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Wendy Cho

University of Illinois at Urbana-Champaign

Computer and Computation Research


Wendy K. Tam Cho and Simon Rubinstein-Salzedo (2019): Understanding Significance Tests From a Non-Mixing Markov Chain for Partisan Gerrymandering Claims, Statistics and Public Policy, Taylor and Francis, Vol 6, Num 1, pp44-49
Wendy K Tam Cho (2019): Technology-Enabled Coin Flips for Judging Partisan Gerrymandering, Southern California Law Review, Southern California Law Review Postscript, Vol 93, pp11-27
Wendy K. Tam Cho and Simon Rubinstein-Salzedo (2019): Rejoinder to “Understanding our Markov Chain Significance Test", Statistics and Public Policy, Taylor and Francis, Vol 6, Num 1, pp54


Wendy K. Tam Cho (2018): Algorithms can foster a more democratic society, Nature, Springer Nature, Vol 558, pp487
Cain, Bruce E., Wendy K. Tam Cho, Yan Y. Liu, and Emily Zhang (2018): A Reasonable Bias Approach to Gerrymandering: Using Automated Plan Generation to Evaluate Redistricting Proposals, William & Mary Law School, William & Mary Law Review, Vol 59, Num 5, pp1521
Wendy K. Tam Cho and Yan Y. Liu (2018): Sampling from complicated and unknown distributions: Monte Carlo and Markov Chain Monte Carlo methods for redistricting, Physica A: Statistical Mechanics and its Applications, Elsevier BV, Vol 506, pp170-178


Yan Y. Liu, Wendy K. Tam Cho, and Shaowen Wang (2016): PEAR: A Massively Parallel Evolutionary Computation Approach for Political Redistricting Optimization and Analysis, Swarm and Evolutionary Computation, Elsevier BV, Vol 30, pp78-92
Wendy K. Tam Cho and Yan Y. Liu (2016): Toward a Talismanic Redistricting Tool: A Computational Method for Identifying Extreme Redistricting Plans, Election Law Journal: Rules, Politics, and Policy, Mary Ann Liebert Inc, Vol 15, Num 4, pp351-366
Wendy K. Tam Cho, and Yan Y. Liu (2016): A Parallel Evolutionary Algorithm for Subset Selection in Causal Inference Models, ACM Press, XSEDE16: Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale, pp7:1-7:8, Miami, Florida, U.S.A.


Yan Y. Liu, Wendy K. Tam Cho, and Shaowen Wang (2015): A Scalable Computational Approach to Political Redistricting Optimization, ACM Press, Proceedings of the 2015 XSEDE Conference on Scientific Advancements Enabled by Enhanced Cyberinfrastructure (XSEDE '15), pp6:1, St Louis, Missouri, U.S.A.


Wendy Cho, Yan Liu, Bruce Cain (2018): Enabling Redistricting Reform: A Computational Study of Zoning Optimization, 2018 Blue Waters Annual Report Summary Slide
Wendy Cho, Yan Liu, Bruce Cain (2018): Enabling Redistricting Reform: A Computational Study of Zoning Optimization, 2018 Blue Waters Annual Report, pp264-267


Wendy Cho (2017): Enabling Redistricting Reform: A Computational Study of Zoning Optimization, Blue Waters annual-book summary slide
Wendy Cho (2017): Enabling Redistricting Reform: A Computational Study of Zoning Optimization, 2017 Blue Waters Annual Report, pp254-255


Wendy Cho (2016): A Computational Model for Causal Inference via Subset Selection, 2016 Blue Waters Annual Report, pp254-255

Yan Y. Liu and W. K. T. Cho: A High-Performance Evolutionary Computation Framework for Scalable Spatial Optimization

International Conference on Computational Science (ICCS 2018); Wuxi, China., Jun 11, 2018

Wendy K. Cho and Yan Liu: Massively Parallel Evolutionary Computation for Empowering Electoral Reform: Quantifying Gerrymandering via Multi-objective Optimization and Statistical Analysis

ACM Student Research Competition at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC17); Denver, Colorado, U.S.A., Nov 14, 2017

