About Stephen Keeley
I am an Assistant Professor at the Department of Natural Sciences at Fordham University. I do research at the interface of machine learning and neuroscience. I did my Ph.D. at NYU's Center for Neural Science in the labs of John Rinzel and Andre Fenton, and then worked as a postdoctoral researcher with Jonathan Pillow.
Keeley SL, Aoi M, Yu Y, Smith SL, BR & Pillow JW (2020). Identifying signal and noise structure in neural population activity with Gaussian process factor models. NeurIPS (accepted). [arxiv]
Keeley SL, Zoltowski DM, Yu Y, Yates JL, Smith SL, & Pillow JW. (2019). Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations. Proceedings of the 37th International Conference on Machine Learning (ICML). [link]
Wu Anqi, Roy NA, Keeley S, & Pillow JW (2017). Gaussian process based nonlinear latent structure discovery in multivariate spike train data Advances in Neural Information Processing Systems 30, 3496-3505 [abs | pdf | link | bib]
Park, E., Keeley, S. Savin, C. Ranck, Jr., J.B., Fenton, A.A. (2019) How the internally-organized direction sense is used to navigate. Neuron 101:1–9. 30522821 [PMID]
Keeley, S., Byrne, Á., Fenton, A., & Rinzel, J. (2019). Firing rate models for gamma oscillations. Journal of neurophysiology, 121(6), 2181-2190.
Keeley, S., Fenton, A.A., Rinzel, J. (2016). Modeling Fast and Slow Gamma Oscillations with Interneurons of Different Subtype. Journal of Neurophysiology. 27927782 [PMID].
I am currently teaching NSCI 2040 - Research Design and Analysis, at Fordham University at Lincoln center. This class teaches basic probability and statistics techniques undergraduates science majors.
Previously, at Princeton University, I instructed ORF 245 - Fundamentals of Statistics at Princeton University and co-instructed NEU314 - Mathematical Tools for Neuroscience with Carlos Brody . I was also an assistant instructor and guest lecturer for SML201 - Introduction to Data Science taught by Michael Guerzhoy.