Hi! I’m a Ph.D. student in Computer Sciences at the University of Wisconsin–Madison and the Waisman Center, where I am advised by Daifeng Wang.

My research focuses on interpretable machine learning for single-cell genomics, with interests in optimal transport, graph neural networks and attention models. I have developed computational methods for multi-modal imputation and modelling time-series transcriptomics for single-cells.

I am part of the PsychAD Consortium, where I’m working on developing computational tools to study neuropsychiatric symptoms (NPS) in Alzheimer’s disease (AD) and related dementias (ADRD).

I completed my undergraduate studies in Computer Engineering at Pune University, followed by a master’s in Computer Science at Stony Brook University, where I was advised by Samir Das.

News

• [September 2025] Starting my postdoc at Stanford in the Pritchard Lab!
• [August 2025] Successfully defended my PhD dissertation!
• [July 2025] Presented ARTEMIS at ISMB/ECCB 2025 in Liverpool, UK.
• [April 2025] ARTEMIS, our method for modeling time-series single-cell trajectories, was accepted at the ECCB 2025 proceedings. Press 1 | Press 2
• [November 2023] Gave a recorded talk on CMOT at RSGDREAM 2023 at UCLA.
• [October 2023] DeepGAMI, our method for multi-modal integration using deep auxiliary learning, was published in Genome Medicine. Press
• [July 2023] CMOT, our method for cross-modality imputation using optimal transport, was published in Genome Biology.
• [May 2022] Presented a poster on cross-modality imputation at RECOMB 2022 in San Diego.
• [May 2021] Awarded the Computer Science Graduate Summer Scholarship.