Professor, Department of
Mathematics and Computer Science

Eindhoven University of Technology.

I also work at CWI, Amsterdam in
the Networks
and Optimization Group

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 First-Order Methods. Nikhil Bansal, Anupam
Gupta.

Algorithmic Aspects of Discrepancy. Chapter in the book Panorama of Discrepancy Theory.

**Current Advisees:**

Phd: Marek Elias, Shashwat Garg,
Greg Koumoutsous,

Postdoc:** **Laszlo Kozma, Thijs Laarhoeven

**Recent Teaching:**

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:** Hierarchies Reading Group,
Eindhoven Discrete Math Seminar

**Service:
**Editorial Boards: Journal of the ACM, SIAM Journal on Computing,
Mathematics of Operations Research

Recent Program Committees: 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

Workshop Organization:

6^{th}
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

5^{th} 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

4^{th}
SDP Days, March 2013, CWI Amsterdam

**Selected Recent Publications:**

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.

Full list: (DBLP, Google Scholar)