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2025/05 - present    Tenure Track Associate Professor

2024/02 - 2025/04   Associate Research Scientist, Flatiron Institute

2019/08 - 2023/12   Postdoctoral Fellow, Harvard University

2012/09 - 2019/06   Ph.D. student, Peking University

2008/09 - 2012/06   BSc in Physics, Central China Normal University

We are broadly interested in how the nervous systems process information at different scales. Our research aimsto elucidate principles of neural computation in the brain by analyzing how the brain acquires, stores, andmanipulates information to support adaptive behaviors. To this end, we integrate techniques from statisticalphysics, dynamical systems, machine learning and information theory, and work closely with experimental collaborators to build mechanistic and interpretable models. We emphasize the shared principles of neuralcomputation across different systems, and generalizable to build more efficient artificial intelligent systems.

Some of the core questions we are addressing include:

How does the brain sense and represent the world around us?

How does it store, update, and organize memories over time?

How can we translate these biological insights into better learning algorithms?

1. S. Qin, S. Farashahi, D. Lipshutz, A. M. Sengupta, D. B. Chklovskii, and C. Pehlevan. “Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning”. Nature Neuroscience 26 (2), 339-349 (2023).

2. P. Masset*, S. Qin*, J. Zavatone-Veth*. “Drifting Neuronal Representations: Bug or Feature?”, Biological Cybernetics, 116, 253–266 (2022). (*equal contribution)

3. Y. Liu, Q. Li, C. Tang, S. Qin†, and Y. Tu†. “Short-Term Plasticity Regulates Both Divisive Normalization and Adaptive Responses in Drosophila Olfactory System”. Frontiers in Computational Neuroscience 15, 730431 (2021).(† corresponding author)

4. S. Qin, N. Mudur, C. Pehlevan,“Contrastive Similarity Matching for Supervised Learning”, Neural Computation 33(5), 1300 (2021).

5. S. Qin*, Q. Li*, C. Tang and Y. Tu, “Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity”, Proc. Natl. Acad. Sci. U.S.A., 116, 20286 (2019). (*equal contribution)

6. S. Qin and C. Tang, “Early-warning signals of critical transition: Effect of extrinsic noise”, Physical Review E, 97, 032406 (2018).

We are seeking highly motivated candidates for positions of PhD students, postdoctoral researchers and research assistants. If you are interested in working with us, send me an email ssqin(at) sjtu.edu.cn