Nikhil Bansal

Researcher at CWI, Amsterdam in the  Networks and Optimization Group
Professor, Department of Math and CS, Eindhoven University of Technology.   

Contact information  Brief Bio  and CV

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.

Some Surveys :
Potential-Function Proofs for Gradient 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:

PhdMarek Elias, Shashwat Garg, Greg Koumoutsous, Bart Kamphort (joint), Jorn van der Pol (joint)
Postdoc: Christian Coester, Ilan Cohen, Jatin Batra, Makrand Sinha (joint), Laszlo Kozma, Thijs Laarhoeven, Lukasz Jez, William Seeun Umboh


Recent Teaching:

Notes for IPCO summer school (lec1, lec2, slides, exercises)

Randomized Algorithms (Sp 2021).

Algorithms beyond worst case (Sp 2018). Some lecture notes. 

Advanced Semidefinite Programming (Sp 2017)
Algorithms and Uncertainty: Fall 2016  (at UC Berkeley)

Graphs and Algorithms (2MMD30)
Approximation Algorithms (2WO07)
Freshman Linear Algebra

Seminars: CWI N&O seminarHierarchies Reading Group,  Eindhoven Discrete Math Seminar

Editorial Boards: Journal of the ACM, Theory of Computing, Stochastic Models.
Previously SICOMP (2012-2018), SIDMA (2012-2018), Math of Operations Research (2013-2020)

Recent Program Committees: ICALP 2021 (chair), ITCS 2020, Approx 2020, 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 2014FOCS 2014, ICALP 2014 


Workshop Organization:

STOC 2020 Workshop on Recent advanced in discrepancy and applications
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, Germany
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:

k-Forrelation Optimally Separates Quantum and Classical Query Complexity. Nikhil Bansal, Makrand Sinha, ECCC.
Scale-free Allocation, amortized Convexity and Myopic Paging. Nikhil Bansal, Christian Coester, Ravi Kumar, Manish Purohit, Erik Vee. Arxiv
Online Discrepancy Minimization for Stochastic Arrivals. Nikhil Bansal. SODA 2021.
Improved Approximations for Min Sum Vertex Cover and Generalized Min Sum Set Cover. Nikhil Bansal, Jatin BatraMajid FarhadiPrasad Tetali. SODA 2021
Geometry of scheduling on multiple machines. Nikhil Bansal, Jatin Batra. SODA 2021.
Online vector balancing and geometric discrepancy. Nikhil Bansal, Haotian Jiang, Janardhan Kulkarni, Sahil Singla. STOC 2020.
On-line balancing of random inputs. Nikhil Bansal, Joel Spencer. Random Structures and Algorithms, to appear.
Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems. Sepehr Abbasi Zadeh, Nikhil Bansal, Guru GuruganeshAleksandar NikolovRoy SchwartzMohit Singh. SODA 2020.
Potential-Function Proofs for Gradient Methods. Nikhil Bansal, Anupam Gupta. Theory of Computing 2019.
New notions and constructions of sparsification for graphs and hypergraphs. Nikhil Bansal, Ola Svensson, Luca Trevisan. FOCS 2019.
On a generalization of iterated and randomized rounding. Nikhil Bansal. STOC 2019.

On the discrepancy of random low degree set systems. Nikhil Bansal, Raghu MekaSODA 2019.

Achievable performance of blind policies in heavy traffic. Nikhil Bansal, Bart Kamphorst, Bert Zwart. Math of OR 43(3), 949-964, 2018.
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 ReichmanSeeun 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.


Full list: (DBLP, Google Scholar)