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Nikhil Bansal

Full Name: Prof.dr. N. Bansal
Function(s): Researcher, Professor - Technische Universiteit Eindhoven

Email: N.Bansal@cwi.nl
Telephone: +31 20 592 4152
Room: M238
Department(s): Networks and Optimization

Publications

Bansal, N, & Meka, R. (2020). On the discrepancy of random low degree set systems. In Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms. doi:10.1137/1.9781611975482.157
Bansal, N, Dadush, D.N, Garg, S, & Lovett, S. (2019). The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues. Theory of Computing, 15. doi:10.4086/toc.2019.v015a021
Bansal, N, Böhm, M, Eliáš, M, Koumoutsos, G, & Umboh, S.W. (2019). Nested convex bodies are chaseable. Algorithmica. doi:10.1007/s00453-019-00661-x
Bansal, N, Svensson, O, & Trevisan, L. (2019). New notions and constructions of sparsification for graphs and hypergraphs. In Proceedings of FOCS (pp. 910–928). doi:10.1109/FOCS.2019.00059
Bansal, N, Chalermsook, P, Laekhanukit, B, Nanongkai, D, & Nederlof, J. (2019). New Tools and Connections for Exponential-Time Approximation. Algorithmica, 81(10), 3993–4009.
Bansal, N, & Gupta, A. (2019). Potential-function proofs for gradient methods. Theory of Computing, 15. doi:10.4086/toc.2019.v015a004
Bansal, N. (2019). On a generalization of iterated and randomized rounding. In Proceedings of the Annual ACM SIGACT Symposium on Theory of Computing (pp. 1125–1135). doi:10.1145/3313276.3316313
Bansal, N, Eliéš, M, Jeż, Ł, & Koumoutsos, G. (2019). The (h,k)-Server Problem on Bounded Depth Trees. ACM Transactions on Algorithms, 15(2). doi:10.1145/3301314
Bansal, N, Dadush, D.N, & Garg, S. (2019). An algorithm for Komlós conjecture matching Banaszczyk's bound. SIAM Journal on Computing, 48(2), 534–553. doi:10.1137/17M1126795
Chen, J.-J, Bansal, N, Chakraborty, S, & von der Brüggen, G. (2018). Packing sporadic real-time tasks on identical multiprocessor systems. In 29th International Symposium on Algorithms and Computation (pp. 71:1–71:14). doi:10.4230/LIPIcs.ISAAC.2018.71

Current projects with external funding

Continuous Methods in Discrete Optimization ()
Optimization for and with Machine Learning (OPTIMAL)

Grants

Vici (2018)