I'm a first-year PhD in Computer Science student at Stanford University currently rotating in the Kundaje Lab. Previously, I did research in the Azizi Lab at Columbia University and the Pe'er Lab at Memorial Sloan Kettering Cancer Center. My research lies at the intersection of machine learning and cancer genomics. Specifically, I'm interested in developing novel machine learning methods which can both learn from data and incorporate prior knowledge, and applying these methods to drive biological discovery and enable precision medicine.
I also love teaching! Most recently, I taught for AI4ALL, a CS/AI summer program for high school students at Columbia. My goal is to teach students mad skills and get them excited about CS, so that we can increase diversity in our field.
Katie Maurer, Cameron Park, Shouvik Mani, Mehdi Borji, Livius Penter, Yinuo Jin, Jia Yi Zhang, Crystal Shin, James R. Brenner, Jackson Southard, Sachi Krishna, Wesley Lu, Haoxiang Lyu, Domenic Abbondanza, Chanell Mangum, Lars Ronn Olsen, Donna S. Neuberg, Pavan Bachireddy, Samouil L. Farhi, Shuqiang Li, Kenneth J. Livak, Jerome Ritz, Robert J. Soiffer, Catherine J. Wu, Elham Azizi
Under review at Cell
A comprehensive study of the mechanisms of response and resistance to donor lymphocyte infusion (DLI), an established adoptive cellular therapy, by analyzing single-cell genomic data from bone-marrow samples of 25 patients with relapsed myeloid leukemia. The video above shows the expansion of a population of CD8+ cytotoxic T lymphocytes (CTLs) (C0) which interact with and inhibit leukemia cells (MC1 Leuk) in acute myeloid leukemia (AML) responders.
Cameron Park, Shouvik Mani, Nicolas Beltran-Velez, Katie Maurer, Satyen Gohil, Shuqiang Li, Teddy Huang, David A. Knowles, Catherine J. Wu, Elham Azizi
Research in Computational Molecular Biology (RECOMB) 2024
Cell-cell interactions are fundamental to normal biological processes
and disease, but their evolution over time is poorly understood. DIISCO characterizes
the temporal dynamics of intercellular interactions using scRNA-seq data
from multiple time-points. It features a Bayesian framework which infers interactions
between cell types according to their co-evolution and incorporates
prior knowledge on receptor-ligand complexes.
Shouvik Mani, Doron Haviv, Russell Z. Kunes, Dana Pe'er
Neural Information Processing Systems (NeurIPS) 2022, Learning Meaningful Representations of Life (LMRL) Workshop [talk]
SPOT is a framework to analyze cellular microenvironments in spatial transcriptomic
data, featuring methods to represent environments, measure their similarities, and
perform clustering.
Teaching
I was a TA for an Applied Machine Learning course at Columbia, for which I created two assignments [1, 2] and taught a lecture on Unsupervised Learning.
At CMU, I was a TA for Professor Zico Kolter's
Practical Data Science
course. I supported a class of over 300 students in implementing and applying data
science techniques.
Teaching a computational biology class at Stanford Splash.
I believe that education can bethe great equalizer, but that achieving this ideal requires a commitment to creating opportunity for all. To this end, I enjoy supporting CS education outreach efforts in my community. I have taught CS in local high schools through the Microsoft TEALS Program, Girls Who Code at Columbia, AI4ALL at Columbia, and Stanford Splash. I encourage you to get involved in these wonderful programs!
Personal
I love running, biking, and playing soccer.
Philadelphia Half Marathon
One of my favorite memories from CMU is participating in
Buggy.
Buggy is a CMU tradition where student orgs build carbon fiber vehicles and train
year-round to prepare for relay races on a mile-long course during Spring Carnival.
And yes, there is a driver inside the vehicle!
Pushing Buggy for a CMU student org
I also like traveling (and flying) a lot! I hope to get my pilot's license one day. Some cool places I've been to:
Cell-cell interactions are fundamental to normal biological processes
and disease, but their evolution over time is poorly understood. DIISCO characterizes
the temporal dynamics of intercellular interactions using scRNA-seq data
from multiple time-points. It features a Bayesian framework which infers interactions
between cell types according to their co-evolution and incorporates
prior knowledge on receptor-ligand complexes.
SPOT is a framework to analyze cellular microenvironments in spatial transcriptomic
data, featuring methods to represent environments, measure their similarities, and
perform clustering.
