Sheldon Jacobson is a Professor in the Department of Computer Science at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He holds cross-appointments in Electrical and Computer Engineering, Industrial and Enterprise Systems Engineering, Biomedical and Translational Sciences, Mathematics, and Statistics. His research spans operations research, optimization, probability, and applied mathematics, with applications in political redistricting, homeland security, stochastic processes, and societal problem-solving. Key focus areas include developing algorithmic frameworks for electoral fairness, optimizing complex systems, and advancing computational solutions for public policy challenges. Recent publications demonstrate strong emphasis on redistricting algorithms, optimization methodologies, and computational social science, with consistent applications in political systems and security domains.
Karel A. Kroeze is a Researcher at the Behavioural Data Science Institute (BDSI) at the University of Twente, specializing in Instructional Technology. With an h-index of 52, he has established himself as a significant contributor in the fields of adaptive learning systems, educational data mining, and psychometrics. His work bridges computer science, statistics, and educational theory to develop innovative assessment and feedback mechanisms. His educational background includes a Master's degree in Methodology and Statistics for the Behavioural, Biomedical and Social Sciences from Utrecht University (2015) and a Bachelor's degree in European Studies from the University of Twente (2013). This interdisciplinary foundation supports his current research in complex data analysis for educational applications. Kroeze's research focuses on developing adaptive systems for learning environments, particularly in inquiry-based education. His work on automated hypothesis assessment, concept mapping, and computerized adaptive testing demonstrates his commitment to improving educational outcomes through data-driven approaches. He explores how adaptive scaffolds, learner models, and automated feedback can enhance the quality of student inquiry and hypothesis formation in science education. His publication record shows consistent output from 2014 through 2024, with recent work expanding into health economic modeling validation and electoral system analysis. This demonstrates both depth in his core educational technology domain and breadth across applied statistical methods. As evidenced by his numerous datasets on GitHub related to adaptive hypothesis grammars across multiple domains (Electrical Circuits, Supply and Demand, Buoyancy, Photosynthesis, and Heat transfer), Kroeze develops practical tools that parse and assess student hypotheses in various scientific contexts. His research contributes to several UN Sustainable Development Goals, particularly in the area of quality education.
Oliver Rittmann serves as a Lorenz von Stein Research Fellow at the Mannheim Centre for European Social Sciences (MZES), University of Mannheim. His research integrates computational analysis of audio, image, and video data to address core political science questions in political representation, inequality, and communication. Rittmann pioneers the application of computer vision in legislative studies, notably through his current project analyzing gender-based attention disparities in the Baden-Württemberg state parliament. Using AI-driven video analysis of plenary sessions, he demonstrates that female members receive systematically less attention than male counterparts—a pattern driven exclusively by male MPs' behavior. His methodology transforms unstructured audiovisual data into quantifiable political behavior metrics, bridging longstanding gaps between social theory and empirical evidence. His publication record (2020-2025) reveals three thematic pillars: audiovisual analysis of parliamentary behavior (body language, vocal pitch, attention patterns), statistical methodology for regression interpretation, and electoral system dynamics . These works consistently advance data science applications in political science, particularly through novel computational frameworks for previously unstructured data sources. Scientific Awards Lorenz von Stein Research Fellowship Rittmann leads interdisciplinary collaborations between political scientists and computer scientists, as evidenced by his parliamentary attention project. His research demonstrates how data science enables large-scale behavioral analysis impossible through traditional methods—processing thousands of speeches rather than selective case studies. Future work will likely expand audiovisual analysis to other legislative contexts while refining gender inequality metrics.
