Monday, October 16, 2017

Call for Nominations for the SIGecom Doctoral Dissertation Award

The SIGecom Doctoral Dissertation Award recognizes an outstanding dissertation in the field of economics and computer science. The award is conferred annually at the ACM Conference on Economics and Computation and includes a plaque, complimentary conference registration, and an honorarium of $1,500. A plaque may further be given to up to two runners-up. No award may be conferred if the nominations are judged not to meet the standards for the award.

To be eligible, a dissertation must be on a topic related to the field of economics and computer science and must have been defended successfully during the calendar year preceding the year of the award presentation.

The next SIGecom Doctoral Dissertation Award will be given for dissertations defended in 2017. Nominations are due by the March 31, 2018, and must be submitted by email with the subject "SIGecom Doctoral Dissertation Award" to the awards committee at A dissertation may be nominated simultaneously for both the SIGecom Doctoral Dissertation Award and the ACM Doctoral Dissertation Award.

Nominations may be made by any member of SIGecom, and will typically come from the dissertation supervisor. Self-nomination is not allowed. Nominations for the award must include the following, preferably in a single PDF file:

1. A two-page summary of the dissertation, written by the nominee, including bibliographic data and links to publicly accessible versions of published papers based primarily on the dissertation.
2. An English-language version of the dissertation.
3. An endorsement letter of no more than two pages by the nominator, arguing the merit of the dissertation, potential impact, and justification of the nomination. This document should also certify the dissertation defense date.
4. The names, email addresses, and affiliations of at least two additional endorsers.

The additional endorsement letters themselves should be emailed directly to, by the same deadline. These endorsements should be no longer than 500 words, and should specify the relationship of the endorser to the nominee, contributions of the dissertation, and its potential impact on the field.

It is expected that a nominated candidate, if selected for the award, will attend the next ACM Conference on Economics and Computation to accept the award and give a presentation on the dissertation work. The cost of attending the conference is not covered by the award, but complimentary registration is provided.

Sunday, July 16, 2017

Submit your papers to WWW 2018

Jennifer Wortman Vaughan and I are the track chairs for the the "Web Economics, Monetization, and Online Markets" track of WWW 2018. The track name is a little unwieldy -- we tried to change it to "Economics and Markets" -- but the focus should be of interest to many in the AGT, theory, and machine learning communities. See the call for papers here: We have a great PC, and the topics of interests include (amongst many other things), "Economics of Privacy" and "Fairness in Economic Environments".

Friday, November 25, 2016

January Fairness Workshop at Penn

At the beginning of the semester, I mentioned that after the law school's semester of events (click for videos) on fairness, machine learning, and the law, we would host a technical workshop on recent work on fairness in machine learning.

We have now finished putting together the program, which will be terrific. The workshop will take place here at Penn from January 19-20th. Take a look at our great line-up of speakers here:

The event is open to the public, but registration is required.

Monday, October 24, 2016

Designing the Digital Economy

I'm on a train to New Haven, where I'll be giving a guest lecture (together with Solon Barocas) in Glen Weyl's class, "Designing the Digital Economy" (n.b. I need to get advice from Glen about how to get as good publicity for my classes...)

Solon and I will be sharing the 3 hour class, talking about fairness in machine learning, starting at 2:30. Pop by if you are around -- otherwise, here are my slides.

Wednesday, September 14, 2016

Semester on Fairness and Algorithms at Penn

This year, the "Fels Policy Research Initiative" is funding two exciting events, both related to fairness and machine learning. The first, joint between the law school and statistics, is called "Optimizing Government", and will host a series of 4 seminars over the course of this semester touching on technical and legal aspects of fairness.

I will be speaking at the first one, introducing the basics of Machine Learning and scenarios in which its use can lead to inadvertent discrimination. My inimitable colleague Richard Berk (who actually builds models used to predict criminal recidivism used by the state of Pennsylvania) will be offering his comments following my talk.

The second seminar will provide a panel discussion on what the law demands in terms of "fair and equal treatment", and how it relates to the use of machine learning. The panelists will come from Philosophy, Political Science, and Law.

The third seminar will be given by our excellent Warren Center postdoc Jamie Morgenstern, and will focus on technical solutions to the problem of unfairness in machine learning, and how it can be squared with learning the optimal policy in online decision making settings.

Finally, the fourth seminar will be an exciting keynote delivered by the current Deputy U.S. CTO Ed Felten on uses of machine learning in government.

I believe the talks will be recorded.

We will begin next semester with the second Fels sponsored workshop, organized between computer science and economics -- a 2 day intensive workshop exploring current research on technical and economic solutions to addressing unfairness in decision making. More details to come.

