Sainyam Galhotra

Sainyam Galhotra is a computer scientist whose research connects machine learning and data management with fair, explainable, and reliable systems. He completed undergraduate studies at IIT Delhi in 2014, coauthored research on Themis, a software fairness testing approach, and shared the ACM SIGSOFT Distinguished Paper Award in 2017. After earning a Ph.D. at the University of Massachusetts Amherst and completing postdoctoral research at the University of Chicago in 2021–2022, he was on Cornell’s computer science faculty by October 2023. His work includes METAM for goal-oriented data discovery and research on machine unlearning and concept bottleneck debugging; as of October 2026, he is an assistant professor of computer science at Cornell.

Now at: Cornell University
Works: 109
Citations: 1,383
h-index: 16

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

2014

Undergraduate studies at IIT Delhi

At IIT Delhi, he completes his undergraduate studies in May 2014 under Professor Amitabha Bagchi.
2017

Fairness testing with Themis

In 2017, he coauthors “Fairness Testing: Testing Software for Discrimination,” which introduces Themis, an approach that generates test suites to measure software discrimination. The work earns the ACM SIGSOFT Distinguished Paper Award.
2021–2022

Postdoctoral work at Chicago

At the University of Chicago, he works as a Computing Innovation Fellow and DSI postdoctoral scholar in 2021–2022. He holds a Ph.D. from the University of Massachusetts Amherst under Barna Saha’s supervision.
2023

METAM for data discovery

In 2023, he coauthors METAM, a goal-oriented framework that uses feedback from downstream tasks to guide data discovery and augmentation.
By October 2023

Assistant professor at Cornell

By October 2023, he is an assistant professor in Cornell's Department of Computer Science. His academic focus includes databases, data science, responsible analytics, causal inference, and algorithms.
2025–2026

Testing machine unlearning

A 2025 paper he coauthors, revised in October 2026, presents CAFÉ, a black-box test for whether machine-unlearned models retain removed information through direct and indirect causal paths. In 2026, he coauthors CBDebug, which uses concept-level expert feedback to retrain concept bottleneck models.
As of October 2026

Assistant professor at Cornell

As of October 2026, he is an assistant professor of computer science at Cornell. His research develops data science tools for effective and responsible analytics across data management, causal inference, machine learning, robustness, explainability, and fairness.