Dr. Arnold Filtser
Algorithmic Theory, Metric Spaces, and geometric data summarization
CV
Dr. Arnold Filtser is one of Israel’s leading young researchers in algorithms and a faculty member in the Department of Computer Science at Bar-Ilan University. He completed his PhD at Ben-Gurion University of the Negev, in collaboration with the Weizmann Institute of Science, and subsequently conducted postdoctoral research at Columbia University as part of the Simons Collaboration on Algorithms and Geometry.
His work is regularly published at the most prestigious and selective conferences in theoretical computer science, including STOC, FOCS, and SODA. His research has received international recognition and support through prestigious, highly competitive research grants. At Bar-Ilan, he leads an active and ambitious research group that offers outstanding students close individual supervision, opportunities to participate in cutting-edge research, and generous scholarships.
Research
Algorithms, Metric Spaces, Graph theory, and computational geometry
Dr. Arnold Filtser’s work stands at the center of theoretical computer science, at the intersection of algorithms, geometry, and graph theory. A central theme in his research is the summarization of geometric data: finding simpler ways to represent large graphs, networks, and distance-based data while preserving the information that matters most. For example, instead of working with a huge and complicated network directly, one may try to build a smaller or cleaner version that still captures the important distances, routes, and connections. This kind of simplification can lead to faster algorithms, better approximations, and a deeper understanding of the structure hidden inside complex data.
Primary Research Areas:
Low-Distortion Metric Embeddings: Mapping data from complex spaces to simpler ones while maintaining the distance between points.
Stochastic Decompositions: Methods for breaking down massive datasets into smaller, manageable sub-structures.
Spanners & Light Networks: Constructing efficient networks that connect points with short paths using minimal connections.
Approximation Algorithms and Computational Geometry: Finding fast, near-optimal solutions to complex geometric problems.
Research Nature:
Theoretical and mathematical research: We uncover the hidden structure of a problem, formulate conjectures, design novel algorithms and representations, and prove that they provide the required guarantees. The work draws on tools from graph theory, probability, and geometry, and requires creativity and independent thinking.
Career Path:
Training in the group prepares students for algorithm development and research roles on the R&D teams of technology companies, such as Algorithm Engineer and Research Scientist. These roles involve developing novel solutions to problems in optimization, networks and routing, mapping systems, and large-scale data analysis. Alongside technical expertise, research cultivates independent and rigorous thinking, enabling students to analyze complex computational problems and approach them systematically and precisely.. In a rapidly evolving technological landscape, this foundational analytical mindset is highly valued and ensures lifelong professional relevance.
Last Updated Date : 20/08/2026
