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Paul GölzView profile

Assistant Professor

• Social Choice Theory
• Algorithmic Fairness
• AI Ethics
• +3 more

Paul Gölz is an Assistant Professor at Cornell University's School of Operations Research and Information Engineering (ORIE), with affiliations in Computer Science. His research focuses on computational social choice, algorithmic fairness, and AI ethics, addressing topics like democratic innovation, fair resource allocation, and AI systems for diverse users. Education: Undergraduate studies at Saarland University (Germany), PhD in Computer Science from Carnegie Mellon University, followed by postdoctoral research at Harvard University, UC Berkeley, and the Simons Laufer Mathematical Sciences Institute. His work has been recognized with awards including the JPMorgan Chase AI Research Fellowship (2021) and honorable mentions for prestigious dissertation awards (Dantzig and ACM SIGecom). Research Highlights : Developed Panelot, a tool for selecting citizens' assemblies using state-of-the-art algorithms. His work on fair refugee resettlement and apportionment methods has been featured in Operations Research and Nature . Recent projects include AI alignment distortion analysis and generative social choice mechanisms. Teaching : Teaches Optimized Democracy (Spring 2025) and Mathematical Programming (Fall 2024). Supervises PhD students in ORIE, focusing on interdisciplinary applications of optimization and social choice. Awards & Grants : Received a $40K Structural Democracy Fellowship (Crankstart) and an OpenAI grant for Democratic Inputs to AI. Active in policy briefs (e.g., mini-public selection strategies). Labs/Tools : Co-developed Panelot.org , a nonprofit platform for fair citizen assembly selection. Active in open-source tool development for social choice applications.

Gary KingView profile

Professor

• Political Methodology
• Statistical Inference
• Quantitative Social Science
• +7 more

Gary King is the Albert J. Weatherhead III University Professor at Harvard University and Director of the Institute for Quantitative Social Science. He is based in the Department of Government within Harvard's Faculty of Arts and Sciences. One of only 22 University Professors at Harvard, this represents the institution's most distinguished faculty position. King received his B.A. from SUNY New Paltz in 1980 and his Ph.D. from the University of Wisconsin-Madison in 1984. His academic journey has led him to become one of the most influential scholars in political methodology and quantitative social science. Professor King's research spans numerous areas of methodological innovation in the social sciences. His work focuses on developing and applying empirical methods across various domains. Key research interests include: Ecological Inference - developing methods to infer individual behavior from group-level data Automated Text Analysis - creating techniques for extracting knowledge from massive text collections Causal Inference - methods for detecting and reducing model dependence in causal effect estimation Missing Data and Measurement Error - statistical approaches to handle incomplete or imperfect data Survey Research - developing methods for more accurate cross-cultural survey comparisons Unifying Statistical Analysis - integrating diverse methodological approaches into coherent frameworks King's recent publications demonstrate a continued focus on methodological innovation with practical applications. His work spans political science, public health, and data science, with particular emphasis on privacy-preserving data analysis, maternal health metrics, survey methodology, and media effects. A notable trend is the increasing interdisciplinary nature of his research, bridging political methodology with public health, computer science, and demography. His work on census data privacy, maternal mortality disparities, and media influence represents cutting-edge applications of social science methodology to critical societal issues. His scientific achievements have been recognized with numerous prestigious awards: Fellow of the National Academy of Sciences (2010) Fellow of the American Statistical Association (2009) Fellow of the American Academy of Arts and Sciences (1998) Guggenheim Foundation Fellow (1994-1995) Career Achievement Award (2010) Warren Miller Prize (2008) Multiple awards for research software and methodology King has mentored numerous students and postdocs, many of whom now hold faculty positions at leading universities. His research has been supported by major funding agencies including the National Science Foundation, Centers for Disease Control and Prevention, World Health Organization, and National Institute of Aging. He has collaborated with over seventy scholars on research publications and served on numerous editorial boards and professional organization councils. His work on the Mexican universal health insurance program represents one of the largest randomized health policy experiments to date, demonstrating his commitment to rigorous evaluation of real-world policy interventions. As Director of the Institute for Quantitative Social Science, King leads a vibrant research community focused on methodological innovation. His work has practical applications in diverse areas including legislative redistricting (used by the U.S. Supreme Court), health policy evaluation (including the largest randomized health policy experiment to date in Mexico), Chinese censorship analysis (revealing government fabrication of 450 million social media comments annually), and automated text analysis (through Crimson Hexagon, a company he co-founded).

