Ronny Luss
Research Staff Member at IBM
New York, New York, United States
Summary
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Senior🎓
Top SchoolRonny 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.
10 years of coding experience
3 years of employment as a software developer
MS, Management Science & Engineering, MS, Management Science & Engineering at Stanford UniversityBSE, Computer Science & Engineering, BSE, Computer Science & Engineering at University of PennsylvaniaPh.D., Operations Research & Financial Engineering, Ph.D., Operations Research & Financial Engineering at Princeton University
Github Skills (5)
explainable-artificial-intelligence10
tensorflow10
python10
machine-learning9
algorithms9
Programming languages (1)
Github contributions (3)
Trusted-AI/AIX360
Aug 2019 - Sep 2020
Interpretability and explainability of data and machine learning models
Role in this project:
Back-end Developer / ML Engineer
Back-end Developer / ML EngineerContributions:6 commits, 4 PRs, 4 comments in 1 year 1 month
Contributions summary:Ronny 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.
explainable-mlibm-research-aitrusted-aicodaitdeep-learning
omertripp/grouptesting
Mar 2016 - Apr 2016
Contributions:9 pushes in 9 days
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