[Colloquium] Fwd: FW: [Omms_workshop] Operations Management Workshop David Shmoys 11/18 12:10 PM

Fri Nov 14 10:39:01 CST 2025

---------- Forwarded message ---------
From: Niazadeh, Rad <Rad.Niazadeh at chicagobooth.edu>
Date: Fri, Nov 14, 2025 at 10:25 AM
Subject: FW: [Omms_workshop] Operations Management Workshop David Shmoys
11/18 12:10 PM
To: Avrim Blum <avrim at ttic.edu>, Haifeng Xu <haifengxu at uchicago.edu>, Jason
D Hartline <hartline at northwestern.edu>
Cc: Tooley, Sidney <Sidney.Tooley at chicagobooth.edu>, Taskesen, Bahar <
Bahar.Taskesen at chicagobooth.edu>

Hi Avrim, Jason, and Haifeng,

I hope all is well.

We have David Shmoys visiting us next Tuesday and giving a seminar in our
group. I thought theory folks at TTIC, UChicago CS/DSI, and Northwestern CS
might be interested in attending his talk. Would you mind advertising it to
anyone who you think might be interested?

Cheers,

Rad

*Rad Niazadeh*

Associate Professor of Operations Management

The University of Chicago

Booth School of Business

5807 South Woodlawn Avenue, HC-303

Chicago, Illinois 60637

Website:  https://faculty.chicagobooth.edu/rad-niazadeh

Phone: 773-834-6247

Mobile: 607-379-5744

[image: signature_336178382] <https://www.chicagobooth.edu/>

*From: *Omms_workshop <omms_workshop-bounces at lists.chicagobooth.edu> on
behalf of Tooley, Sidney <Sidney.Tooley at chicagobooth.edu>
*Date: *Tuesday, November 11, 2025 at 1:54 PM
*To: *omms_workshop at lists.chicagobooth.edu <
omms_workshop at lists.chicagobooth.edu>
*Subject: *[Omms_workshop] Operations Management Workshop David Shmoys
11/18 12:10 PM

Dear All,

We are happy to continue the Fall Operations Management Seminar series on
Tuesday Nov. 18 at 12:10 p.m. in Harper C10. Lunch will be available for
attendees during the seminar.

David Shmoys from Cornell will be presenting “Algorithmic Tools for
Redistricting: Fairness via Analytics.” Here is an abstract for the
presentation.

The American winner-take-all congressional district system empowers
politicians to engineer electoral outcomes by manipulating district
boundaries. Many computational solutions focus on drawing unbiased maps by
ignoring political and demographic input, and instead simply optimize for
compactness and other related metrics. However, we maintain that this is a
flawed approach because compactness and fairness are orthogonal qualities;
to achieve a meaningful notion of fairness, one needs to model political
and demographic considerations, using historical data. We will discuss a
series of papers that explore and develop this perspective through the lens
of optimization models and algorithms. We first present a scalable approach
to explicitly optimize for arbitrary piecewise-linear definitions of
fairness; this employs a stochastic hierarchical decomposition approach to
produce an exponential number of distinct district plans that can be
optimized via a standard set partitioning integer programming formulation.
This enables a large-scale ensemble study of congressional districts,
providing insights into the range of possible expected outcomes and the
implications of this range on potential definitions of fairness.

Further work extending this shows that many additional real-world
constraints can be easily adapted in this framework (such as minimal county
splits as was recently required in Alabama legislation in response to the
US Supreme Court decision Milligan v. Alabama). In addition, one can adapt
the same framework to heuristically optimize for other fairness-related
objectives, such achieving a targeted number of majority-minority districts
(and in taking this approach, achieving stronger results than obtained by a
prominent randomized local search approach known as “short bursts”). We
also show that our optimization infrastructure facilitates the study of the
design of multi-member districts (MMDs) in which each district elects
multiple representatives, potentially through a non-winner-takes-all voting
rule (as was proposed in H.R. 4000 in an earlier session of Congress). We
carry out large-scale analyses for the U.S. House of Representatives under
MMDs with different social choice functions, under algorithmically
generated maps optimized for either partisan benefit or proportionality. We
find that with three-member districts using Single Transferable Vote,
fairness-minded independent commissions can achieve proportional outcomes
in every state (up to rounding), and this would significantly curtail the
power of advantage-seeking partisans to gerrymander. This is joint work
with Wes Gurnee, Nikhil Garg, David Rothschild, Julia Allen, Cole Gaines,
David Domanski, Rares-Stefan Bucsa, and Daniel Brous.

We look forward to seeing you on Tuesday!

All the best,

*Sidney Tooley*

Events Manager

Faculty Services
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