Evan T.R. Rosenman is an Assistant Professor of Statistics at Claremont McKenna College’s Mathematical Sciences Department. Previously, he was a Postdoctoral Fellow at Harvard’s Data Science Initiative, affiliated with the Department of Statistics and the Institute for Quantitative Social Sciences. He earned his Ph.D. in Statistics from Stanford University in 2020, advised by Art Owen and Mike Baiocchi. His research focuses on causal inference, particularly hybridizing observational and experimental data to estimate causal effects, with applications in political science, public health, and gender-based violence prevention. His academic journey includes teaching roles as an instructor for STATS/CME 195 (R programming) at Stanford and as a teaching assistant for courses in statistical learning, linear models, and machine learning. He has contributed to open-source projects like the wru package for Bayesian racial category prediction using surnames and geolocation. Rosenman’s work spans methodological advancements in combining datasets, robust experimental design, and addressing census data challenges. His publications address topics such as recalibrating predictive probabilities, race imputation via Bayesian methods, and mitigating bias in sensitive topic trials. Collaborations include studies on sexual assault prevention in Kenyan informal settlements and policy evaluations related to the U.S. Census’s differential privacy system. Though no specific grants or awards are listed, his research demonstrates significant contributions to causal inference and applied statistics, with a focus on societal impact in health and policy domains. His work often bridges theoretical statistics and real-world applications, emphasizing interdisciplinary relevance.
Cory McCartan is the Hoben and Patricia Thomas and Thomas and Ann Hettmansperger Early Career Professor of Statistics at Penn State University, with an affiliate appointment in Political Science. His research focuses on methodological and applied problems in computational social science, including redistricting, Bayesian methods, causal inference, and racial disparities. He co-leads the Algorithm-Assisted Redistricting Methodology (ALARM) Project at Harvard University and develops open-source software for redistricting analysis (e.g., redist , birdie ). McCartan holds a Ph.D. in Statistics from Harvard (2023) and a B.A. in Mathematics from Grinnell College (2019). His work bridges statistical methodology and policy applications, with recent emphasis on legislative redistricting reforms, gerrymandering analysis, and privacy-preserving census data methods. Key projects include evaluating redistricting algorithms, quantifying partisan bias, and estimating disparities in administrative data using Bayesian models. Publications span topics like redistricting simulation, census privacy impacts, and neighborhood modeling. His ALARM Project produces 50-state redistricting simulations for public use, emphasizing reproducible workflows and open-source tools. McCartan's software packages ( e.g. , redist , conformalbayes ) are widely adopted in policy and academic research. Grants and collaborations involve the Voting and Election Science Team and Harvard Data Science Initiative, with computational support from Microsoft. His work has been covered in Science Advances , Proceedings of the National Academy of Sciences , and Science .
Robert F. Kelly is a Professor and Undergraduate Program Director for the Information Systems (ISE) program in the Department of Computer Science at Stony Brook University. He holds a Ph.D. in Computer Science from New York University's Courant Institute of Mathematical Sciences (1991). His research focuses on computational problems in political informatics, particularly congressional redistricting analysis, involving interdisciplinary work in computer science, applied mathematics, and political science. Key projects include the Automated Redistricting System (ARS), which optimizes districting plans using algorithms for graph partitioning, compactness measurement, and political fairness analysis. Teaching responsibilities include courses such as CSE336 (Internet Programming), CSE308/416 (Software Engineering), CSE333 (User Interface Development), and ISE courses in programming, computer organization, and security. He actively involves students through the VIP PoliTech program and undergraduate/graduate research initiatives. Notable awards include the 2002 Computer Science Undergraduate Teaching Award, Long Island Software Network's Best Internet Software (2003), and SUNY Research Foundation Promising Inventor Award (2005). His work in medical informatics also led to licensed software for anesthesia record-keeping.
Ari Stern is a Professor of Mathematics at Washington University in St. Louis , specializing in Geometric Numerical Analysis . His work bridges geometry, applied analysis, and computational mathematics, focusing on numerical methods that maintain global accuracy for differential equations through modern geometric principles. He earned his B.A. and M.A. in Mathematics from Columbia University and a Ph.D. in Applied and Computational Mathematics from Caltech (2009), advised by Jerrold E. Marsden and Mathieu Desbrun. Prior to WashU (2012), he was a postdoc at UCSD with Michael Holst. Research Interests : Geometric integration, finite element exterior calculus, symplectic geometry, and applications to physics and machine learning. His recent publications address multisymplecticity, functional equivariance, and hybrid finite element methods. Collaborations span topics from Alzheimer’s disease modeling via machine learning to Hamiltonian mechanics and geometric electrodynamics. Awards : NSF Grant (2019). Teaching : Courses include Numerical Methods for Differential Equations, Measure Theory, and Honors Mathematics.
