Statistical Analysis of Neural Data

We develop statistical models and open-source computational tools to extract insights from neural data. We are particularly interested in characterizing flexibility and variability in neural circuits—e.g., how do the dynamics of large neural ensembles change over the course of learning a new skill, during periods of high attention or task engagement, or during development and aging.

Summarizing these processes even on a descriptive level is a difficult and unsolved challenge in need of new analysis tools.

Laboratory for Neural Statistics website

Publications

April 23, 2026

Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion

Eghbal A. Hosseini, Brian Cheung, Evelina Fedorenko, A. Williams

Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalities. This convergence…

arXiv:2604.21836

April 10, 2026

Quasi Monte Carlo methods enable extremely low-dimensional deep generative models

Miles Martinez, A. Williams

This paper introduces quasi-Monte Carlo latent variable models (QLVMs): a class of deep generative models that are specialized for finding…

International Conference on Learning Representations (ICLR)

April 10, 2026

Partial Soft-Matching Distance for Neural Representational Comparison with Partial Unit Correspondence

Chaitanya Kapoor, A. Williams, Meenakshi Khosla

Representational similarity metrics typically force all units to be matched, making them susceptible to noise and outliers common in neural…

International Conference on Learning Representations (ICLR)

Group Leader

Alex Williams, Ph.D.

Associate Research Scientist, Statistical Analysis of Neural Data, CCN

Alex is jointly appointed as an Assistant Professor in the Center for Neural Science at NYU and an Associate Research Scientist / Project Leader at the Flatiron Institute.

Philip Greengard, Ph.D.

Associate Research Scientist, Statistical Analysis of Neural Data, CCN

Philip is an Associate Research Scientist in Flatiron’s Center for Computational Neuroscience.

Sarah Harvey, Ph.D.

Flatiron Research Fellow, Statistical Analysis of Neural Data, CCN

Sarah Harvey is a Flatiron Research Fellow in the Statistical Analysis of Neural Data at the Center for Computational Neuroscience.

Kushal Kolar

Predoctoral Researcher, Statistical Analysis of Neural Data, CCN

Kushal Kolar is a Ph.D. student at NYU in Biomedical Engineering.

Andrew Mah, Ph.D.

Flatiron Research Fellow, Statistical Analysis of Neural Data, CCN

Andrew Mah joined the Flatiron Institute’s Center for Computational Neuroscience in 2025, as a Flatiron Research Fellow.

Arina Medvedeva

Research Analyst, Statistical Analysis of Neural Data, CCN

Arina Medvedeva joined the Center for Computational Neuroscience in 2024 as a research analyst in the Statistical Analysis of Neural Data group led by Alex Williams.

Joshua Pughe-Sanford, Ph.D.

Flatiron Research Fellow, Statistical Analysis of Neural Data, CCN

Joshua Pughe-Sanford is a Flatiron Research Fellow in the Center for Computational Neuroscience (CCN).

Aramis Tanelus

Guest Researcher, Statistical Analysis of Neural Data, CCN

Aramis Tanelus is a guest researcher at the Flatiron Institute researching artificial neural networks for sound source localization in collaboration with Alex Williams.

Sarah Jo Venditto, Ph.D.

Associate Research Scientist, NeuroRSE and Statistical Analysis of Neural Data, CCN

Starting in 2024, Sarah Jo is an Associate Research Scientist for the Center for Computational Neuroscience, where she supports and develops open-source software for the center and with the Statistical Analysis of Neural Data group.

Sam Zheng

Guest Researcher, Statistical Analysis of Neural Data, CCN

Sam Zheng is a guest researcher at the Center for Computational Neuroscience (CCN) and is currently obtaining his Ph.D. at New York University (NYU).