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 working on ReproScreener, a tool to automate and enable the verification of computational reproducibility in machine learning at scale.
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.
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.
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.
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