Bruce E. Cain, W. K. Tam Cho: A Reasonable Bias Method for Redistricting: A New Tool for an Old Problem

113th American Political Science Association (APSA) Annual Meeting and Exhibition; San Francisco, California, U.S.A., Sep 1, 2017
Wendy K. Tam Cho: Enabling Redistricting Reform: A Computational Study of Zoning Optimization
Blue Waters Symposium 2017, May 18, 2017

Wendy K. Tam Cho, and Yan Y. Liu: A High-Performance Approach for Solution Space Traversal in Combinatorial Optimization

The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15); Austin, Texas, U.S.A., Nov 17, 2015

Hyperion Research Announces Winners of HPC Innovation Excellence Awards

Jun 25, 2019

Through the utilization of 131,000 processors on the Blue Waters supercomputer at NCSA, the team has created a scalable algorithm to help tackle gerrymandering and could save hundreds of millions of dollars spent in law suits and improve democratic society by supplying the missing districting information.


'Dogmeat,' laughter, and a house on an isthmus: Four surprising tidbits from Ohio's gerrymandering trial

Apr 2, 2019

The League of Women Voters and Democratic organizations filed a federal lawsuit against Ohio Republican state officials accusing them of unconstitutional "gerrymandering" in 2011 when they redrew the lines for the state's 16 congressional districts.


Seven Ways Enterprises Are Using Supercomputers to Solve Global Issues

Mar 16, 2018

Wendy K. Tam Cho's algorithm generates billions of voter district maps to measure the degree of partisanship present within any given electoral boundary and bring greater transparency to the process of redistricting.


U.S. Supreme Court Will Hear Arguments In Wisconsin Gerrymandering Case

Oct 2, 2017

Wendy Tam Cho, a University of Illinois political science professor, says this case is particularly important because it could determine the court’s role in future cases on gerrymandering.


What I Learned at Gerrymandering Summer Camp

Aug 27, 2017

It also comes in handy when you’re carving the American electorate into voting districts that favor your political party, a time-honored—and reviled—tradition known as gerrymandering. That’s what’s brought the group here to Tufts. They’re participants in a weeklong summer camp of sorts for adults focused on how math and technology can be used to make electoral maps more fair, and to convince judges and juries when they’re not. Gerrymandering, they believe, allows politicians to choose their voters, not the other way around. This event is the first of many planned by the unfortunately named Metric Geometry and Gerrymandering Group at Tufts. You can think of the hackathon as the arts and crafts part of the week—a chance for the geeks to get their hands dirty. Attendees had to apply to this session; just 14 made the cut.


UIUC’s Supercomputer Has a Projected $1B Impact On Illinois’ Economy

May 12, 2017

Nestled on the outskirts of the University of Illinois at Urbana-Champaign campus — at the corner of Oak Street and St. Mary’s Road — is Blue Waters, a supercomputer that was first instituted as a result of a 2007 National Science Foundation grant and an initial $60 million investment from the State of Illinois. A report released this past week on the economic impact of this supercomputer — on the UIUC campus, its five surrounding counties, as well as nationwide spillover effects — puts a whole new meaning to the term “return on investment.”


Can a Supercomputing Algorithm Kill Gerrymandering?

Apr 14, 2017

A supercomputing application that can figure out if state legislative districts have been unfairly drawn, has the potential to change electoral politics in the United States. According to its inventors at the University of Illinois at Urbana-Champaign, the application could be used by courts to determine if partisan gerrymandering has been used to unfairly manipulate these maps.


The algorithm that could help end partisan gerrymandering

Apr 10, 2017

We are living in the era of the computer algorithm. Data science drives the global economy — to the point where, for many people, an algorithm will play a role in everything from what news articles they read to whom they will date — or even marry. So it’s no surprise that political scientists would want to use an algorithm to improve political redistricting, a process that is often distorted by partisan maneuverings.


