Research
I am a postdoctoral researcher at Zuckerman Institute, Columbia working with Liam Paninski. My research interests lie at the intersection of computational neuroscience and machine learning where I focus on improving and applying deep generative models, analyzing large-scale neural recordings, and developing novel software tools for running and benchmarking automated spike sorters. Recently, I have been expanding my research to dynamical models of neural population recordings.
News
- I finished my PhD and am now working as a postdoctoral researcher at Zuckerman Institute.
- Our paper Targeted Neural Dynamical Modeling was accepted at NeurIPS 2021.
- Our paper Building population models for large-scale neural recordings: opportunities and pitfalls was published in Current Opinion in Neurobiology.
- New preprint: Building population models for large-scale neural recordings: opportunities and pitfalls.
- Our paper SpikeInterface, a unified framework for spike sorting was published in eLife.
- New preprint: Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling.
- New preprint: not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget.
- Our paper SpikeForest: reproducible web-facing ground-truth validation of automated neural spike sorters was published in eLife.
- New preprint: SpikeForest: reproducible web-facing ground-truth validation of automated neural spike sorters.
- New preprint: SpikeInterface, a unified framework for spike sorting.
- Our paper Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference was accepted at NeurIPS 2019.