Sambit Panda

AI Engineer (PhD) @ MATRIX AI Consortium

I’m an AI Engineer at the MATRIX AI Consortium where I am developing a end-to-end solution for trauma care using AI. Previously, I did my PhD at Johns Hopkins, where I was advised by Joshua T. Vogelstein. Here’s a little bit more about me.

Research

Here are some of my favorite research articles. If you want to read more, take a look at the full publication list.

📄 Universally Consistent K-Sample Tests via Dependence Measures

Introduces 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

Introduces hyppo, a package that incorporates conventional and novel multivariate hypothesis tests.
📝 Learning Interpretable Characteristic Kernels via Decision Forests

Demonstrates 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

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

I love solving difficult problems, and often times develop software to help. You can see more in the full software list.

🔬 treeple

Extends scikit-learn decision trees to do oblique splits, manifold learning, hypothesis testing, etc. I am a core contributor and maintainer of this project.
🔬 hyppo

hyppo (HYPothesis Testing in PythOn, pronounced ‘Hippo’) is an open-source software package for multivariate hypothesis testing, closing the gap with R. I am the creator and maintainer of this project; I also wrote a paper about it.
🔬 scipy.stats.multiscale_graphcorr

Multiscale Graph Correlation is a powerful multivariate test (the first multivariate test in SciPy). I ported this code and am a maintainer of this method.