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Piotr DworczakView profile

Associate Professor

• Mechanism Design
• Information Design
• Inequality-aware Market Design
• +7 more

Piotr Dworczak is an Associate Professor of Economics at Northwestern University's Weinberg College of Arts & Sciences, holding tenure since 2022. He also serves as a Research Affiliate at the Group for Research in Applied Economics (GRAPE) in Warsaw and a Senior Researcher at the University of Zurich. His research focuses on Mechanism and Information Design , with applied interests in Inequality-aware Market Design and Financial Over-the-Counter Markets . He earned his PhD in Economic Analysis and Policy from Stanford University's Graduate School of Business in 2017, alongside B.A. degrees in Mathematics (University of Warsaw) and Economics (Warsaw School of Economics). His work bridges theoretical economics with practical applications, including vaccine prioritization frameworks, optimal transparency in financial markets, and redistributive mechanisms. Notable contributions include Preparing for the Worst but Hoping for the Best: Robust (Bayesian) Persuasion (Econometrica, 2022) and Redistribution through Markets (Econometrica, 2021). Recent projects address price gouging theory, energy crisis responses, and property rights via mechanism design. Research Highlights: Over 20 peer-reviewed papers in top journals like Econometrica, Journal of Political Economy, and Journal of Finance. Grants: ERC Starting Grant (2022–2027), Alfred P. Sloan Fellowship (2022–2024). Awards: Second-place Fama-DFA Award (2021), Amundi Smith Breeden First Prize (2017). Teaching includes PhD courses on Market Design (Northwestern) and Financial Market Design (Chicago Booth). Professional activities span organizing major conferences (e.g., SIGecom Winter Meeting) and refereeing for journals like Econometrica and the American Economic Review.

Massimiliano PontilView profile

Professor

• Machine Learning
• Statistical Learning Theory
• Kernel Methods
• +5 more

Massimiliano Pontil is a **part-time Professor of Computational Statistics and Machine Learning** in the Department of Computer Science at University College London (UCL) and a Senior Researcher at the **Istituto Italiano di Tecnologia (IIT)**, leading the **Computational Statistics and Machine Learning (CSML)** research group. He holds an MSc and PhD in Physics from the University of Genova (1994 and 1999) and has held postdoctoral positions at MIT and research roles at institutions like the University of Siena and City University of Hong Kong before joining UCL in 2003. **Research Interests**: Focuses on machine learning, including statistical learning theory, kernel methods, multitask and transfer learning, online learning, sparsity regularization, and applications in computer vision, bioinformatics, and user modeling. His work bridges theoretical foundations (e.g., regularization, optimization) with practical applications. **Publications**: Over 100 papers in top venues like NIPS/NeurIPS, ICML, COLT, and JMLR, covering topics such as structured sparsity, multitask learning, and optimization frameworks. Recent work emphasizes meta-learning, fair algorithms, and dynamical systems learning. **Grants & Awards**: EPSRC Advanced Research Fellowship. His research has been applied to diverse domains, including bioinformatics (e.g., protein-protein interface prediction) and social network analysis (e.g., mobility patterns). **Labs/Teams**: Leads the CSML group at IIT, collaborating on projects like Koopman operator regression and conditional meta-learning. Active in organizing workshops on multi-task learning and structured outputs (e.g., PASCAL thematic programs).

Roberto Solis-ObaView profile

Professor

• Algorithm Design
• Approximation Algorithms
• Data Structures
• +9 more

Overview Roberto Solis-Oba is a Professor in the Department of Computer Science at the University of Western Ontario. He holds a Ph.D. from Purdue University (supervised by Prof. Greg Frederickson) and completed a postdoctoral fellowship at the Max Planck Institute for Informatics under Prof. Kurt Mehlhorn. His research focuses on algorithm design, particularly approximation algorithms for NP-hard optimization problems, with applications in networks, scheduling, packing, and computational biology. Education & Background Ph.D., Computer Science, Purdue University (1990s) Postdoctoral Fellowship, Max Planck Institute for Informatics Research Interests His work spans algorithm design, including approximation algorithms, data structures, parallel/distributed/online/randomized algorithms. Key application areas include networks, scheduling, packing optimization, computational biology, and data mining. He co-founded the Workshop on Approximation and Online Algorithms (WAOA), held annually since 2003. Awards & Recognition Faculty Scholar Award, University of Western Ontario USC Teaching Award, University of Western Ontario Professional Contributions Prof. Solis-Oba has served on program committees for major algorithm-focused conferences and contributed to research in scheduling, packing, and graph theory. His work emphasizes practical and theoretical advancements in algorithmic efficiency and optimization.