Jason Wittenberg is a Professor in the Department of Political Science at the University of California, Berkeley. His work focuses on Eastern Europe and the post-Soviet region, with expertise in quantitative analysis, religion and politics, electoral analysis, and ethnic conflict. He holds a Ph.D. from MIT, an M.A. from The American University, and a B.A. in Physics from UC Berkeley. His research explores historical legacies of communism, political continuity, and the dynamics of ethnic violence, particularly in the context of the Holocaust and post-communist transitions. Wittenberg's notable publications include *Intimate Violence: Anti-Jewish Pogroms on the Eve of the Holocaust* (2018), which received the Bronislaw Malinowski Award, and *Crucibles of Political Loyalty: Church Institutions and Electoral Continuity in Hungary* (2006), honored with the Hubert Morken Award. His interdisciplinary work bridges historical analysis with quantitative methods, addressing topics like democratic decline, authoritarianism, and electoral behavior in Eastern Europe. He teaches courses on Eastern European politics, quantitative methods, and authoritarianism. His awards reflect contributions to understanding historical-political linkages and methodologies in political science. Wittenberg’s research often intersects with contemporary political challenges, including the erosion of democracy in Hungary and Poland, and the legacies of communist regimes.
Ewan Davies is an Assistant Professor in the Department of Computer Science at Colorado State University, where he conducts research at the intersection of combinatorics, theoretical computer science, and statistical physics. He has previously held positions as a Postdoctoral Researcher at CU Boulder, a Research Fellow at the Simons Institute, and a Postdoc at the University of Amsterdam. His educational background includes a Ph.D. in Mathematics from the London School of Economics and Political Science (2013–2017), an M.Math from the University of Cambridge (2012–2013), and a B.A. (Hons) in Mathematics from the University of Cambridge (2009–2012). Davies’s research focuses on probabilistic and extremal combinatorics, particularly in graph coloring, independent sets, partition functions, and spin models such as the hard-core and Potts models. He employs techniques from statistical physics, entropy compression, and the Lovász local lemma to develop algorithmic frameworks for coloring and counting problems. His work often bridges theoretical insights with practical algorithmic implications, especially in the context of approximate counting and sampling. The trends in his recent publications reveal a sustained focus on algorithmic graph theory, with major contributions to list packing, local graph degeneracy, and computational thresholds in spin systems. His work frequently appears in top-tier venues such as FOCS, STOC, ICALP, and journals like Random Structures & Algorithms and SIAM Journal on Computing . He has co-authored numerous papers with leading researchers in the field, including Ross J. Kang, Will Perkins, and Alexandra Kolla. Sampling and Optimization under Global Constraints, NSF Grant #2309707 PhD Prize for Outstanding Academic Performance, London School of Economics Mathematics Department New Teacher Prize, London School of Economics Foundation Scholarship and R.A. Watchman Prize, Jesus College, Cambridge Foundation Scholarship and Sir Harold Spencer Jones Prize, Jesus College, Cambridge Foundation Scholarship and Ware Prize, Jesus College, Cambridge Foundation Exhibition and Bronowski Prize, Jesus College, Cambridge Davies actively mentors students, having supervised multiple undergraduate and graduate research projects in areas such as redistricting and graph coloring. He is also involved in organizing academic workshops, including the upcoming Rocky Mountain Summer Workshop on Algorithms, Probability, and Combinatorics. His research is supported by the National Science Foundation, and he continues to contribute to both theoretical advances and interdisciplinary applications in computer science and mathematics. He leads research on graph structure via local occupancy, regularity inheritance in hypergraphs, and the algorithmic analysis of phase transitions in statistical mechanical models. His work on the occupancy fraction and fractional coloring has provided new insights into longstanding conjectures in graph theory.
