Geri Skenderi is a postdoctoral researcher at Bocconi Institute for Data Science and Analytics with nine years of experience at the crossroads of statistical physics, algorithms, and modern neural networks. His work focuses on probing the limitations of deep learning—creating hard benchmarks and physics-inspired algorithms for combinatorial optimization—while maintaining active interests in graph and disentangled representation learning. He completed a PhD in Computer Science at the University of Verona, was a visiting scholar at Michigan State’s DSE Lab, and collaborates with IFOM on AI for multi-omics integration. Comfortable moving between theory and applied projects, he has industrial experience delivering clustering, time-series forecasting, and ML-driven products for startups and spin-offs. Outside of research he composes music and follows anime, a creative counterpoint that often fuels unconventional approaches to technical problems.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Università degli Studi di Verona
Master's degree, Computational Data Science, 110/110 cum laude, Master's degree, Computational Data Science, 110/110 cum laude at Free University of Bozen-Bolzano
Visiting Scholar, Visiting Scholar at Michigan State University
Contributions:77 commits, 13 PRs, 73 pushes in 3 months
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