Shouvik Mani

Email · @shouvikmani · LinkedIn · CV

I'm an MS in Computer Science student in the Azizi Lab at Columbia University and a research intern in 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.

Prior to Columbia, I was a data scientist at C3 AI and received my BS in Statistics and Machine Learning from Carnegie Mellon University.

My Research

DIISCO: Dynamic Intercellular Interactions in Single Cell Transcriptomics
Cameron Park, Shouvik Mani, Satyen Gohil, Katie Maurer, Catherine J. Wu, Elham Azizi
International Conference on Machine Learning (ICML) 2022, Workshop on Computational Biology
[journal submission under preparation] [talk]

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: Spatial Optimal Transport for Analyzing Cellular Microenvironments
Shouvik Mani, Doron Haviv, Russell Z. Kunes, Dana Pe'er
Neural Information Processing Systems (NeurIPS) 2022, Learning Meaningful Representations of Life (LMRL) Workshop
[journal submission under preparation] [talk]

SPOT is a framework to analyze cellular microenvironments in spatial transcriptomic data, featuring methods to represent environments, measure their similarities, and perform clustering.

Automatic Digitization of Engineering Diagrams using Deep Learning and Graph Search
Shouvik Mani, Michael A. Haddad, Dan Constantini, Willy Douhard, Qiwei Li, Louis Poirier
Computer Vision and Pattern Recognition (CVPR) 2020, Diagram Image Retrieval and Analysis Workshop
[supplemental] [video] [blog post]

A computer vision pipeline which digitizes engineering diagrams by detecting common symbols (red), detecting and parsing text (blue), and identifying connections between symbols via lines (not shown).

Expert-guided Regularization via Distance Metric Learning
Shouvik Mani, Mehdi Maasoumy, Sina Pakazad, Henrik Ohlsson
Neural Information Processing Systems (NeurIPS) 2019, Learning with Rich Experience Workshop

Distance Metric Learning Regularization (DMLreg) is an approach to elicit prior knowledge from domain experts through pairwise similarity comparisons and incorporate that knowledge into a regularized linear model. DMLreg helps improve model performance in high-dimensional settings.

Intelligent Pothole Detection and Road Condition Assessment
Umang Bhatt, Shouvik Mani, Edgar Xi, J. Zico Kolter
Bloomberg Data for Good Exchange 2017
[blog post]


In Fall 2021, 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 [slides].

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 lecture on time series modeling in Practical Data Science.

I believe that education can be the great equalizer, but that achieving this ideal requires a commitment to creating opportunity for all. To this end, I’m actively involved in 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, and AI4ALL at Columbia. I encourage you to get involved in these wonderful programs!


I love running, biking, and playing soccer.

Philadelphia Half Marathon 2021

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! Some cool places I've been to: