Scott Linderman

Scott Linderman
Scott
Linderman
Graduate Student
Harvard University

Associated Research Thrust: 

Bio: Scott is a Computer Science Ph.D. candidate at Harvard University advised by Prof. Leslie Valiant and Prof. Ryan Adams. He works at the intersection of computational neuroscience and machine learning, building analytical and theoretical tools to decipher large-scale neural recordings and understand biological computation. He completed his B.S. in Electrical and Computer Engineering at Cornell University in 2008 and then helped build and test the Windows networking stack for three years as an engineer at Microsoft. As a graduate student, he received the National Defense Science and Engineering Graduate Fellowship, contributed to numerous computational neuroscience summer programs, was a teaching fellow for three graduate computer science courses, and co-organized a workshop at the Computational and Systems Neuroscience Conference. He collaborates with neuroscientists through affiliations with the interdisciplinary NSF Center for Brains, Minds, and Machines and the Harvard Center for Brain Science.

 

Honors:

National Defense Science and Engineering Graduate Fellowship

Siebel Scholar 2016

Website: 

Projects

Probabilistic Learning and Inference in a Neuroidal Model of Computation

CBMM Publications

S. W. Linderman, Johnson, M. J., Wilson, M. A., and Chen, Z., “A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation”, Journal of Neuroscience Methods, vol. 263, no. 1, 2016.
Z. Chen, Linderman, S. W., and Wilson, M. A., “Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes”, in IEEE Workshop on Machine Learning for Signal Processing, Salerno, Italy, 2016.
M. J. Johnson, Linderman, S. W., Datta, S. R., and Adams, R., “Discovering Switching Autoregressive Dynamics in Neural Spike Train Recordings”. Computational and Systems Neuroscience (Cosyne) Abstracts, Salt Lake City, UT, USA, 2015.
S. W. Linderman, Adams, R., and Pillow, J., “Inferring structured connectivity from spike trains under negative-binomial generalized linear models”. Computational and Systems Neuroscience (Cosyne) Abstracts, Salt Lake City, UT, USA, 2015.
N. Amir, Besold, T. R., Camoriano, R., Erdogan, G., Flynn, T., Gillary, G., Gomez, J., Herbert-Voss, A., Hotan, G., Kadmon, J., Linderman, S. W., Liu, T. T., Marantan, A., Olson, J., Orchard, G., Pal, D. K., Pasquale, G., Sanders, H., Silberer, C., Smith, K. A., de Briton, C. Stein N., Suchow, J. W., Tessler, M. H., Viejo, G., Walker, D., Wehbe, L., Barbu, A., Isik, L., Mackevicius, E., and Meroz, Y., “Abstracts of the 2014 Brains, Minds, and Machines Summer Course”. 2014.
S. W. Linderman, Johnson, M. J., Wilson, M. A., and Chen, Z., “A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation.”. 2014.
S. W. Linderman, Stock, C., and Adams, R., “A framework for studying synaptic plasticity with neural spike train data”, in Neural Information Processing Systems, 2014.