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Research

If you are interested in extensions of AI and/or how it can be used to improve human health, you are in the right place. You can browse my original research below and on Google Scholar.

  1. 📄 Universally Consistent K-Sample Tests via Dependence Measures

    Sambit Panda*, Cencheng Shen*, Ronan Perry, Jelle Zorn, Antoine Lutz, Carey E. Priebe, Joshua T. Vogelstein
    Statistics & Probability Letters, 2025

    Introduces the idea that the k-sample testing problem and independence testing problem are equivalent up to a transformation of the data.

  2. 📝 hyppo: A Multivariate Hypothesis Testing Python Package

    Sambit Panda, Satish Palaniappan, Junhao Xiong, Eric W. Bridgeford, Ronak Mehta, Cencheng Shen, Joshua T. Vogelstein
    arXiv, 2024

    Introduces hyppo, a package that incorporates conventional and novel multivariate hypothesis tests.

  3. 📝 Accurate and efficient data-driven psychiatric assessment using machine learning

    Kseniia Konishcheva, Bennett Leventhal, Maki Koyama, Sambit Panda, Joshua T. Vogelstein, Michael Milham, Ariel Lindner*, Arno Klein*
    PsyArXiv, 2024

    Provides a tool for creating a machine learning based scientific assessment using data from the Healthy Brain Network (HBN).

  4. 📄 FiPhA: an open-source platform for fiber photometry analysis

    Matthew F. Bridge, Leslie R. Wilson, Sambit Panda, Korey D. Stevanovic, Ayland C. Letsinger, Sandra McBride, Jesse D. Cushman
    Neurophotonics, 2024

    Introduces FiPhA, a R package for performing fiber photometry analysis.

  5. 📝 When no answer is better than a wrong answer: a causal perspective on batch effects

    Eric W. Bridgeford, Michael Powell, Gregory Kiar, Stephanie Noble, Jaewon Chung, Sambit Panda, Ross Lawrence, Ting Xu, Michael Milham, Brian Caffo, Joshua T. Vogelstein
    bioRxiv, 2024

    Models batch effects as causal effects, and introduces approaches that leverage causal machinery to mitigate these effects.

  6. 📄 Partial or Complete Loss of Norepinephrine Differentially Alters Contextual Fear and Catecholamine Release Dynamics in Hippocampal CA1

    Leslie R. Wilson*, Nicholas W. Plummer*, Irina Y. Evsyukova, Daniela Patino, Casey L. Stewart, Kathleen G. Smith, Kathryn S. Konrad, Sydney A. Fry, Alex L. Deal, Victor W. Kilonzo, Sambit Panda, Natale R. Sciolino, Jesse D. Cushman, Patricia Jensen
    Biological Psychiatry: Global Open Science, 2024

    Investigates the role of norepinephrine (NE), a neurotransmitter, in fear and NE release changes with genotype, sex, etc.

  7. 📝 Learning sources of variability from high-dimensional observational studies

    Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Adam Li, Cencheng Shen, Alexandra Badea, Brian Caffo, Joshua T. Vogelstein
    arXiv, 2023

    Generalizes causal estimators to arbitrary dimensional space and uses this to develop a new test (Causal CDcorr).

  8. 📝 Simplest Streaming Trees

    Haoyin Xu, Jayanta Dey, Sambit Panda, Joshua T. Vogelstein
    arXiv, 2023

    Developed a streaming algorithm for decision trees based on the simplest possible extension of them.

  9. 📝 Learning Interpretable Characteristic Kernels via Decision Forests

    Sambit Panda*, Cencheng Shen*, Joshua T. Vogelstein
    arXiv, 2023

    Demonstrates the kernel derived from random forest is characteristic and develops a hypothesis test based on that fact (KMERF).

  10. 📄 The Chi-Square Test of Distance Correlation

    Cencheng Shen, Sambit Panda, Joshua T. Vogelstein
    JCGS, 2022

    Derives an approximation to the p-value of distance correlation that bypasses the permutation test with no significant loss of power.

  11. 📝 When are Deep Networks really better than Decision Forests at small sample sizes, and how?

    Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, Carey E. Priebe
    arXiv, 2021

    Illustrates that forest based methods excel at tabular data classification at small sample sizes while networks excel at larger sample sizes.

  12. 🎓 Multivariate Independence and k-sample Testing

    Sambit Panda
    Johns Hopkins, 2020

    My master’s thesis, which introduces a Python package and a new framework for k-sample testing.

  13. 📄 Selective and Mechanically Robust Sensors for Electrochemical Measurements of Real-Time Hydrogen Peroxide Dynamics in Vivo

    Leslie R. Wilson, Sambit Panda, Andreas C. Schmidt, Leslie A. Sombers
    Analytical Chemistry, 2018

    Developed a sensor that can be used to monitor real-time dynamics of hydrogen peroxide in the brain; we used it to investigate Parkinson’s disease.


  1. 📁 Elucidating Relationships within Neurological Screening Batteries via Random Forest-Based Hypothesis Testing

    Sambit Panda, Leslie R. Wilson, Jariatu Stallone, Dalisa Kendricks, Korey Stevanovic, Jesse D. Cushman
    2023

    Applies a random forest based hypothesis test (specifically KMERF) to evaluate the effectiveness of a neurological screening test for mice.