Adhithya Bhaskar
Hi there! I am a PhD candidate in the ISE department at the University of Southern California advised by Dr. Victoria Stodden.
My research focuses on developing metrics and tools that facilitate transparent and verifiable machine learning research, with an emphasis on promoting computational reproducibility through open code and data standards. I am also interested in developing LLM frameworks to automate the assessment of reproducibility metrics while ensuring meta-level reliability and trustworthiness of results.
My current ReproReady work develops a local, offline, deterministic tool for statically checking computational research artifacts. It is designed to help researchers understand the manual work required to reproduce reported computational results, rather than predict whether the code will run.
ReproReady builds on ReproScreener, my earlier work using LLM-assisted evaluation to assess computational reproducibility evidence in machine-learning artifacts at scale.
You can find my resume here (updated August 31, 2026).
Publications
- Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines
Adhithya Bhaskar, Victoria Stodden
ACM REP, 2024 - Learning from reproducing computational results: introducing three principles and the Reproduction Package
Matthew S. Krafczyk, August Shi, Adhithya Bhaskar, et al
Phil. Trans. R. Soc. A, 2021 - Scientific Tests and Continuous Integration Strategies to Enhance Reproducibility in the Scientific Software Context
Matthew Krafczyk, August Shi, Adhithya Bhaskar, et al
ACM P-RECS, 2019 - Enabling the Verification of Computational Results: An Empirical Evaluation of Computational Reproducibility
Victoria Stodden, Matthew S. Krafczyk, and Adhithya Bhaskar
ACM P-RECS, 2018
Awards
- Jenny Wang Excellence in Teaching Award. Viterbi School of Engineering, 2023 and 2026.
- Outstanding Teaching Assistant of the Year. Daniel J. Epstein Department of Industrial and Systems Engineering, 2023 and 2026.