Research

My research lies at the intersection of computational neuroscience and machine learning, focusing on the applications of quantitative approaches to the study of the brain. With the adoption of increasingly powerful neurotechnology, novel quantitative approaches for understanding these new datasets of massive size and complexity are in increasing demand. My research interests are developing computational tools for processing and interpreting neuroscience data, and optimizing experimental designs and data acquisition through close collaborations with experimental neuroscientists. Data is the core of all my research and I am mostly interested in two central topics: 1, how to maximally identify the signals from the recorded raw data using the currently available experimental setups; 2, how to efficiently draw scientific conclusions from the data.

Biography

2020-Present, Associate Investigator, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China.

2017-2020, Postdoc, Columbia University, United States

2011-2016, Ph.D. in Neural Computation and Machine Learning, Carnegie Mellon University, United States

2006-2010, B.Sc. in Physics, University of Science and Technology of China

Selected publications

1. Zhou, P., Reimer, J., Zhou, D., Pasarkar, A., Kinsella, I.A., Froudarakis, E., Yatsenko, D., Fahey, P., Bodor, A., Buchanan, J. and Bumbarger, D.J., et.al. 2020. EASE: EM-Assisted Source Extraction from calcium imaging data. bioRxiv 03.25.007468.
2. Lu, R., Liang, Y., Meng, G., Zhou, P., Svoboda, K., Paninski, L. and Ji, N., 2020. Rapid mesoscale volumetric imaging of neural activity with synaptic resolution. Nature Methods, pp.1-4.
3. Zhou, P., Resendez, S.L., Rodriguez-Romaguera, J., Jimenez, J.C., Neufeld, S.Q., Giovannucci, A., Friedrich, J., Pnevmatikakis, E.A., Stuber, G.D., Hen, R., Kheirbek, M.A., Sabatini, B.L., Kass, R.E. and Paninski L., 2018. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife, 7, p.e28728. (highlighted by Nature Methods, 2018)
4. Friedrich, J., Zhou, P.and Paninski, L., 2017. Fast online deconvolution of calcium imaging data. PLoS computational biology, 13(3), p.e1005423.
5. Zhou, P.,Burton, S.D., Snyder, A.C., Smith, M.A., Urban, N.N. and Kass, R.E., 2015. Establishing a statistical link between network oscillations and neural synchrony. PLoS computational biology, 11(10), p.e1004549.
6. Scott, J.G., Kelly, R.C., Smith, M.A., Zhou, P.and Kass, R.E., 2015. False discovery rate regression: an application to neural synchrony detection in primary visual cortex. Journal of the American Statistical Association, 110(510), pp.459-471.
7. Zhou, P., Burton, S., Urban, N. and Ermentrout, G.B., 2013. Impact of neuronal heterogeneity on correlated colored noise- induced synchronization. Frontiers in computational neuroscience, 7, p.113.