Jörg RotheView profile

Professor

• Computational Complexity
• Computational Social Choice
• Algorithmic Game Theory
• +5 more

Prof. Jörg Rothe is a faculty member at the Heinrich-Heine-Universität Düsseldorf, affiliated with the Institut für Informatik (Department of Computer Science) under the Mathematisch-Naturwissenschaftliche Fakultät (Faculty of Mathematics and Natural Sciences). His research focuses on computational complexity, computational social choice, algorithmic game theory, fair division, and cryptology. He has authored or co-authored numerous books and publications in top-tier journals and conferences. Education details not explicitly mentioned in provided texts. Key research areas include voting theory, bribery complexity, coalition formation, and argumentation systems. Rothe's work explores topics like the complexity of control in elections, fair allocation of resources, and stability in hedonic games. His recent publications address challenges in multiagent systems and computational social choice, such as fair division mechanisms and algorithmic solutions for strategic behavior. Despite extensive research contributions, no scientific awards or grants were explicitly listed in the provided materials. His group's work often intersects theoretical computer science with practical applications in AI and decision-making systems.

Christopher StockerView profile

Associate Professor

• Graph Theory
• Combinatorics
• Discrete Mathematics

Dr. Christopher Stocker serves as Assistant Chair and Teaching Associate Professor at the Department of Mathematical and Statistical Sciences, Marquette University. His expertise lies in Graph Theory, Combinatorics, and Discrete Mathematics. He teaches courses such as Finite Mathematics, Calculus I/III, and Discrete Mathematics. His research focuses on advanced graph theory topics including hypergraph packing, overlap numbers, graph coloring, and domination theory. Notable contributions include studies on acyclic colorings and online Ramsey theory. No scientific awards or grants are explicitly listed. He has advised no documented students. His academic work emphasizes theoretical contributions to discrete structures and combinatorial optimization.

Murat KantarciogluView profile

Professor

• Data Privacy
• Computer Security
• Machine Learning
• +5 more

Murat Kantarcioglu is an Ashbel Smith Professor of Computer Science at the University of Texas at Dallas within the Erik Jonsson School of Engineering and Computer Science. He holds visiting appointments at UC Berkeley and Harvard University, focusing on data privacy and security. With a Ph.D. in Computer Science from Purdue University (2005), he has made significant contributions to privacy-preserving data mining, blockchain analytics, and secure machine learning. Education: Ph.D. in Computer Science (Purdue, 2005) Current Roles: Ashbel Smith Professor (2021-present), Visiting Scholar at UC Berkeley (2020-present), Affiliate at Harvard (2013-present) Past Roles: Assistant (2005-2011), Associate (2011-2015), and Full Professor (2015-2021) at UTD His research focuses on data privacy , computer security , and machine learning , particularly addressing challenges in privacy-preserving distributed data mining , blockchain analytics , and adversarial machine learning . He has pioneered techniques for secure federated learning , topological analysis of blockchain networks , and privacy-utility tradeoffs in health data systems. Recent publications reveal a strong emphasis on IoT security , graph neural network vulnerabilities , and blockchain data structures . His work combines theoretical rigor with practical implementations using technologies like Intel SGX and homomorphic encryption. Notable Awards NSF CAREER Award (2009) IEEE Technical Achievement Award (2017) AMIA Homer Warner Best Paper Award (2014) Fellow of IEEE (2022), AAAS (2020), and ACM (2016) Key Projects Privacy-Preserving Genomics Data Sharing Adversarial Learning Frameworks Smart Contract Security Medical Data Protection Systems Labs Director of Data Security and Privacy Lab Collaborations with Vanderbilt, UC Berkeley (RISE Lab), and Harvard (Data Privacy Lab)

• Quantitative Methods
• Social Policy Analysis
• Basic Income Guarantee
• +3 more

Michael A. Lewis is Professor at Silberman School of Social Work, City University of New York, with expertise in quantitative methods, social policy, and civic engagement. Education includes a Ph.D. from CUNY Graduate Center (1995), M.S.W. from Columbia University (1990), and B.A. from McDaniel College (1987). Research centers on economic justice frameworks, particularly Basic Income Guarantee systems, causal inference methodologies, and mathematical modeling of social phenomena. Publications (2006-2020) show progression from economic policy analysis to advanced quantitative applications in social work research, with consistent focus on poverty/social welfare systems. Awards include Fulbright Specialist Grant, NASW Leadership Award, and quantitative methods recognition. Professional service includes board membership for Basic Income organizations. Current office: Room 603, Silberman Building, 2180 Third Avenue, New York.

