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.
I am working on ReproScreener, a tool to automate and enable the verification of computational reproducibility in machine learning at scale by leveraging LLMs.
Publications
Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines
- Adhithya Bhaskar and Victoria Stodden. 2024. Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines. In Proceedings of the 2nd ACM Conference on Reproducibility and Replicability (ACM REP ‘24). Association for Computing Machinery, New York, NY, USA, 101–109.
- doi/10.1145/3641525.3663629
Learning from reproducing computational results: introducing three principles and the Reproduction Package.
- Matthew Krafczyk, August Shi, Adhithya Bhaskar, Darko Marinov and Victoria Stodden. 2021. Philosophical Transactions of the Royal Society A. 3792020006920200069.
- doi/10.1098/rsta.2020.0069
Scientific Tests and Continuous Integration Strategies to Enhance Reproducibility in the Scientific Software Context
- Matthew Krafczyk, August Shi, Adhithya Bhaskar, Darko Marinov and Victoria Stodden. 2019. In Proceedings of the 2nd International Workshop on Practical Reproducible Evaluation of Computer Systems (P-RECS ‘19). ACM, New York, NY, USA, 23-28.
- doi/10.1145/3322790.3330595
An Empirical Evaluation of Computational Reproducibility
- Victoria Stodden, Matthew S. Krafczyk, and Adhithya Bhaskar. 2018. Enabling the Verification of Computational Results: In Proceedings of the First International Workshop on Practical Reproducible Evaluation of Computer Systems (P-RECS’18). ACM, New York, NY, USA, Article 3, 5 pages.
- doi/10.1145/3214239.3214242
Awards
Jenny Wang Excellence in Teaching Award
- Viterbi School of Engineering
- May 1, 2023
Outstanding Teaching Assistant of the year
- Daniel J. Epstein Department of Industrial and Systems Engineering
- 2022-2023