Tyler SimkoView profile

Assistant Professor

• State and Local Politics
• Political Geography
• Subnational Policymaking
• +5 more

Tyler Simko is an Assistant Professor of Political Science at the University of Michigan, specializing in US state and local politics, political geography, and computational social science. His research focuses on understanding and addressing inequality in American public policy through innovative methodological approaches. Education: Ph.D. in Government, Harvard University (2024) A.B. in Politics, Princeton University Simko's research examines state and local politics in the United States with particular focus on political geography and subnational policymaking. His active research agendas include legislative redistricting ("gerrymandering"), local public meetings, school segregation, affordable housing, and data privacy. Methodologically, he develops new techniques in computational social science and machine learning to evaluate subnational inequality and how it can be reduced. His work regularly involves partnerships with federal, state, and local officials to improve the design of public policy. His recent publications demonstrate a strong focus on applying computational methods to address real-world policy challenges, particularly in school desegregation, redistricting, and local government transparency. His research often leverages large-scale data collection efforts, such as LocalView (the largest database of local government meetings in the US), to analyze patterns of political behavior and policy outcomes across different jurisdictions. Awards and Recognition: APSA 2024-25 Best Paper in Education Politics and Policy Award APSA 2024-25 Best Paper in Urban and Local Politics, Honorable Mention MPSA 2024 Robert H. Durr Award for "the best paper applying quantitative methods to a substantive problem" Derek C. Bok Award for Excellence in Graduate Student Teaching of Undergraduates (2023) Simko teaches graduate and undergraduate courses in American Politics and Political Methodology at the University of Michigan. His teaching experience spans multiple institutions, including Harvard University and Princeton University. He has designed innovative courses on US Local Policymaking, data science, and computational social science. As a Data Scientist at the Office of Evaluation Sciences, he partners with federal, state, and local officials to improve program design and reduce administrative burdens. He is a co-PI of the Algorithm-Assisted Redistricting Methodology (ALARM) Project and co-creator of LocalView, the largest audio, video, and text database of local government meetings in the United States. These projects represent significant contributions to the field of computational social science and provide valuable resources for researchers studying local governance and policy-making.

Ningchuan XiaoView profile

Professor

• Machine Learning and Spatial Data
• Census Data
• Spatial Optimization
• +3 more

Ningchuan Xiao is a Professor of Geography at The Ohio State University's Department of Geography. His work bridges Geographic Information Science (GIScience) with computational methods, emphasizing spatial optimization, cartography, and machine learning integration. Education: Ph.D. in Geography from The University of Iowa (2003). Courses taught include GIS fundamentals, cartography, and Python-based spatial analysis. Research Interests: Spatial Optimization: Developing algorithms for land acquisition, redistricting, and resource allocation. Machine Learning & Cartography: Exploring AI-driven map interpretation and ethical visualization of complex data. Census Data: Innovating privacy-preserving techniques while maintaining data utility, including temporal/spatial modeling. Open Source Tools: Authored GIS Algorithms (2016) and maintains GitHub repository 'gisalgs' for accessible code. Publications: Recent work (2023-2025) highlights advancements in synthetic microdata generation, privacy-utility tradeoffs in census aggregation, and AI-driven cartographic recognition. His 2022 studies include traffic camera analytics and choropleth map QA systems. Awards: Not explicitly listed in the provided texts. Advising & Grants: Collaborated with researchers like Y. Lin, J. Li, and S. Bao. Projects include the Sustainable Columbus Observatory (SCO) for urban sustainability metrics. Research is supported through academic partnerships and computational initiatives.

Matthew PeteringView profile

Associate Professor

• Political Redistricting
• University Course Scheduling
• Operations Research
• +8 more

Matthew Petering is an Associate Professor in the Department of Industrial and Manufacturing Engineering at the University of Wisconsin-Milwaukee. His work bridges operations research with real-world applications in logistics, transportation, and political redistricting. He holds a PhD in Industrial and Operations Engineering from the University of Michigan (2007), an MS from the same program (2003), and a BA in Mathematics from Washington University in St. Louis (1999). His research focuses on optimizing complex systems: from seaport container terminals to university course scheduling, and recently developing the FastMap algorithm for unbiased political redistricting. His algorithm was central to Wisconsin Supreme Court litigation in 2023-2024, submitting proposals for legislative maps that balanced fairness and operational efficiency. Dr. Petering has pioneered board game designs like Distrix (2020), which won international awards, and authored The Distrix Puzzle Book . His peer-reviewed work spans over 30 publications in journals like Transportation Science , European Journal of Operational Research , and International Journal of Production Economics . Key contributions include: Algorithmic solutions for container shipping and rail logistics Cyclic production planning models Real-time location management systems for distribution facilities Emergency evacuation modeling His FastMap redistricting system was recognized with the 2024 INFORMS Practitioner Poster Competition award. Dr. Petering frequently advises governmental bodies and judicial proceedings on redistricting methodology and logistics optimization.

