Sophie Klumper

Full Name

S.J. Klumper

Email

Sophie.Klumper@cwi.nl

Phone

+31 20 592 4160

Room

M236

Department(s)

Networks and Optimization

Function(s)

Researcher

Sophie Klumper

Colini-Baldeschi, R., Klumper, S., Kroll, T., Leonardi, S., Schäfer, G., & Tsikiridis, A. (2026). Optimal type-dependent liquid welfare guarantees for autobidding agents with budgets. In Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 1795–1823). doi:10.1137/1.9781611978971.64
Klumper, S. (2025, October 23). The gap and the gain : improving the approximate mechanism design frontier in constrained environments. ILLC Dissertation Series. Retrieved from http://hdl.handle.net/11245.1/a0a900d5-4b38-4032-b1f3-8eb69e26040c
Amanatidis, G., Klumper, S., Markakis, V., Schäfer, G., & Tsikiridis, A. (2025). Partial allocations in budget-feasible mechanism design: Bridging multiple levels of service and divisible agents. ACM Transactions on Economics and Computation, 13(2), 9:1–9:28. doi:10.1145/3723883
Colini-Baldeschi, R., Klumper, S., Schäfer, G., & Tsikiridis, A. (2024). To trust or not to trust: Assignment mechanisms with predictions in the private graph model. In Proceedings of the ACM Conference on Economics and Computation (pp. 1134–1154). doi:10.1145/3670865.3673578
Haret, A., Klumper, S., Maly, J., & Schäfer, G. (2024). Committees and equilibria: Multiwinner approval voting through the lens of budgeting games. In Proceedings of the ACM Conference on Economics and Computation (pp. 51–70). doi:10.1145/3670865.3673484
Amanatidis, G., Klumper, S., E. Markakis, Schäfer, G., & Tsikiridis, A. (2023). Partial allocations in budget-feasible mechanism design: Bridging multiple levels of service and divisible agents. In Proceedings of the International Conference on Web and Internet Economics (pp. 41–58). doi:10.1007/978-3-031-48974-7_3
Klumper, S., & Schäfer, G. (2022). Budget feasible mechanisms for procurement auctions with divisible agents. In Proceedings of SAGT 2022 (pp. 78–93). doi:10.1007/978-3-031-15714-1_5

Customized and Coordinated Solutions for Congestion Bottlenecks in our infrastructure (COCONUT)
Optimization for and with Machine Learning (OPTIMAL)