Supercomputers vs. gerrymandering: Data could be the next key to creating fair state voting districts

Apr 10, 2017

For nearly as long as the Unites States has existed there have been partisan hacks trying to draw up voting districts in a way that gives one political party an unfair advantage over the other. Though judges acknowledge that this partisan gerrymandering occurs, and that it can be unconstitutional, there’s hasn't yet been a definitive way for them to decide whether a district has been egregiously engineered to politically neuter voters of an opposing party. Indeed, as recently as 2004, the U.S. Supreme Court acknowledged that measuring how much political influence on redistricting is too much is an “unanswerable question.” But thanks to the power of algorithms and the latest supercomputing powers, new methods are arising that can help answer this unanswerable question. These methods played a role in November in convincing a three-judge panel to invalidate Wisconsin’s district assembly maps. The U.S. Supreme Court is expected to issue a final ruling this fall, and if the decision is upheld it would be a victory for political and social scientists, and it may finally give judges reliable methodologies to help decide if voting districts have been unfairly drawn.


How to Quantify (and Fight) Gerrymandering

Apr 4, 2017

Partisan gerrymandering — the practice of drawing voting districts to give one political party an unfair edge — is one of the few political issues that voters of all stripes find common cause in condemning. Voters should choose their elected officials, the thinking goes, rather than elected officials choosing their voters. The Supreme Court agrees, at least in theory: In 1986 it ruled that partisan gerrymandering, if extreme enough, is unconstitutional. Yet in that same ruling, the court declined to strike down two Indiana maps under consideration, even though both “used every trick in the book,” according to a paper in the University of Chicago Law Review. And in the decades since then, the court has failed to throw out a single map as an unconstitutional partisan gerrymander. “If you’re never going to declare a partisan gerrymander, what is it that’s unconstitutional?” said Wendy K. Tam Cho, a political scientist and statistician at the University of Illinois, Urbana-Champaign.


Blue Waters Illinois allocations awarded to 26 research teams

Mar 7, 2017

Twenty-six research teams at the University of Illinois at Urbana-Champaign have been allocated computation time on the National Center for Supercomputing Application's (NCSA) sustained-petascale Blue Waters supercomputer after applying in Fall 2016. These allocations range from 25,000 to 600,000 node-hours of compute time over a time span of either six months or one year. The research pursuits of these teams are incredibly diverse, ranging anywhere from physics to political science.


Wired In: Wendy Tam Cho

Oct 9, 2016

WENDY K. TAM CHO is a professor in two departments, political science and statistics, as well as a senior research scientist in the National Center for Supercomputing Applications. She is working on political redistricting in collaboration with Yan Liu. Cho won the Guggenheim Foundation fellowships last year for her research.


UIUC Profs Use Illinois Supercomputer to Take on Partisan Gerrymandering

Sep 21, 2016

Though the approval rating for Congress is at just 18 percent, the re-election rate is approximately 95 percent. Why do politicians with low approval ratings continue to get re-elected? Many point to partisan gerrymandering, drawing legislative district maps that discriminate against a political party to benefit another. Though both parties and many voters oppose the practice, courts have struggled to address gerrymandering, in part because it can be difficult to evaluate whether maps have been drawn with partisanship as the main motive. That’s where University of Illinois Urbana-Champaign researchers Wendy K. Tam Cho and Yan Liu come in. Using the Blue Waters supercomputer at Illinois’ National Center for Supercomputing Applications (NCSA), they’ve generated 800 million voter district maps that could be used as an objective way to measure the fairness of a legislative map.


Blue Waters supercomputer used to develop a standard for partisan gerrymandering, generates 800 million maps

Sep 14, 2016

University of Illinois at Urbana-Champaign researchers, Professor Wendy K. Tam Cho and Yan Y. Liu recently won 1st place in the Common Cause 2016 First Amendment Gerrymander Standard Writing Competition with their proposal of a novel method for identifying partisan gerrymandering, which they created using the Blue Waters supercomputer at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign.


17 campus teams to accelerate their research with Blue Waters

Jun 10, 2015

Seventeen U of I research teams from a wide range of disciplines have been awarded computational and data resources on the sustained-petascale Blue Waters supercomputer at NCSA. “These diverse projects highlight the breadth of computational research at the University of Illinois,” said Athol Kemball, associate professor of Astronomy and chair of the Illinois allocation review committee. “Illinois has a tremendous pool of talented researchers in fields from political science to chemistry to engineering who can harness the power of Blue Waters to discover and innovate.”