Daanika GordonView profile

Associate Professor

• Law and criminal legal system
• Racial inequality and racial formation
• Urban politics and cities
• +2 more

Daanika Gordon is an Associate Professor of Sociology at Tufts University, with a secondary appointment in Studies in Race, Colonialism, and Diaspora. Her research explores the intersection of racial inequality, urban governance, and policing through organizational and interactional lenses. PhD, University of Wisconsin–Madison (2018) MS, University of Wisconsin–Madison (2013) BA, Development Studies & Sociology, UC Berkeley (2009) Her work connects theories of race and racism with urban political economy and organizational behavior, focusing on how: Policing functions as a tool for urban governance Racial inequalities emerge from race-neutral policies Segregation is dynamically produced through relational processes Recent publications analyze police funding debates, data science applications in policing, and the bureaucratic dissociation of race. Her groundbreaking 2022 book Policing the Racial Divide won the 2023 Edwin H. Sutherland Book Award. She teaches sociology of race, criminal legal systems, and research design while serving on the advisory committee for the Tufts University Prison Initiative. Her research has been funded by: Institute for Citizens & Scholars Bernstein Faculty Fellowship Neubauer Faculty Fellowship National Science Foundation

Jonathan RoddenView profile

Professor

• Comparative Politics
• Political Economy
• Federalism
• +6 more

Jonathan Rodden is a Professor of Political Science at Stanford University and a Senior Fellow at the Hoover Institution and Stanford Institute for Economic Policy Research. His research focuses on the comparative political economy of institutions, particularly federalism and fiscal decentralization, with significant contributions to understanding the urban-rural political divide and its implications for representation and policy. PhD from Yale University BA from the University of Michigan Fulbright student at University of Leipzig His research spans several domains: Comparative Federalism: Analyzed fiscal decentralization mechanisms, budgetary transfers, and institutional design in federations globally. Political Geography: Developed computational methods for redistricting analysis and demonstrated how geographic voter distribution creates inherent electoral biases. Policy Uncertainty: Co-authored foundational work linking economic uncertainty to political polarization and fiscal cycles. Public Health: Conducted epidemiological studies on firearm-related mortality and suicide patterns in California. Scientific awards include: Gregory Luebbert Prize for best book in comparative politics (2007) Michael Wallerstein Award for best paper in political economy (2015) He has advised PhD students like Jowei Chen and directed policy projects with international institutions including the World Bank, IMF, and OECD. His work on redistricting simulations has been cited in U.S. Supreme Court amicus briefs and congressional testimony.

Steven BramsView profile

Professor

• Game Theory
• Social Choice Theory
• Fair Division
• +6 more

Steven Brams is a Professor of Politics at New York University's Department of Politics within the College of Arts & Science. His research focuses on game theory, social choice theory, fair division, voting systems, and international politics. He holds a B.S. from MIT (1962) and a Ph.D. from Northwestern University (1966). Brams' work emphasizes fair allocation mechanisms, including envy-free division algorithms, voting system reforms, and rule design in sports and conflict resolution. He has collaborated extensively with researchers like D. Marc Kilgour and Mehmet S. Ismail on topics such as fair shootouts in soccer, equitable chess openings, and gerrymandering solutions. Education: B.S., Massachusetts Institute of Technology, 1962 Ph.D., Northwestern University, 1966 His research explores how game-theoretic models can address real-world problems, such as improving voting systems, resolving disputes, and enhancing fairness in sports. Notable contributions include the 'Catch-Up' rule for service sports and the 'Excess Method' for multiwinner approval voting. Awards: American Association for the Advancement of Science Fellow (1992) Guggenheim Fellow (1986–1987) Russell Sage Foundation Visiting Scholar (1998–1999) Elinor Ostrom Prize (2013) Brams has advised on electoral reforms and contributed to policy discussions on fair division in international conflicts, such as the Spratly Islands dispute. His interdisciplinary work bridges mathematics, political science, and philosophy, addressing both theoretical and applied challenges in decision-making.