Ellen Veomett is an Associate Professor in the Department of Computer Science at the University of San Francisco. Her research focuses on mathematical and computational approaches to detecting gerrymandering, with significant contributions to the development of the GEO metric for evaluating redistricting maps. She has held roles at Saint Mary's College of California, serving as Chair of the Mathematics and Computer Science department and a Professor. Dr. Veomett earned a PhD in Mathematics from the University of Michigan (2007) and a BS in Mathematics from the University of Nebraska-Lincoln (2002). Her research explores the intersection of discrete geometry, graph theory, and political science, emphasizing the use of geometric and algorithmic methods to address issues of fairness in redistricting. Notable contributions include co-designing the GEO metric, which analyzes geographic and election data to identify potentially gerrymandered maps. She has also contributed to interdisciplinary projects like the 'Caminos a Las Ciencias' initiative funded by the US Department of Education, aimed at supporting underrepresented students in STEM fields. Dr. Veomett's work has been published in venues like the Election Law Journal, American Mathematical Monthly, and Simons Institute research programs. Her awards include the 2020 Saint Mary's College Outstanding Scholar Award. She serves on the editorial board of Math Horizons and participated in the Simons Laufer Mathematical Sciences Institute's Algorithms, Fairness, and Equity program. Her research highlights the societal impact of mathematical methodologies in addressing contemporary political challenges.
Dr. Michael P. McDonald is a Professor of Political Science at the University of Florida. He holds a Ph.D. from the University of California, San Diego, and a B.S. in Economics from the California Institute of Technology. Prior to his current position, he taught at George Mason University, Vanderbilt University, and the University of Illinois, Springfield, and completed a post-doctoral fellowship at Harvard University. His research focuses on elections and methodology, particularly voter turnout dynamics and redistricting processes. He co-leads the Public Mapping Project, a initiative promoting public participation in redistricting through technology. McDonald has authored/co-authored influential works such as Numerical Issues in Statistical Computing for the Social Scientist and edited volumes like The Marketplace of Democracy . Practically, he has consulted for the U.S. Election Assistance Commission, Pew Center for the States, and served as an expert witness in election-related litigation across multiple states. His media engagements include advising ABC and NBC, with frequent commentary in outlets like The Washington Post and Politico . McDonald’s work spans academic research, policy advocacy, and public engagement, emphasizing transparency in electoral processes and innovative solutions to improve democratic participation.
Anthony McGann is Professor of Politics at the School of Government and Public Policy, University of Strathclyde, with affiliations at the University of California, Irvine's Institute for Mathematical Behavioral Sciences and Center for the Study of Democracy. His research examines democratic institutions, electoral systems, and policy dynamics, with focus on gerrymandering, proportional representation, and voter behavior. Education includes: PhD in Political Science, Duke University (1999) MA, Duke University (1993) BA in Philosophy, Politics and Economics, University College, Oxford (1989) Research explores how institutional designs like electoral rules and district boundaries shape policy outcomes and political representation. Publications analyze comparative legislative behavior, public opinion-policy linkages, and constitutional constraints on redistricting, emphasizing quantitative approaches to political dominance and intra-party competition. Current projects investigate partisan polarization effects on federalism and electoral reform. Supervises doctoral candidates and serves as External Examiner at University of Essex since 2021.
