Sebastian Pokutta

Artificial Intelligence, Optimization, and Machine Learning. Professor @TUBerlin, Vice President @ZuseInstitute

Research Lab

My group is interested in Artificial Intelligence, Optimization, and Machine Learning and its applications. We develop new methodologies (e.g., new optimization and learning algorithms), work on combining learning and decision-making, as well as design AI Systems for real-world deployment in various application contexts. Recent examples include the estimation of biomass from satellite data, entanglement and non-locality thresholds via optimization, new constructions in extremal combinatorics via AI, as well as questions around AI and creativity. [group homepage] [more about research and projects]

TL;DR. We use computers to learn from data and make better decisions.

Prospective Students. If you are interested in working in our group or writing your MS/BS thesis please check our openings.

Select Recent Papers

(see publications for a complete list)

  1. Sadiku, S., Wagner, M., Nagarajan, S. G., and Pokutta, S. (2025). S-CFE: Simple Counterfactual Explanations. To Appear in Proceedings of AISTATS. [arXiv]
  2. Roux, C., Zimmer, M., and Pokutta, S. (2025). On the Byzantine-Resilience of Distillation-Based Federated Learning. To Appear in Proceedings of ICLR. [arXiv] [summary] [code]
  3. Sadiku, S., Wagner, M., and Pokutta, S. (2025). Group-wise Sparse and Explainable Adversarial Attacks. To Appear in Proceedings of ICLR. [arXiv] [poster]
  4. Mexi, G., Kamp, D., Shinano, Y., Pu, S., Hoen, A., Bestuzheva, K., Hojny, C., Walter, M., Pfetsch, M. E., Pokutta, S., and Koch, T. (2025). State-of-the-art Methods for Pseudo-Boolean Solving with SCIP. Preprint. [arXiv]
  5. Martinez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2025). Accelerated Methods for Riemannian Min-Max Problems. To Appear in Proceedings of AISTATS. [arXiv]
  6. Wirth, E., Besançon, M., and Pokutta, S. (2025). The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control. To Appear in Proceedings of AISTATS. [arXiv]
  7. Wirth, E., Peña, J., and Pokutta, S. (2024). Accelerated Affine-Invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes. To Appear in Mathematical Programming A. [PDF] [arXiv]
  8. Haase, J., and Pokutta, S. (2024). Human-AI Co-Creativity: Exploring Synergies Across Levels of Creative Collaboration. Preprint. [arXiv]
  9. Głuch, G., Turan, B., Nagarajan, S. G., and Pokutta, S. (2024). The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses. Preprint. [arXiv] [summary] [poster]
  10. Designolle, S., Vértesi, T., and Pokutta, S. (2024). Better bounds on Grothendieck constants of finite orders. Preprint. [arXiv]
  11. Braun, G., Pokutta, S., and Woodstock, Z. (2024). Flexible block-iterative analysis for the Frank-Wolfe algorithm. Preprint. [arXiv]
  12. Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., Franco, N., Fratini, F., Fuller, B., Gacon, J., Gonciulea, C., Gribling, S., Gupta, S., Hadfield, S., Heese, R., … Zoufal, C. (2024). Quantum Optimization: Potential, Challenges, and the Path Forward. Nature Reviews Physics. [PDF] [arXiv]
  13. Wirth, E., Peña, J., and Pokutta, S. (2024). Fast Convergence of Frank-Wolfe algorithms on polytopes. Preprint. [arXiv]
  14. Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024). Estimating Canopy Height at Scale. Proceedings of ICML. [arXiv] [code]
  15. Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Geombinatorics Quarterly. [arXiv] [summary] [slides]
  16. Martinez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of ICML. [arXiv]
  17. Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. ICLR 2024 Workshop on AI4DifferentialEquations In Science. [PDF] [arXiv] [slides] [poster]
  18. Pokutta, S. (2024). The Frank-Wolfe algorithm: a short introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung, 126, 3–35. [PDF] [arXiv]
  19. Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokthari, A., and Pokutta, S. (2022). Conditional Gradient Methods. Preprint. [arXiv]

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