About Me

I'm a PhD candidate in the computer science department at Purdue, advised by Prof. Ananth Grama. I work in applied data science. My PhD research centers around understanding variability and commonalities in functional brain networks. I bring a computer scientist's perspective to interesting problems in connectomics. My solutions are rooted in computationally sound methods with good performance guarantees. I have acquired tools from a diverse set of areas, including: deep learning, matrix computation, network science, and randomized numerical linear algebra.

More broadly, I'm interested in finding new solutions for problems by means of clever models, and insights from different areas of data science. To this end, I actively develop new methods, while leveraging the power of relevant existing work. A key emphasis in my work is to find the right balance between interpretability and performance.

I've previously worked on single-cell transcriptomics (first two years in Purdue), protein prediction (in TU Munich), rehabilitation robotics (in DLR/German Aerospace Center) and wireless networks (in the Indian Institute of Science).

I'm looking for full-time jobs in academia and industry [Full CV and One-page Resume].

Please send me an e-mail for research and/or teaching statements.

UPDATES:

  • Paper accepted in ICIP 2021 (Preliminary version in link)

  • Paper accepted in SIGMOD 2021

  • Paper accepted in Frontiers in Neuroscience [2021]

  • Paper accepted in IJCAI 2020