Category: Uncategorized

NSF funds Institute for Data, Econometrics, Algorithms, and Learning

As part of the HDR TRIPODS program, NSF has funded the Institute for Data, Econometrics, Algorithms, and Learning (IDEAL).  The institute is codirected by Prof. Hartline and Prof. Vijayaraghavan with key programs being organized also by Prof. Khuller and Prof. Makarychev.  It is a collaboration between Northwestern, Toyota Technology Institute, and University of Chicago bridges faculty in CS, Economics, Statistics, Electrical Engineering, and Operations Research.   See the news release by McCormick.

Two Northwestern papers at FOCS 2017

The Northwestern CS Theory group had two papers at the 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2017), which was recently held in Berkeley, CA.

TCS Postdoc Huck Bennett had a joint paper with Alexander Golovnev (Columbia and Yahoo Research) and Noah Stephens-Davidowitz (Princeton). The paper, “On The Quantitative Hardness of CVP,” initiates the study of the fine-grained complexity of lattice problems, a study which is important to the rapidly developing field of lattice-based cryptography. As its main result, the paper shows strong hardness of the Closest Vector Problem (CVP) with certain parameters assuming the Strong Exponential Time Hypothesis (SETH).

TCS Prof. Aravindan Vijayaraghavan had a joint paper with Oded Regev
(NYU). The paper, “Learning Mixtures of Well-Separated Gaussians,”
studies the classic problem of learning a mixture of k spherical
Gaussian distributions. The paper tries to characterize the
minimum amount of separation needed between the components to
estimate the parameters (means) of the Gaussians, and presents lower
bounds and upper bounds towards this end.



Konstantin Makarychev joins Northwestern CS Theory Group!

makarychev-konstantinThe Computer Science Division at Northwestern University welcomes new faculty member Dr. Konstantin (Kostya) Makarychev as an Associate Professor, beginning immediately. Dr. Makarychev’s position is one of the ten new faculty lines in CS which were announced in June 2016.

Dr. Makarychev is a theoretical computer scientist working on approximation algorithms, beyond worst-case analysis, applications of high-dimension geometry to computer science, and combinatorial optimization for designing efficient algorithms for computationally hard problems.

Dr. Makarychev joins Northwestern from Microsoft Research in Redmond, WA (2012-2016) and IBM Research Labs in Yorktown Heights, NY (2007-2012). Further details of his background can be found on his personal webpage.

Please click here for details, and the announcement on Northwestern homepage.

“Teaching” Postdocs

The EECS Department has announced multiple postdoctoral fellowships in Computer Science.  These fellowships come with a mix of teaching and research responsibilities and a ideal for candidates who wish to strengthen both their teaching and research experience before going on the academic job market.  Successful candidates will teach one course per term and conduct independent research, collaborating as is most effective, with current Northwestern faculty and students.

One of the priority areas for these positions is algorithms.  The teaching component of this position would be the undergraduate algorithms or discrete math courses and an advanced elective in the fellow’s research area.

Postdoc Openings

The Northwestern Theory group seeks applications for 1-2 postdoctoral positions starting in September 2017. Applicants should be recent Ph.D.’s with interest in theoretical computer science. Research areas include but are not limited to algorithms, computational complexity, theoretical machine learning and optimization.  The postdoc will also be able to take advantage of the strong theory presence in the Chicago area overall. 
Applications will be accepted until the position is filled. However, applications need to be submitted by Jan 1st, 2017 to receive full consideration. Please see for details.

Prof. De’s research at FOCS 2016

TCS Prof. Anindya De had a joint paper with Michael Saks (Rutgers) and Sijian Tang (Rutgers) in 57th Annual IEEE Symposium on Foundations of Computer Science (FOCS 16). The paper “Noisy population recovery in polynomial time” addresses the problem of recovering an unknown distribution on binary strings under noise. This problem is related to well-studied problems in learning such as learning mixtures of spherical Gaussians and product distributions. A manuscript of the paper can be found here.




Abhratanu Dutta and Yiding Feng finish 1st in ACM-ICPC Midcentral Regional

Theory PhD students Abhratanu Dutta and Yiding Feng along with their teammate Ruohong Zhang finished first at the ACM-ICPC Midcentral Regional Programming Contest this year. They finished in 1st place out of 156 teams representing 56 different schools in total and have advanced to the ACM-ICPC World Finals in Rapid City, South Dakota from May 20-25, 2017.

The ACM-ICPC (Association for Computing Machinery – International Collegiate Programming Contest) is a multi-tier, team-based, programming competition. Headquartered at Baylor University, Texas, it operates according to the rules and regulations formulated by the ACM. The contest participants come from over 2,000 universities that are spread across 80 countries and six continents.

Details can be found here.

Eight at EC and GAMES

The 2016 ACM Conference on Economics and Computation and the World Congress of the Game Theory Society (GAMES 2016) were colocated at Maastricht University in the medieval town of Maastricht, Netherlands in late July.  The Northwestern theory group had an especially strong showing with seven papers presented and a public lecture!  Brief summaries are given below.