Matthew Kahle is a Professor in the Department of Mathematics at The Ohio State University, where he has served since 2011. His research focuses on the interplay between topology, geometry, probability, and combinatorics, with applications to statistical physics and geometric group theory. He holds a PhD from the University of Washington (2007) and has held positions at Stanford University and the Institute for Advanced Study. Kahle advises multiple PhD students and has mentored numerous postdocs. His honors include the Simons Fellowship, NSF CAREER Award, and Sloan Research Fellowship. Education: PhD (2007) in Mathematics from the University of Washington; MS (2001) in Mathematics from Colorado State University. Earlier studies include a BS from the University of Chicago. Research interests emphasize stochastic topology, configuration spaces, and random simplicial complexes. He explores how probabilistic methods reveal structural properties in geometric and combinatorial systems. His work on persistent homology and topological phase transitions has influenced computational topology and statistical mechanics. Key awards include the 2019 Mercator Fellowship, AMS Fellowship, and Simons Fellowship. His Erdős number is 2, reflecting collaborations in combinatorics and graph theory. Kahle has also engaged in visiting roles at institutions worldwide, including TU Berlin and Queen Mary University of London. Advising spans over a dozen PhD students and postdocs, contributing to their success in academic and applied mathematics. His grants include NSF funding for research on topological phases and configuration space topology. Collaborative efforts with international researchers highlight his global impact in mathematical science. Labs/Teams: Kahle leads a research group focused on algebraic topology and its applications, collaborating with postdocs and students on projects ranging from random geometric complexes to topological data analysis.
Malavika Mukundan is a Research Fellow and Postdoctoral Associate in the Department of Mathematics and Statistics at Boston University. Previously, she held a PPFP Postdoctoral Fellowship at UCLA and completed her PhD at the University of Michigan (2024) under Sarah Koch. She earned her undergraduate degree from the Chennai Mathematical Institute in India. Her research focuses on holomorphic dynamics, Teichmüller theory, and algorithmic fairness in voting systems like ranked choice and Borda. She co-organizes the BU Dynamical Systems seminar and contributes to interdisciplinary projects in social choice theory. Education: PhD in Mathematics, University of Michigan, 2024 Bachelor’s Degree, Chennai Mathematical Institute, India Research Interests: Her work bridges complex dynamics and social choice, with recent emphasis on transcendental Thurston theory and geometric fairness in voting systems. She employs tools from topology, complex analysis, and computational methods to study dynamical systems and voting mechanisms. Current projects include approximating transcendental maps via rational dynamics and analyzing random ranked ballots. Recent Trends in Articles: Her publications span complex dynamics (e.g., transcendental maps, Thurston pullbacks) and social choice (e.g., VoteKit software for voting analysis). Recent work explores connections between geometric structures in dynamical systems and fair voting algorithms. Awards: PPFP Postdoctoral Fellowship, UCLA Lauter Program Associate, MSRI Advising & Grants: Mentors in the BIG Ideas in Dynamics program and contributed to the MGGG Redistricting Lab (Tufts). Her work is supported by fellowships and institutional grants. Labs/Teams: Collaborates with Ryan Goh, Montie Avery, and the BU Dynamical Systems group. Active in interdisciplinary initiatives like the MSRI Algorithms, Fairness, and Equity program.
Ian G. Ludden is an Assistant Professor of Computer Science and Software Engineering at Rose-Hulman Institute of Technology, located in Terre Haute, IN. His academic journey includes a B.S. in Computer Engineering and Mathematics from Rose-Hulman (2013-2016) and a Ph.D. in Computer Science (Theory and Algorithms) from the University of Illinois Urbana-Champaign (2017-2023). He joined Rose-Hulman as faculty in 2023. Research Focus: Ludden's work centers on Combinatorial Optimization, Algorithmic Game Theory, and Graph Theory applications in Health Care and Sports Analytics. His recent projects emphasize political redistricting optimization frameworks, including algorithmic fairness and compromise mechanisms in districting processes. He has developed models for NCAA March Madness bracket analysis and x-ray image processing. Key Projects: Graph partitioning for redistricting games, NCAA tournament prediction systems, and health analytics. Tools: Python, GitHub repositories (e.g., power-model-ncaa ). Awards: NSF Graduate Research Fellowship (GRFP) Illinois CS Outstanding Teaching Assistant — Lifetime Professional Contributions: Active GitHub contributions since 2019, including open-source projects like bracket analytics and machine learning for medical imaging. Collaborates with institutions on redistricting optimization frameworks and sports probability models.