Pirathayini SrikanthaView profile

Associate Professor

• Game Theory
• Large-scale Optimization
• Distributed Control
• +5 more

Pirathayini Srikantha serves as Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering, holding a Tier 2 Canada Research Chair in Reliable and Secure Power Grid Systems with critical national security implications for Canada's power infrastructure. Her academic foundation includes: B.A.Sc. in Systems Design Engineering from University of Waterloo (2009) M.A.Sc. in Electrical and Computer Engineering from University of Waterloo (2013) Ph.D. from University of Toronto's Edward S. Rogers Sr. Department of Electrical and Computer Engineering (2017) Professor Srikantha's research centers on game theory, large-scale optimization, and distributed control to enable adaptive power grid operations. Her work addresses cybersecurity vulnerabilities in smart grids through distributed coordination frameworks and develops transactive energy market mechanisms that incentivize grid support during congestion. By integrating AI breakthroughs, she focuses on creating systems that automatically infer, predict, and respond to power disturbances while enhancing infrastructure resilience against evolving threats. Her publication trajectory reveals consistent innovation in grid security and optimization, with increasing emphasis on AI-driven solutions for grid resilience since 2015. Key thematic evolution shows progression from foundational distributed control (2015-2017) to sophisticated cybersecurity applications (2018-2021), consistently appearing in premier venues like IEEE Transactions on Smart Grid. Her scholarly impact is recognized through: Best paper award at IEEE Smart Grid Communications Runner-up best poster award at ACM Women in Computing As Canada Research Chair holder, she directs a federally funded research program addressing critical grid vulnerabilities, while serving as Associate Editor for IEEE Transactions on Smart Grid. Her work bridges theoretical advances in game-theoretic security with practical grid applications, though specific student mentorship details aren't publicly enumerated in source materials. Her research group operates at the intersection of electrical engineering and computer science, developing next-generation tools for grid operators through collaborations with Canadian energy infrastructure stakeholders.

Kumar ManiView profile

Senior Lecturer

• Algorithms
• Distributed Systems
• Graph Theory
• +3 more

Kumar Mani is a Senior Lecturer in the Department of Computer Science at New Jersey Institute of Technology (NJIT). He holds a B.S. in Computer Engineering from NJIT, an M.B.A. in Finance and Strategy from Columbia University, and an M.S. in Computer Science from Columbia University. His research focuses on algorithms, distributed systems, graph theory, and swarm robotics, with a particular emphasis on path optimization, reconfigurable systems, and load balancing in dynamic networks. Key research interests include shortest path algorithms in graphs, swarm robotics using the amoebot model for shape formation and locomotion, and distributed computing strategies for load balancing. His work spans theoretical computer science, combinatorial optimization, and multi-agent coordination. Recent publications explore topics like partially disjoint shortest paths, reconfigurable spanning trees, and distributed algorithms for dynamic networks. He has contributed to advancements in graph-based algorithms and their applications in real-world distributed systems. Dr. Mani has no listed scientific awards or notable grants in the provided texts. His advising and mentorship activities are not detailed here, but his educational background spans both technical and managerial disciplines, combining computer science expertise with strategic business acumen.

Alexander K. WagnerView profile

Professor

• Behavioral Economics
• Experimental Economics
• Data-Driven Decision Making
• +3 more

Alexander K. Wagner is a Professor of Behavioral Economics and Digitization at the University of Salzburg (PLUS), affiliated with the Faculty of Law, Business and Economics and the Department of Economics. He previously held positions at the University of Vienna, University of Cologne, and University of Konstanz. His research focuses on behavioral economics, experimental economics, algorithmic interactions in decision-making, and applied game theory. Education Bachelor/Master: Humboldt-University Berlin, University of Toronto PhD in Economics: Toulouse School of Economics Research Interests Wagner combines theoretical models with empirical analysis to study individual and firm decisions. Key areas include algorithmic pricing strategies, experimental platforms, and the impact of behavioral biases on economic outcomes. His recent work examines how managers respond to algorithmic recommendations in hotel pricing and explores strategic behavior in ambiguous environments. Grants & Projects Anniversary Fund Project (OeNB): "Human-Algorithm Interactions in Economic Decision Making" (2023-2027) Affiliations He is an affiliate at the Vienna Center for Experimental Economics (VCEE) and maintains active collaborations across international research networks.

