Marek Elias

Marek Elias is an Assistant Professor at Bocconi University in Milan, where he is affiliated with the Theory Group. Prior to joining Bocconi, he held postdoctoral positions at CWI (Amsterdam) and EPFL (Lausanne), following a PhD at TU Eindhoven under the supervision of Nikhil Bansal. Earlier, he studied at Charles University in Prague with advisor Jiří Matoušek.

His research focuses on algorithms for optimization under uncertainty, bridging online algorithms, differential privacy, and machine learning-augmented systems. Key areas include

• Learning-Augmented Algorithms
• Online Metric Algorithms
• Combinatorial Optimization
• Privacy-Preserving Mechanisms

The trends in his recent publications highlight advancements in online optimization with applications in machine learning and data structures, particularly in problems like the k-server system, Steiner tree approximation, and differentially private clustering. Papers often explore the interplay between algorithmic robustness and predictive modeling to enhance efficiency in uncertain environments.

He has contributed to major conferences including ICML, NeurIPS, SODA, and FOCS, with a consistent emphasis on theoretical guarantees and practical applications. While specific awards or grants are not detailed in the provided texts, his work is supported through institutional affiliations and collaborative research networks.

Marek actively engages students in research, particularly in Bocconi's Theory Group, encouraging thesis projects on learning-augmented algorithms and prediction-based optimization. His laboratory environment fosters interdisciplinary collaboration between theoretical computer science and machine learning communities.