جست‌وجوی استاد و پژوهشگر

Barton Leeمشاهده پروفایل

استادیار

• Political Economics
• Applied Theory
• EconCS
• +۳ مورد دیگر

Barton Lee is an Assistant Professor (tenure track) and Chair of Political Economy & eDemocracy at the Department of Management, Technology, and Economics (D-MTEC) of ETH Zurich . He holds an Affiliate Fellowship with the Stigler Center at Chicago Booth and was a Junior Research Fellow at Magdalen College, University of Oxford (2021–2022). Dr. Lee earned his PhD in Economics from the University of New South Wales (UNSW) in 2021, following a visiting year at Harvard University (2018–2019). Current Role : Asst. Prof (TT) + Chair, ETH Zurich Affiliations : Stigler Center (Chicago Booth), UNSW Resilient Democracy Lab Research Focus : Political Economics, Applied Theory Dr. Lee investigates democratic fragility through lenses of legislative gridlock , technocratic governance , and populist voter behavior . His work connects economic policy with political institution design , often employing formal modeling and empirical analysis . Notable projects include studies on the feedback loops between polarization and policy failure and adaptive strategies in autocratic regimes . He actively contributes to academic networks via personal website and Google Scholar , and engages in collaborative workshops such as the 2025 IAST/TSE Workshop on Political Economy of Autocratic Rule . Dr. Lee also advocates for academic positions like the Professor of Health Economics role at ETH Zurich. Scientific Awards: Affiliate Fellow, Stigler Center (2021–present) Junior Research Fellow, Magdalen College, Oxford (2021–2022) Dr. Lee's recent work explores intersections between technology , democracy , and economic theory , with active participation in conferences and mentoring networks across Europe and Australia.

Shahin Jabbariمشاهده پروفایل

استادیار

• Machine Learning
• Algorithmic Fairness
• Game Theory
• +۷ مورد دیگر

Shahin Jabbari is an Assistant Professor in the Computer Science Department at the College of Computing & Informatics, Drexel University, where he is a member of the EconCS research group. His research lies at the intersection of machine learning, game theory, and algorithmic fairness, with a focus on ethical AI and its societal implications. Prior to Drexel, he was a CRCS postdoctoral fellow at Harvard University's School of Engineering and Applied Sciences, hosted by Milind Tambe, and affiliated with the EconCS group. Education: PhD in Computer and Information Science, University of Pennsylvania (2013–2019), advised by Michael Kearns Master's in Computing Science, University of Alberta, advised by Robert Holte and Sandra Zilles Bachelor's in Computer Engineering, Sharif University of Technology His research interests center on machine learning, algorithmic fairness, and game theory, particularly focusing on how AI systems can be designed to be more equitable, interpretable, and robust. He investigates ethical aspects of algorithmic decision-making, aiming to ensure AI technologies contribute positively to society. His work often integrates human behavior modeling and experimental validation, especially in cybersecurity and public health domains. His recent publications span top venues including ICML, NeurIPS, AAAI, AAMAS, PNAS, and TMLR. The research trends show a consistent focus on fairness in AI, explainability, robustness, and strategic interactions in complex systems. Topics include fair influence maximization, adaptive phishing training, cyber deception games, and ethical machine learning frameworks. These works reflect a multidisciplinary approach combining theoretical rigor with real-world applicability. Scientific Awards and Recognitions: Best Paper Finalist, AAMAS 2021 Best Paper, GameSec 2020 Spotlight Presentation, ICML 2021 Best Paper, KI 2012 Shahin Jabbari actively contributes to the academic community through advising, teaching, and service. He teaches graduate courses such as CS 589: Responsible Machine Learning and CS 590: Privacy. He has served on the senior program committees of ICML and NeurIPS, is an Action Editor for TMLR, and has reviewed for numerous top-tier conferences and journals. He mentors students through research projects and invites prospective PhD candidates to apply through Drexel’s formal channels. He is involved in the Drexel Computer Science Theory Reading Group and contributes to advancing responsible AI practices. He is affiliated with the EconCS group at Drexel, which focuses on economic and computational aspects of AI, including game theory, mechanism design, and multi-agent systems. His lab integrates tools from machine learning, behavioral modeling, and optimization to develop AI systems that are not only intelligent but also fair and trustworthy. Future work is expected to further explore human-AI collaboration, ethical AI deployment, and policy-aware algorithm design.

