DR. ARUN PATEL Computational Neuroscientist I build models that decode what populations of neurons are computing. Bridging Bayesian inference, deep learning, and experimental neuroscience. arun@arunpatel.science · +1 (617) 555 0231 · Cambridge, MA · arunpatel.science · Google Scholar: https://scholar.google.com/citations?user=xxxx · ORCID: https://orcid.org/0000-0003-1234-5678 · GitHub: https://github.com/arunpatel-neuro RESEARCH SUMMARY ──────────────────────── I develop computational methods for understanding how neural circuits implement decision-making and memory. My work combines large-scale electrophysiology (Neuropixels, calcium imaging) with probabilistic modeling and representation learning. During my postdoc at MIT, I introduced a variational framework for latent-variable models of neural population dynamics that has been adopted by six labs worldwide. I have published 14 peer-reviewed papers (h-index 12, 840+ citations) and maintain three open-source analysis libraries used across the field. RESEARCH EXPERIENCE ─────────────────────────── Postdoctoral Research Associate — MIT — McGovern Institute for Brain Research · Cambridge, MA 2022 – Present Advisor: Prof. Mehrdad Jazayeri. Neural dynamics of timing and motor planning in primate prefrontal cortex. • Developed NeuroPLDI, a variational autoencoder for inferring latent dynamics from Neuropixels recordings across 400+ neurons simultaneously. • First-authored a Nature Neuroscience paper showing that prefrontal population trajectories encode elapsed time as a continuous manifold. • Built and released the pyLDI library (Python/JAX) — 600+ GitHub stars, used by 6 labs in the US and Europe. JAX · Python · Neuropixels · Bayesian inference PhD Researcher — Columbia University — Zuckerman Institute · New York, NY 2017 – 2022 Advisor: Prof. Liam Paninski. Scalable inference methods for calcium imaging data in decision-making circuits. • Developed a GPU-accelerated spike deconvolution algorithm that is 40x faster than CaImAn on 100K-neuron datasets. • First-authored three papers in Neuron, NeurIPS, and PLOS Computational Biology. • Thesis: 'Scalable Bayesian methods for neural population decoding' (Zuckerman Prize for Outstanding Thesis, 2022). Python · PyTorch · CUDA · Calcium imaging Research Intern — Google DeepMind — Neuroscience Research · London, UK 2020 – 2020 Summer internship applying deep RL models to neural data from the hippocampus. • Showed that a successor-representation RL agent predicts hippocampal place-cell remapping patterns with 89% accuracy. • Co-authored a PNAS paper with three DeepMind researchers. Python · TensorFlow · Reinforcement learning SELECTED PUBLICATIONS ───────────────────────────── Patel, A.; Remington, E.; Jazayeri, M.. "Continuous time representation in prefrontal population dynamics". Nature Neuroscience (2025) https://doi.org/10.1038/s41593-025-01892 Patel, A.; Jazayeri, M.. "NeuroPLDI: variational inference for neural population latent dynamics". NeurIPS 2024 (2024) https://arxiv.org/abs/2410.09341 Patel, A.; Paninski, L.. "GPU-accelerated spike inference for large-scale calcium imaging". Neuron (2022) https://doi.org/10.1016/j.neuron.2022.04.018 Patel, A.; Botvinick, M.; Kurth-Nelson, Z.; Behrens, T.. "Hippocampal remapping predicted by successor representations in deep RL agents". Proceedings of the National Academy of Sciences (2021) https://doi.org/10.1073/pnas.2108476119 Patel, A.; Pillow, J.; Paninski, L.. "Scalable Bayesian decoding of neural population activity". PLOS Computational Biology (2020) OPEN-SOURCE SOFTWARE ──────────────────────────── pyLDI — Creator · Maintainer (2023–) https://github.com/arunpatel-neuro/pyLDI A JAX library for latent dynamics inference from neural population data. Supports GP-based, RNN-based, and VAE-based models. Tech: JAX, Python fastspike — Creator (2021) https://github.com/arunpatel-neuro/fastspike GPU-accelerated spike deconvolution for calcium imaging. 40x faster than CaImAn on 100K-neuron datasets. Tech: Python, CUDA, PyTorch neuro-bench — Core Contributor (2022–) https://github.com/neural-benchmarks/neuro-bench Community benchmark suite for neural population analysis methods. Maintains the decoding and dimensionality reduction tracks. Tech: Python, NumPy TECHNICAL SKILLS ──────────────────────── Languages: Python, MATLAB, Julia, C++, R, SQL ML / Stats: JAX, PyTorch, TensorFlow, Stan, Bayesian inference, Variational methods Neuroscience: Neuropixels, Calcium imaging, Spike sorting, Population decoding, LFP analysis Infrastructure: SLURM, AWS, Docker, Git, Jupyter, Weights & Biases EDUCATION ───────────────── PhD, Neuroscience (Computational Track) — Columbia University (2017 – 2022) • Dissertation: 'Scalable Bayesian methods for neural population decoding' (Advisor: Liam Paninski). • Zuckerman Prize for Outstanding Thesis, 2022. BTech, Electrical Engineering, Minor in Computer Science — Indian Institute of Technology Bombay (2013 – 2017) Institute Gold Medal. President's Gold Medal for highest GPA in the graduating class. GPA: 9.4 / 10.0 HONOURS & FELLOWSHIPS ───────────────────────────── Simons-Emory International Consortium Fellowship — Simons Foundation (2023) Two-year fellowship supporting computational neuroscience research. NeurIPS 2024 Spotlight Paper — NeurIPS (2024) Zuckerman Prize for Outstanding Thesis — Columbia University (2022) Kavli Summer Institute Fellow — Kavli Foundation / UCSD (2019) Institute Gold Medal — IIT Bombay (2017) LANGUAGES ───────────────── English — Fluent · Hindi — Native · Gujarati — Native · French — Basic