Sambit Panda

I'm a PhD candidate at Johns Hopkins, where I am advised by Joshua T. Vogelstein in the NeuroData lab. Most days, I develop and apply high-dimensional and nonlinear machine learning algorithms to answer interesting biomedical questions. Here's a little bit more about me.

Publications

Here are some of my favorite research articles that I have written. If you want to read more, here is the full publication list.

• The Chi-Square Test of Distance Correlation
  by Cencheng Shen, Sambit Panda, and Joshua T. Vogelstein
  in Journal of Computational and Graphical Statistics on January, 2022
• Selective and Mechanically Robust Sensors for Electrochemical Measurements of Real-Time Hydrogen Peroxide Dynamics in Vivo
  by Leslie R. Wilson, Sambit Panda, Andreas C. Schmidt, and Leslie A. Sombers
  in Analytical Chemistry on January, 2018

Software

I love solving difficult problems, and often times develop software to help. If you want to see more, here is the full software list.

scikit-tree

Scikit-learn compatible decision trees beyond those offered in scikit-learn.

scipy.stats.multiscale_graphcorr

Multiscale Graph Correlation (MGC) is a powerful, universally consistent independence test that performs well on high-dimensional and non-Euclidean data.

hyppo

hyppo (HYPothesis Testing in PythOn, pronounced “Hippo”) is an open-source software package for multivariate hypothesis testing.