Ohad Eytan is a Cloud Data Researcher at IBM Research with 11 years of experience combining academic rigor from a PhD in Computer Science at Technion with hands-on systems work. His research focuses on adaptive caching algorithms, and he has made practical contributions to the high-profile caffeine Java caching library—implementing and tuning FRD-based policies and simulator tooling. At IBM he progressed from intern to PhD researcher to current research staff, blending performance analysis, configuration engineering, and algorithm design for cloud data systems. Based in Israel’s Center District, he brings a rare mix of theoretical depth and production-oriented optimization expertise that directly improves cache effectiveness in real deployments.
11 years of coding experience
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Technion - Israel Institute of Technology
Contributions:23 commits, 10 PRs, 11 pushes in 3 years 2 months
Contributions summary:Ohad primarily contributed to the `simulator` portion of the `caffeine` repository, a high-performance caching library for Java. Their work involved implementing and adjusting caching policies, specifically the FRD policy and its indicator and climber adaptations. They also addressed configuration parsing for the climber, indicating involvement in system optimization and potentially performance analysis of the caching library.
Contributions:56 commits, 64 pushes, 1 branch in 2 years 7 months
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