Below is the schedule for this semester:

The “Optimizing Government” interdisciplinary research collaboration, supported by the Fels Policy Research Initiative, will hold the following workshops this fall:

Thursday, 9/22/16: What is Machine Learning (and Why Might it be Unfair)?Fundamentals of machine learning with a focus on what makes it different from traditional statistical analysis and why it might lead to unfair outcomes.Speakers: Aaron Roth (Penn Computer Science), with comments from Richard Berk (Wharton Statistics; Chair of SAS Criminology)

Thursday, 10/6/16: What Does Fair and Equal Treatment Demand?
Current legal and moral norms about fairness and equal protection as they relate to the use of machine learning in government.
Speakers: Panel featuring Samuel Freeman (Penn Philosophy), Nancy Hirschmann (Penn Political Science), and Seth Kreimer (Penn Law)

Thursday, 11/3/16: Fairness and Performance Trade-Offs in Machine Learning
Technical solutions to fairness challenges raised by machine learning and their impacts on algorithm effectiveness.
Speaker: Jamie Morgenstern (Penn Computer Science)

Thursday, 11/17/16: Keynote on Machine Learning and Government
How to use machine learning for a variety of administrative and policy functions, and findings from a White House initiative on artificial intelligence in government.
Speaker: Ed Felten, Deputy U.S. CTO (Invited)

Each workshop will take place from 4:30-6:00 pm in Gittis 213 (Penn Law). You can enter the Law School through its main entrance at 3501 Sansom Street.

EDIT: The Optimizing Government Project now has a website: and the talks will be livestreamed here:

Wednesday, August 24, 2016

Call for Papers: Second Workshop on Adaptive Data Analysis

As part of NIPS 2016, we will be running the second annual workshop on adaptive data analysis. Last year's workshop was a big hit. As a new addition this year, we are soliciting submitted contributions in addition to invited speakers. The call for papers is below. If you are working on relevant work, definitely submit it to our workshop! More information at:

Call for Papers

The overall goal of WADAPT is to stimulate the discussion on theoretical analysis and practical aspects of adaptive data analysis. We seek contributions from different research areas of machine learning, statistics and computer science. Submissions focused on a particular area of application are also welcome.

Submissions will undergo a lightweight review process and will be judged on originality, relevance, clarity, and the extent to which their presentation can stimulate the discussion between different communities at the workshop. Submissions may describe either novel work (completed or in progress), or work already published or submitted elsewhere provided that it first appeared after September 1, 2015.

Authors are invited to submit either a short abstract (2-4 pages) or a complete paper by Oct 15, 2016. Information about previous publication, if applicable, should appear prominently on the first page of the submission. Abstracts must be written in English and be submitted as a single PDF file at EasyChair.

All accepted abstracts will be presented at the workshop as posters and some will be selected for an oral presentation. The workshop will not have formal proceedings, and presentation at the workshop is not intended to preclude later publication at another venue.

Those who need to receive a notification before the NIPS early registration deadline (Oct 6, 2016) should submit their work by the early submission deadline of Sept 23, 2016.

Important Dates:

Submission deadlines. Early: Sep 23, 2016; Regular: Oct 25, 2016. Submit at EasyChair.
Notification of acceptance. Early: Oct 3, 2016, Regular: Nov 7, 2016.
Workshop: December 9, 2016

Specific topics of interest for the workshop include (but are not limited to):

Selective/post-selection inference
Sequential/online false discovery rate control
Algorithms for answering adaptively chosen data queries
Computational and statistical barriers to adaptive data analysis
Stability measures and their applications to generalization
Information-theoretic approaches to generalization

Saturday, June 04, 2016

Machine Learning Postdoc

My brand new colleague Shivani Agarwal is in the market for a postdoc; the announcement is below. One of the targeted areas is machine learning and economics. Whoever takes this position will join a growing group of exceptional postdocs in this area at Penn, including Jamie Morgenstern and Bo Waggoner.

Postdoctoral Position in Machine Learning at UPenn

Applications are invited for a postdoctoral position in machine learning  in the Department of Computer and Information Science at the University  of Pennsylvania. The position is expected to begin in Fall 2016, and is  for a period of up to two years (with renewal in the second year  contingent on performance in the first year). Applications in all areas  of machine learning will be considered, with special emphasis on the  following areas: ranking and choice modeling; connections between machine learning and economics; and learning of complex structures.

The ideal candidate will demonstrate both ability for independent  thinking and interest in co-mentoring of graduate students. The candidate will work primarily with Shivani Agarwal (joining UPenn faculty in July 2016), but will also have opportunities to collaborate with other faculty in machine learning and related areas at UPenn, including Michael Kearns, Daniel Lee, Sasha Rakhlin, Aaron Roth, Lyle Ungar, and other faculty.

UPenn is located in the vibrant city of Philadelphia, which is known for its rich culture, history, museums, parks, and restaurants. It is less than 1.5 hrs by train to NYC, 1.5 hrs by flight to Boston, 2 hrs by train to Washington DC, and 40 mins by train to Princeton. For more details about the CIS department at UPenn, see:

To apply, send the following materials in an email titled “Application for Postdoctoral Position” to  by June 17, 2016:

- curriculum vitae
- 2-page statement of research interests and goals
- 3 representative publications or working papers
- 3 letters of recommendation (to be sent separately by the same date)

Shortlisted candidates will be invited for a short meeting/interview at ICML/COLT in NYC during June 23-26 (in your email, please indicate your availability for this).