Research

I am a postdoctoral researcher at Zuckerman Institute, Columbia working with Dr. 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, pose estimation, and neural decoding.

News

• I am working as a postdoctoral researcher at Zuckerman Institute supervised by Dr. Liam Paninski.
• I completed my PhD and published my thesis: Scalable software and models for large-scale extracellular recordings
• 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.