Steven O. KimbroughView profile

Professor

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +6 more

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Sheldon Howard JacobsonView profile

Professor

• Operations Research
• Aviation Security
• Optimization
• +3 more

Sheldon Howard Jacobson is a Founder Professor 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 focuses on operations research, optimization, and security systems, with notable contributions to aviation security, pediatric vaccines, and homeland security. Jacobson has been recognized with prestigious awards including the AAAS Fellowship (2019), George E. Kimball Medal (2020), and Guggenheim Fellowship (2003). His work bridges theoretical and applied domains, addressing real-world challenges in public health, policy, and technology. He has contributed to media discussions on topics like pandemic impacts and coronavirus safety measures. In research, Jacobson emphasizes interdisciplinary approaches, combining mathematical modeling with policy analysis. His recent work includes optimizing political redistricting and analyzing viral transmission risks in aviation. Collaborations span multiple disciplines, reflecting his role as a bridge between academia and practical problem-solving.

Wendy K Tam ChoView profile

Professor

• Quantitative Methods in Social Sciences
• Political Science
• Computer Science
• +5 more

Wendy K Tam Cho is a Professor at the University of Illinois at Urbana-Champaign with appointments in multiple departments: Political Science, Statistics, Mathematics, Computer Science, Asian American Studies, and the College of Law. She also holds roles as Senior Research Scientist at the National Center for Supercomputing Applications (NCSA), Faculty in the Illinois Informatics Institute, and Affiliate of several research centers including the Cline Center for Advanced Social Research and the CyberGIS Center. Her research focuses on quantitative methods in social sciences, AI applications in law and public policy, and computational epidemiology. She has received prestigious fellowships from the Guggenheim Foundation, Society for Political Methodology, and Stanford’s Center for Advanced Study. Her work bridges computational methods and social science, addressing issues like redistricting, health disparities, and causal inference. Recent publications analyze demographic impacts on pandemic outcomes and the role of AI in legal processes. She leads interdisciplinary teams at NCSA and the Illinois Informatics Institute, advancing data-driven solutions for societal challenges.

Kassra A.R. OskooiiView profile

Associate Professor

• American Politics
• Political Psychology
• Race and Ethnic Politics
• +4 more

Kassra A.R. Oskooii is an Associate Professor in the Department of Political Science & International Relations at the University of Delaware, College of Arts & Sciences. His expertise spans American Politics, Political Psychology, and Race and Ethnic Politics with a focus on voting rights, redistricting, and minority political engagement. He holds a Ph.D. from the University of Washington (2016) and serves as an affiliated faculty member at UD's Data Science Institute, Center for Political Communication, and Center for the Study of Diversity. Dr. Oskooii's research examines how contextual and psychological factors influence political attitudes among marginalized groups, particularly Muslim Americans and Latinx communities. His work has addressed anti-Muslim policy preferences, the impact of perceived discrimination on civic participation, and the role of social media in shaping political attitudes. He has served as an expert witness in voting rights cases across multiple states, leveraging methodological innovations such as his eiCompare Ecological Inference R Package. Key research areas include: Islamophobia and minority political behavior Electoral politics and redistricting strategies Immigration policy and partisan mobilization Methodological advancements in voting rights analysis His publications in journals like the British Journal of Political Science and Political Behavior reflect interdisciplinary approaches to understanding contemporary U.S. political dynamics. Oskooii's work bridges academic research with legal and policy applications, emphasizing the societal implications of political decisions on marginalized communities.

Federica RiccaView profile

Associate Professor

• Combinatorial Optimization
• Network Optimization
• Portfolio Selection
• +5 more

Federica Ricca is an Associate Professor (SECS-S/06) at the Department of Methods and Models for Economy, Territory and Finance (MEMOTEF) at Sapienza University of Rome. She teaches courses including Project Management, Financial Optimization and Asset Management, and Quantitative Portfolio Selection for Management at the Faculty of Economics. Her academic career includes: Associate Professor at MEMOTEF Department since 2015 Researcher (MAT/09) at the Department of Statistical Sciences, University of Rome 'La Sapienza' from December 30, 2008 to September 30, 2015 Federica Ricca's research focuses on applied mathematics, combinatorial optimization, and network optimization . Her work has significant applications in electoral system mathematics (particularly biproportional seat allocation and electoral districting), service location problems and customer center assignment, and classification problems. She has developed innovative approaches to portfolio selection problems using combinatorial optimization and financial network analysis. Her recent publications demonstrate a strong focus on the intersection of mathematical optimization with practical applications in finance and political systems. She has made significant contributions to portfolio optimization through network approaches, political districting with attention to minority representation, and combinatorial optimization for financial decision-making. Her work bridges theoretical mathematical concepts with real-world applications in finance and electoral systems. Dr. Ricca is actively involved in research projects including: Mathematical Models for Portfolio Selection Problems: Innovative Combinatorial Optimization and Financial Network Analysis Approaches Quantitative models and efficient procedures for territorial partition and aggregation problems with applications in the public and private services sector She maintains regular office hours for student consultations and follows institutional protocols for student communications, requiring students to use their institutional email addresses when contacting her.

