. He also serves the community through directing the Center for Computational and Applied Mathematics (CCAM).

Sam Behseta photo

Research Interests

Behseta’s main theme of research is modeling neurophysiological data, primarily spike train data. He has developed a host of Bayesian and machine learning approaches for the purpose of prediction and inference associated with neuronal spike trains. Recently, Behseta’s team at Cal State Fullerton has focused on developing predictive models for Alzheimer’s disease through machine learning techniques.

Fun Facts

Behseta is a cinephile: in his free time, he digs, watches, and contemplates about the mostly black and white films made during 1920-1960. To that extent, he has written a few articles about the avant-garde directors of French cinema. Sam is also an avid vinyl and LP-collector, mainly jazz, specifically albums issued by the legendary label Blue Note.

Publications

Behseta S., Ichinose C.L., and Drew D.L. (2022). A call for more graduate programs in statistics education, Amstat News, 539, p. 16.

David E. Drew, Sam Behseta and Cherie L. Ichinose (2022). An Urgent Plea for More Graduate Programs in Statistics Education, Journal of Humanistic Mathematics, Volume 12 Issue 1, 422-427.

McEligot A.J., Cuajungco M.P., Behseta S., Chandler L., Chauhan H., Mitra S., Rusmevichientong P., and Charles S. (2018). Big Data Science Training Program at a Minority Serving Institution: Processes and Initial Outcomes, California Journal of Health Promotion, Vol 16, 1, 1-5.

Zhou B., Moorman D., Behseta, S., Ombao H., and Shahbaba B. (2016). A Dynamic Bayesian Model for Characterizing Cross Neuronal Interaction During Decision Making, Journal of the American Statistical Association, 111, 514, 459-471.

Shahbaba B., Zhou B., Ombao H., Moorman D.E., Behseta S. (2014). A Semiparametric Bayesian Model for Neural Coding, Neural Computation, 26, No. 9, 2025-2051.

Behseta, S., Berdyyeva, T., Olson, C., and Kass, R.E. (2009). Bayesian Correction for Attenuation of Correlation in Multi-Trial Spike Count Data, Journal of Neurophysiology, 101:2186-2193.