Ph.D. student Manolis Pountourakis presented two papers.  At GAMES, he presented his paper Optimal Auctions vs. Anonymous Pricing (joint with with coauthors Saeed Alaei, Northwestern Prof. Jason HartlineRad Niazadeh, and Yang Yuan).  The paper proves that eBay’s Auctions and Buy-It-Now (posted pricings) are within at least an e = 2.718 factor of the revenue optimal auction (which may be asymmetric and very complicated). This is a worst-case bound that is quite difficult to prove; in practice the eBay’s sales mechanisms likely do much better.  This paper previously appeared in FOCS 2015 and with this video of the talk.  At EC, he presented Procrastination with Variable Present Bias (joint with coauthors Nick Gravin, Nicole Immorlica, and Brendan Lucier).  Individuals attempting to complete a task often procrastinate, making plans and then failing to follow through. One well-known model of such behavioral anomalies is present-bias discounting: individuals over-weight present costs by a bias factor. This paper introduces a variant of this model where the bias factor varies across time, identifies a connection between the planning problem with variable present bias and optimal pricing, and uses this connection to bound the cost of procrastination.

Nima Haghpanah, a former Ph.D. student and now faculty member in the Economics Department at Penn State, presented two papers.  At GAMES he presented Multi-dimensional Virtual Values and Second-degree Price Discrimination (joint with Northwestern Prof. Jason Hartline).  This paper studies multi-dimensional mechanism design.  It develops a method for solving for multi-dimensional virtual values, the pointwise optimization of which gives the optimal mechanism.  It uses these virtual values to characterize the family of distributions where second-degree price discrimination (e.g., with a high- and low-quality version of a product) is not profitable.  At EC he presented  Sequential Mechanisms with ex-post Participation Guarantees (joint with Profs. Itai Ashlagi of Stanford and Constantinos Daskalakis).  The paper identifies optimal and approximately optimal mechanisms in dynamic setting in which a seller and a buyer interact repeatedly.  In contrast to standard settings, in this paper the outcomes of a mechanism must be acceptable to the buyer even after all uncertainty is resolved.

At the second Workshop on Algorithmic Game Theory and Data Science (a part of the EC program), Ph.D. student Sam Taggart presented Non-revelation Mechanism Design (joint with Northwestern Prof. Jason Hartline).  This paper shows how to better design Bayes-Nash mechanisms, e.g., with winner-pays-bid or all-pay semantics, in a repeated environment by “linking decisions” across the repetitions.  The designed mechanisms are asymptotically optimal with the number of repetitions that are linked.

At EC, Prof. Jason Hartline presented A/B Testing of Auctions (joint with Shuchi Chawla and Denis Nekipelov).  This paper shows how the counter factual revenue of a mechanism B can be estimated from the equilibrium bids in a mechanism C which is the convex combination of mechanism A and B.  For example, an auctioneer with multiple units can determine whether selling one unit or two units gives higher revenue by running the auction that probabilistically sells one or two units.  The methods applies generally to position auctions to both winner-pays-bid and all-pay semantics.

Adjunct Northwestern Prof. Nicole Immorlica presented two papers.  At the Workshop on Economics of Cloud Computing she presented On-demand or Spot? Selling the Cloud to Risk-averse Customers (joint with former Ph.D. student Darrell Hoy and Nikhil Devanur). In Amazon EC2, cloud resources are sold through a combination of the on-demand market, in which customers buy resources at a fixed price, and the spot market, in which customers bid for an uncertain supply of excess resources. This paper showed that such a dual market system improves upon key objectives when customers have heterogeneous risk attitudes: the welfare and revenue of its unique equilibrium is larger than that of spot markets alone and the efficiency is larger than only on-demand markets.


A slide from “Market Design without Money”

At a GAMES affiliated evening soirée with the Brightlands Young Professionals network Prof. Immorlica gave a Roy-Lichtenstein-themed public lecture entitled “Market Design without Money”.  As a market-designer, a primary goal is to allocate resources to individuals in a way that maximizes the total value of the community for the allocation.  Selling the resources is a great way to achieve this: if someone is willing to pay a high price for something, then they must have a high value for it as well.  However, in many settings of interest, payments are infeasible.  This could be due to repugnance, as in the assignment of public schools to children or cadaver kidneys to patients.  Or perhaps the market technologies can not support financial transactions, e.g., users of an online review system like TripAdvisor may not have bank accounts linked to their user accounts.  This talk explored using alternative incentives including risk, waiting time, and social status, in place of money to help optimize markets in such scenarios.

Great work to the presenters and coauthors of these papers!

Sam Taggart wins Best TA award for 2016!

13350393_10153594297472826_6314011487591609603_oSam Taggart won the 2016 Northwestern EECS Best TA award for his work TAing EECS 336: Design and Analysis of Algorithms this Fall. The class tripped in size over the last couple years and Sam taught all 150 students in six discussion sessions. Even with this overwhelming workload he received incredibly high scores on the teaching evaluations with rave comments. Great work Sam!!