Pieter Kleerمشاهده پروفایل

استادیار

• Algorithmic game theory
• Approximation algorithms
• Markov Chain Monte Carlo (MCMC) methods
• +۶ مورد دیگر

Pieter Kleer is an Assistant Professor at Tilburg School of Economics and Management (TiSEM), Department of Econometrics and Operations Research at Tilburg University since April 2021. His research focuses on algorithmic game theory, approximation algorithms, and Markov Chain Monte Carlo methods. He holds a PhD from Vrije Universiteit Amsterdam (2019) and completed postdoctoral research at the Max Planck Institute for Informatics (2019–2021). Education: Bachelor’s and Master’s in Mathematics (cum laude) from TU Delft (2010–2015) PhD in Economics and Computer Science from Vrije Universiteit Amsterdam (2015–2019) Research interests: Resource allocation games and online algorithms (EconCS) MCMC methods for combinatorial sampling (e.g., graphs with given degrees) Algorithmic economics and network optimization Advising: Co-supervising PhD student Bas Verseveldt. Active in teaching courses like Linear Optimization and Computational Aspects in Econometrics. His work bridges economics, computer science, and mathematics, with recent contributions to graph sampling theory and network equilibrium analysis.

• Economics of Artificial Intelligence
• Competition Economics
• Experimental Economics
• +۳ مورد دیگر

Tobias Felix Werner is an Assistant Professor (Lecturer) in Economics at the University of Southampton. He maintains an affiliation as an Associated Research Scientist at the Center for Humans and Machines at the Max-Planck Institute for Human Development and is affiliated with the Düsseldorf Institute for Competition Economics (DICE). Previously, he completed a postdoctoral fellowship at the Max-Planck Institute and will be visiting Harvard University's EconCS group in Spring 2025. His educational background includes: Dr. rer. pol. in Economics, University of Düsseldorf / DICE (2019-2023) M.Sc. Economics, University of Bonn (2016-2019) B.Sc. Economics, University of Cologne (2012-2015) Werner's research focuses on the intersection of economics, artificial intelligence, and experimental methods. His primary areas of interest include the economics of artificial intelligence, competition economics, experimental economics, and human-machine interactions. He examines how algorithms affect market competition, pricing behavior, and consumer decision-making through both laboratory and field experiments. His work combines theoretical frameworks with empirical approaches to understand the implications of AI in economic contexts, particularly regarding algorithmic pricing and potential anti-competitive behavior. His publication record reveals a strong focus on algorithmic pricing, collusion, and human-AI interactions in economic settings. Recent work examines how pricing algorithms can lead to collusive outcomes, how conversational AI influences consumer behavior, and the challenges of conducting behavioral research in the age of large language models. His research spans both controlled laboratory environments and real-world field experiments, with applications to industrial organization, competition policy, and digital markets. Werner has received recognition for his work, including: Fellowship from the Joachim Herz Stiftung Werner is actively involved in academic collaborations across institutions, working with researchers at the Max-Planck Institute for Human Development, Harvard University, and various European institutions. His fellowship from the Joachim Herz Stiftung specifically supports his research into how companies use large language models to influence consumer decisions. His work on 'LLM Pollution' addresses critical methodological challenges facing behavioral researchers in the AI era. He is affiliated with the Center for Humans and Machines at the Max-Planck Institute for Human Development, which focuses on understanding the interactions between humans and machines in various contexts. His work contributes to the institute's research on how AI systems affect human behavior, decision-making, and social dynamics in economic settings, with implications for both theoretical understanding and practical policy applications.

Yiling Chenمشاهده پروفایل

استاد

• Artificial Intelligence
• Algorithmic Game Theory
• Multi-Agent Systems
• +۷ مورد دیگر

Yiling Chen is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Her research focuses on the intersection of artificial intelligence and social sciences, emphasizing robustness and integrity of AI systems in strategic environments. She is affiliated with the EconCS research group and the Center for Research on Computation and Society (CRCS). Key research areas include information elicitation, incentive-aware machine learning, algorithmic fairness, and algorithmic game theory. Notable contributions include work on prediction markets, privacy mechanisms, and behavioral experiments. She has taught courses such as AM 121: Introduction to Optimization, CS 128: Convex Optimization, and advanced topics courses on blockchain, societal impacts of AI, and data valuation. She has organized workshops on social computing, prediction markets, and human computation. Her work has received awards, including the Best Technical + Interdisciplinary Paper Award (2019) and the Best Paper Award at CSCW (2019). She advises graduate students in computer science with a focus on interdisciplinary work between CS and economics.

Gregory Kehneمشاهده پروفایل

استادیار

• Computational Methodologies
• Machine Learning & Artificial Intelligence
• Theoretical Computer Science
• +۴ مورد دیگر

Gregory Kehne is an Assistant Professor in the Department of Computer Science & Engineering at the McKelvey School of Engineering , Washington University in St. Louis. His primary research focuses on online and approximation algorithms, computational social choice, and improving collective decision-making through algorithmic methods. Education: PhD in Computer Science, Harvard University (2023) BA in Computer Science, Williams College (2016) Research Interests: Combines theoretical computer science with applications in social and economic systems. Explores how algorithms can optimize decision-making processes in collaborative environments. Current work emphasizes computational methodologies for collective intelligence and algorithmic fairness in distributed systems. Awards: 2023 Siebel Scholar Professional Background: Previously a postdoctoral researcher at the University of Texas at Austin (2023–present) and a doctoral researcher in Harvard’s EconCS Group , which bridges AI, algorithms, and societal impact. Earlier studies included the Algorithms, Combinatorics, and Optimization (ACO) program at Carnegie Mellon University. Labs & Resources: Maintains an active lab website and contributes to the Google Scholar profile linked here. His work frequently intersects with interdisciplinary teams addressing algorithmic challenges in real-world decision-making frameworks.