• Political Institutions
• Political Economy
• Media and Politics
• +3 more

Kevin DeLuca is an Assistant Professor of Political Science at Yale University and a Resident Fellow at the Institution for Social and Policy Studies (ISPS). He is also a Faculty Affiliate at the Center for the Study of American Politics (CSAP). His research focuses on political institutions, the political economy of media, and election laws, particularly examining the role of local newspapers in shaping political outcomes. DeLuca holds a Ph.D. in Political Economy and Government from Harvard University (2023), an M.A. in Economics from the University of Texas at Austin (2015), and a B.A./B.S. in Economics and Psychology from the same university (2014). He teaches courses such as 'Data Analytics for Redistricting and Voting Rights' and 'Regulating Elections: Voting Rights and Election Law.' His work intersects with computational methods in redistricting analysis and the influence of media on electoral processes. Previously, he collaborated with institutions like the MIT Election Data and Science Lab (MEDSL) and the Stanford-MIT Healthy Elections Project. His research emphasizes methodologies like Bayesian geocoding for redistricting validation and explores partisan dynamics in gerrymandering solutions. He has contributed to groundbreaking studies on candidate quality measurement via media endorsements and the impact of top-tier elections on down-ballot races.

Kamesh MunagalaView profile

Associate Professor

• Algorithm design
• Discrete optimization
• Approximation algorithms
• +8 more

Kamesh Munagala is an Associate Professor of Computer Science at Duke University, where he has been employed since 2004. His research spans theoretical computer science with applications in e-commerce, databases, data analysis, and networks. He serves as an Area Editor for PeerJ Computer Science and has held leadership roles including Director of Graduate Studies for the Duke CS department from 2012 to 2015. Education: PhD in Computer Science from Stanford University (2003) BTech from IIT Bombay (1998) Munagala's research focuses on algorithm design and discrete optimization, particularly in scenarios with uncertain inputs. His work encompasses approximation algorithms, online algorithms, and algorithmic game theory. Recent research has concentrated on two main themes: (1) Persuading optimizers or learners toward certain objectives through information revelation and pricing, and (2) Ensuring fairness to groups based on proportionality and stability in resource allocation and societal decision making contexts. His theoretical work has practical applications in designing data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Analysis of Munagala's recent publications reveals a strong trend toward interdisciplinary research at the intersection of computer science, economics, and social choice theory. His work shows increasing sophistication in handling fairness constraints across multiple dimensions, particularly in societal decision-making contexts like school assignment, participatory budgeting, and redistricting. There is also a notable expansion into the emerging area of large language models for combinatorial optimization, reflecting the field's evolution toward AI-assisted algorithmic solutions. His research consistently bridges theoretical foundations with real-world applications requiring nuanced approaches to resource allocation under uncertainty. Scientific Awards: NSF CAREER Award (2008) Alfred P. Sloan Research Fellowship (2009) Best paper award at WWW 2009 conference Munagala has served as Director of Graduate Studies for the Duke CS department from 2012 to 2015 and was a Visiting Research Professor at Twitter in 2012. His research has been supported by prestigious grants including the NSF CAREER award. He actively mentors students, with many of his publications featuring student co-authors across his two main research thrusts. His work addresses fundamental challenges in computing efficient solutions under uncertainty, designing pricing and incentive mechanisms for selfish agents, and developing fairness-preserving algorithms for societal decision making. Munagala leads research projects in two broad categories: proportionality in resource allocation and societal decision making, and asymmetric information and persuasion in learning and optimization. His work on proportionality explores fairness concepts like proportionality or core-stability across various societal contexts, while his research on asymmetric information addresses how parties with incomplete information collaborate or compete to achieve optimization objectives through strategic information revelation.