Approximation & Polyhedral Methods
Designing and proving hardness of algorithms with provable guarantees by exploiting structure in convex relaxations and polyhedra.
Associate Professor • Theory Group at EPFL
Ola Svensson
I study the foundations of algorithms and computation, with emphasis on approximation, combinatorial optimization, computational complexity, and scheduling, as well as online, streaming, and parallel algorithms.
Approximation algorithms
Online & streaming
Learning-augmented
Data-driven optimization
Combinatorial optimization
Contact
ola.svensson@epfl.ch
EPFL, 1015 Lausanne, Switzerland
Research
A major part of my work addresses the challenge of finding near-optimal solutions for NP-hard problems through approximation algorithms and polyhedral techniques. Recently, I also pursue conceptual and ML-inspired theory directions including learning-augmented algorithms, data-driven optimization, and beyond-worst-case analysis, alongside fundamental work on online and streaming algorithms, parallel computation, scheduling, and the hardness of approximation in combinatorial optimization.
Online, Streaming & Parallel
Algorithms that adapt to evolving data, limited memory, and parallel computation models.
Learning-Augmented & Data-Driven Optimization
Beyond-worst-case analysis, learning-augmented algorithms, and portfolio-style decision making for data-driven optimization problems.
Short CV
Selected honors, grants, and academic appointments.
Honors & Grants
- Frontier of Science Award (ICBS), 2023
- Invited Speaker, International Congress of Mathematicians, 2022
- ERC Consolidator Grant (POTCO), 2023-
- Michael and Sheila Held Prize (NAS), 2019
- Best Paper Award, STOC 2018
- Best Paper Award, FOCS 2017
- Best Paper Award, FOCS 2011
- ERC Starting Grant (OptApprox), 2014-2019
- SNF Grant: Randomness in Problem Instances and Randomized Algorithms, 2019-2023
- I&C Teaching Award, EPFL, 2014
Appointments & Visits
- 2024, 2026 Visiting Faculty Researcher, Google Research
- 2018- Associate Professor, EPFL
- 2012-2018 Tenure-Track Assistant Professor, EPFL
- 2015, 2018 Visiting Researcher, Microsoft Research Redmond
- 2011-2012 Postdoctoral Researcher, EPFL
- 2017, 2022 Long-term participant, Simons Institute for the Theory of Computing
- 2009-2011 Postdoctoral Researcher, KTH Royal Institute of Technology
- 2008 Visiting Student Researcher, MIT
- 2006-2009 PhD Student, IDSIA / USI (Advisor: Monaldo Mastrolilli)
Teaching
I am very proud to have received the EPFL I&C teaching award. For semester project opportunities, please contact me by email.
Algorithms (Bachelor, Master)
Core algorithms courses covering basic and more advanced concepts.
Topics in Theoretical Computer Science (Master)
Advanced topics in theoretical computer science, approximation, and modern optimization.
LLM-Enhanced Teaching
Introduced a flipped classroom model where over 500 students explained key algorithms using large language models. Try it out here.
Students
PhD students advised at EPFL.
Current PhD Students
- Davide Mazzali (since 2023, co-advised with Michael Kapralov)
- Lukas Vogl (since 2022, co-advised with Friedrich Eisenbrand)
- Miltiadis Stouras (since 2022)
- Radu Vintan (since 2022)
- Yuan Weiqiang (since 2021, co-advised with Mika Goos)
- Marina Drygala (since 2021)
Past PhD Students
- Andreas Maggiori (2019-2023, co-advised with Rudiger Urbanke)
- Xinrui Jia (2019-2023)
- Etienne Bamas (2019-2023)
- Paritosh Garg (2017-2022, co-advised with Michael Kapralov)
- Buddhima Gamlath (2016-2021)
- Jakub Tarnawski (2014-2019)
- Ashkan Norouzi-Fard (2013-2018)
- Abbas Bazzi (2013-2017)
- Christos Kalaitzis (2012-2016)
Publications
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