Ronny Luss is a research staff member at IBM with a decade of experience applying optimization, machine learning, and large-scale data mining to problems in finance, text analysis, biology, and operations management. He holds a Ph.D. in Operations Research & Financial Engineering from Princeton and has a strong academic pedigree with postdoctoral work at Berkeley, INRIA, and Tel Aviv University where he developed scalable optimization algorithms and methods for high-dimensional regressions and genetic interaction discovery. At IBM he bridges rigorous research and industrial projects, and his open-source contributions include enhancements to Trusted-AI’s AIX360 explainability toolkit—improving the CEM algorithm and adapting code for varied data scales. Known for translating advanced optimization theory into practical tools, he combines deep statistical insight with hands-on engineering in production settings. Based in New York, he brings a rare mix of academic depth and product-oriented implementation experience.
Interpretability and explainability of data and machine learning models
Role in this project:
Back-end Developer / ML Engineer
Contributions:6 commits, 4 PRs, 4 comments in 1 year 1 month
Contributions summary:rluss made several changes to the codebase related to the AI Explainability (AIX360) project. Their contributions include updating links within the code, changing the license to BSD, and modifying the CEM (Contrastive Explanations Method) algorithm. These changes likely involved fixing dependencies, adapting the code to a new API, and improving the functionality of the CEM algorithm for generating explanations. The user also contributed by updating the code to handle data with different scales.
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