Research interests: Theoretical computer science with emphasis on design and analysis of algorithms for discrete optimization problems. I also dabble in related areas such as discrete math, complexity theory, machine learning and probability.
Potential-Function Proofs for First-Order Methods. Nikhil Bansal, Anupam Gupta.
Algorithmic Aspects of Discrepancy. Chapter in the book Panorama of Discrepancy Theory.
New developments in iterated rounding. Write up for a talk at FSTTCS 2014.
Current and Past Advisees:
Phd: Marek Elias, Shashwat Garg,
Greg Koumoutsous, Bart Kamphort
(joint), Jorn van der Pol (joint)
Postdoc: Ilan Cohen, Jatin Batra, Makrand Sinha (joint), Laszlo Kozma, Thijs Laarhoeven, Lukasz Jez, William Seeun Umboh
Randomized Algorithms (Sp 2019).
Graphs and Algorithms (2MMD30)
Approximation Algorithms (2WO07)
Freshman Linear Algebra
Seminars: CWI N&O seminar, Hierarchies Reading Group, Eindhoven Discrete Math Seminar
Editorial Boards: Journal of the ACM, Mathematics of Operations Research. Previously
SICOMP (2012-2018) and SIDMA (2012-2018).
Recent Program Committees: SODA 2020, RANDOM 2019, FOCS 2018, HALG 2018, ICALP 2018, Approx 2017, IPCO 2017, SPAA 2017, IPDPS 2017, WAOA2016, ITCS 2016, ESA 2015 (chair), STOC 2014, FOCS 2014, ICALP 2014
6th SDP Day, Apr 2018, CWI Amsterdam
Semester on Bridging Continuous and Discrete Optimization, Fall 2017 at UC Berkeley
Optimization and Decision-Making Under Uncertainty, Oct 2016, UC Berkeley
5th SDP Days, Jun 2016, CWI Amsterdam
Dagstuhl Seminar on Scheduling, Feb 2016
Relaxation Workshop, Nov 2015, HIM, Bonn
Scheduling under Uncertainty, June 2015, Eindhoven
Stochastic Activity Month: Probability and Combinatorics, Jan 2014, Eindoven
4th SDP Days, March 2013, CWI Amsterdam
Selected Recent Publications:
On a generalization of iterated and randomized rounding. Nikhil Bansal. Arxiv.
On the discrepancy
of random low degree set systems. Nikhil Bansal, Raghu Meka. SODA 2019.
The Gram-Schmidt Walk: A cure for the Banaszczyk blues. Nikhil Bansal, Daniel Dadush, Shashwat Garg, Shachar Lovett. STOC 2018.
Competitive Algorithms for Generalized k-server in uniform metrics. Nikhil Bansal, Marek Elias, Grigorios Koumoutsos, Jesper Nederlof. SODA 2018.
Nested Convex Bodies are Chaseable. Nikhil Bansal, Martin Bohm, Marek Elias, Grigorios Koumoutsos, Seeun William Umboh. SODA 2018.
Weighted k-server bounds via combinatorial dichotomies. Nikhil Bansal, Marek Elias, Greg Koumoutsos. FOCS 2017.
Faster Space-Efficient Algorithms for Subset Sum, k-Sum and Related Problems. Nikhil Bansal, Shashwat Garg, Jesper Nederlof, Nikhil Vyas. STOC 2017.
Algorithmic discrepancy beyond partial coloring. Nikhil Bansal, Shashwat Garg, STOC 2017.
Robust Algorithms for Noisy Minor-Free and Bounded Treewidth Graphs. Nikhil Bansal, Daniel Reichman, Seeun William Umboh. SODA 2017.
New Bounds for the (h, k)-Server Problem. Nikhil Bansal, Marek Elias, Lukasz Jez, Greg Koumousos. SODA 2017
Algorithm for Komlos Conjecture: Matching Banaszczyk’s bound. Nikhil Bansal, Daniel Dadush and Shashwat Garg. FOCS 2016. Invited to FOCS special issue.
Lift and Round to improve weighted completion time on unrelated machines. Nikhil Bansal, Aravind Srinivasan and Ola Svensson. STOC 2016. Invited to STOC special issue.
Approximation-Friendly Discrepancy Rounding. Nikhil Bansal and Viswanath Nagarajan. IPCO 2016.
Improved Approximation for Vector Bin Packing. Nikhil Bansal, Marek Elias and Arindam Khan. SODA 2016.
On the Lovasz theta function for independent sets. Nikhil Bansal, Anupam Gupta and Guru Guruganesh. STOC 2015. Invited to STOC special issue.
Approximating independent set in sparse graphs. Nikhil Bansal. SODA 2015.
Minimizing flow-time on unrelated machines. Nikhil Bansal and Janardhan Kulkarni. STOC 2015.