Kira Goldnerمشاهده پروفایل

استادیار

• Algorithmic Mechanism Design
• Approximation Algorithms
• Social Good Applications
• +۷ مورد دیگر

Kira Goldner is an Assistant Professor in the Faculty of Computing & Data Sciences at Boston University. She is a co-founder of the Mechanism Design for Social Good (MD4SG) initiative and directs the BEACH research hub. PhD in Computer Science & Engineering, University of Washington Postdoc: Columbia University (NSF Mathematical Sciences & Data Science Institute) Her research focuses on algorithmic mechanism design and approximation algorithms , particularly their applications to social equity, healthcare access, climate policy, and AI ethics. She explores foundational questions in revenue maximization and behavioral economics under varied information constraints. Recent publications highlight her work on interdependent valuations in bilateral trade (EC 2025), multidimensional Bayesian utility optimization (2025), and matroid prophet inequalities (SAGT 2024). Her research bridges theoretical computer science with real-world socio-economic challenges. Awardees include the NSF CAREER Award (2025), Microsoft Research PhD Fellowship (2017-19), and Google Anita Borg Scholarship (2016-17). She mentors students across disciplines, including postdoc Divyarthi Mohan and PhD candidate Thodoris Tsilivis. She organizes academic events like the BEACH Day 2025 workshop on behavioral econCS models and co-organized WALE 2024 . Her work emphasizes interdisciplinary collaboration, with affiliations to Boston University’s Rafik Hariri Institute and the Boston Economics and Computing Hub .

• Computer Science
• Economics
• Operations Research
• +۵ مورد دیگر

Kangning Wang is an Assistant Professor of Computer Science at Rutgers University (New Brunswick). He is affiliated with the EconCS group and the CS theory group. His research focuses on the intersection of Computer Science, Economics, and Operations Research, with expertise in Mechanism Design, Social Choice, Information Design, Market Design, and Algorithmic Fairness. Education: Ph.D. in Computer Science, Duke University (advised by Kamesh Munagala) Bachelor's degree from Yao Class, Tsinghua University Professional Experience: Postdoctoral researcher at Stanford University (with Moses Charikar and Aviad Rubinstein) Visiting scholar at the Simons Institute for the Theory of Computing (UC Berkeley) Research internships at Google Research (hosted by Jieming Mao, Renato Paes Leme, and Aranyak Mehta) Awards & Recognition: ACM SIGecom Doctoral Dissertation Award Honorable Mention Duke CS Best Dissertation Award Best Paper Awards at SODA 2024 and WINE 2018 Labs/Teams: Active member of the EconCS and CS theory research groups at Rutgers.

• Economics of Artificial Intelligence
• Industrial Organization
• Experimental Economics
• +۲ مورد دیگر

Dr. Tobias Werner is an academic economist specializing in human-machine interaction and market dynamics. He holds a Lecturer position (equivalent to Assistant Professor) at the University of Southampton's Department of Economics starting September 2025. Concurrently, he maintains affiliations as an Associated Research Scientist at the Max Planck Institute for Human Development's Center for Humans and Machines and as a Research Affiliate at the Düsseldorf Institute for Competition Economics (DICE). Education includes: Ph.D. in Economics, Heinrich-Heine University Düsseldorf (2023) M.Sc. Economics, University of Bonn (2016-2019) B.Sc. Economics, University of Cologne (2012-2015) His research bridges industrial organization, experimental economics, and AI, with core interests in: Algorithmic collusion and pricing mechanisms Human-AI interaction in economic decision-making Experimental analysis of competition economics Behavioral impacts of remote work structures LLM influence on consumer behavior Recent publications demonstrate a consistent focus on experimental approaches to algorithmic market behavior, with recurring themes of pricing algorithms, remote work dynamics, and human-machine interaction. His work frequently employs controlled experiments to analyze how AI systems influence traditional economic frameworks. Scientific recognition includes: Joachim Herz Stiftung Fellowship for research on LLM-driven consumer behavior He leads research on algorithmic market behaviors at the Center for Humans and Machines and collaborates with Harvard's EconCS group. Current projects include field experiments on human-machine pricing interactions and studies on LLM pollution in behavioral research.