# Research

My research encompasses many topics, including causal inference, hypothesis testing, neuroscience, etc. A full list is also available on Google Scholar.

## 📄 Universally Consistent K-Sample Tests via Dependence Measures

**Sambit Panda***, Cencheng Shen*, Ronan Perry, Jelle Zorn, Antoine Lutz, Carey E. Priebe, Joshua T. Vogelstein

*Stat & Prob Letters*, 2025Introduces the idea that the k-sample testing problem and independence testing problem are equivalent up to a transformation of the data.

## 📝 hyppo: A Multivariate Hypothesis Testing Python Package

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

*arXiv*, 2024Introduces

`hyppo`

, a package that incorporates conventional and novel multivariate hypothesis tests.## 📝 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*, 2024Provides a tool for creating a machine learning based scientific assessment using data from the Healthy Brain Network (HBN).

## 📄 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*, 2024Introduces

`FiPhA`

, a R package for performing fiber photometry analysis.## 📝 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*, 2024Models batch effects as causal effects, and introduces approaches that leverage causal machinery to mitigate these effects.

## 📄 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

*BP: GOS*, 2024Investigates the role of norepinephrine (NE), a neurotransmitter, in fear and NE release changes with genotype, sex, etc.

## 📝 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*, 2023Generalizes causal estimators to arbitrary dimensional space and uses this to develop a new test (Causal CDcorr).

## 📝 Simplest Streaming Trees

Haoyin Xu, Jayanta Dey,

**Sambit Panda**, Joshua T. Vogelstein

*arXiv*, 2023Developed a streaming algorithm for decision trees based on the simplest possible extension of them.

## 📝 Learning Interpretable Characteristic Kernels via Decision Forests

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

*arXiv*, 2023Demonstrates the kernel derived from random forest is characteristic and develops a hypothesis test based on that fact (KMERF).

## 📄 The Chi-Square Test of Distance Correlation

Cencheng Shen,

**Sambit Panda**, Joshua T. Vogelstein

*JCGS*, 2022Derives an approximation to the p-value of distance correlation that bypasses the permutation test with no significant loss of power.

## 📝 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*, 2021Illustrates that forest based methods excel at tabular data classification at small sample sizes while networks excel at larger sample sizes.

## 🎓 Multivariate Independence and k-sample Testing

**Sambit Panda**

*Johns Hopkins*, 2020My master’s thesis, which introduces a Python package and a new framework for k-sample testing.

## 📄 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*, 2018Developed 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.

## 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

2023Applies a random forest based hypothesis test (specifically KMERF) to evaluate the effectiveness of a neurological screening test for mice.