Klas MarkströmView profile

Professor

• Combinatorics
• Graph Theory
• Hypergraphs
• +3 more

Klas Markström is a Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. He holds academic qualifications as a Docent and a Recognised University Teacher in Mathematics. His primary research interests include combinatorics, graph theory, hypergraphs, computational combinatorics, linear algebra, and mathematical physics. He utilizes Swedish supercomputing resources, including those at HPC2N, for his research. Markström has advised PhD students Lan Anh Pham (2019) and Joel Larsson (2018). His postdoctoral collaborators include Jakub Sliacan, Fei Song, and Victor Falgas-Ravry. His work spans theoretical contributions to combinatorics, computational methods, and applications in statistical mechanics and algorithm design. Notable research trends include explorations of Condorcet domains in social choice theory, extremal graph/hypergraph problems, and algorithmic solutions for SAT problems. His software contributions include the Umsat SAT-solver and GrafPack, a Mathematica package for graph theory. He has published extensively on topics such as Turán hypergraphs, covering designs, and computational techniques for graph enumeration. Collaborations involve interdisciplinary projects with physicists and computer scientists, reflecting his engagement with both pure and applied mathematics. His research often bridges combinatorial theory with computational experimentation, yielding impactful results in discrete mathematics and beyond.

Arunima ChhikaraView profile

Assistant Professor

• Information Systems
• Operations Management
• Supply Chain Management
• +3 more

Arunima Chhikara is an Assistant Professor in the Analytics, Information, and Operations academic area at the University of Kansas School of Business. Her research bridges Information Systems and Operations Management, focusing on social impact of technologies, retail operations, and educational technology. Her educational background includes: Ph.D. in Business Administration (Information Systems and Operations Management) from the University of Florida M.S. in Integrated Manufacturing Systems Engineering (with minors in Industrial Engineering and Business Administration) from North Carolina State University Dr. Chhikara employs experimental methods, applied econometrics, game theory, and optimization to investigate technology's societal impact, retail dynamics, and educational settings. Her work addresses supply chain challenges and human-algorithmic decision biases through interdisciplinary approaches. Her 2020-2022 publications demonstrate a focus on multi-channel retail strategies and social learning networks. These studies combine theoretical modeling with experimental validation to solve real-world problems in pricing, channel operations, and inventory liquidation within complex business ecosystems. No scientific awards were found in the provided information. Dr. Chhikara received the Networks, Electronic Commerce, and Telecommunications (NET) Institute Summer Research Grant in 2019. The source material contains no information about student advising or grant administration beyond this award. She contributes to the Analytics, Information, and Operations (AIO) research group at KU School of Business, leveraging her industry experience to connect academic research with practical supply chain applications across manufacturing, automotive, and retail sectors.

• Operations Research
• Combinatorial Optimization
• Game Theory
• +3 more

Tom Demeulemeester is a Research Fellow at the Operations Research and Statistics Research Group (ORSTAT) of KU Leuven , with a future appointment as Assistant Professor in Operations Research at Maastricht University starting August 2025. His work bridges operations research, combinatorial optimization, and computational social choice, focusing on fair resource allocation and algorithmic game theory. His research emphasizes fairness in integer programming, hedonic games, and one-sided matching. Recent work examines Rawlsian assignments and strategyproofness in multiwinner elections. Publications span journals like the European Journal of Operational Research and conferences such as MFCS and AAAI, with a focus on both theoretical and applied optimization. Key trends in his publications include randomization for fairness, stability in coalition formation, and axiomatic approaches to matching problems. All works are publicly accessible via repositories, with code available on GitHub. This aligns with his commitment to open science and reproducibility. Tom actively engages with academic communities through conference participation (EURO, MFCS, AAAI) and workshops on school choice. He will soon transition to Maastricht University after completing his PhD defense at KU Leuven in May 2024, titled Fairness through randomization: An operations research perspective .

• Health Informatics
• Fraud Detection
• Cybersecurity
• +6 more

Dr. Fletcher Lu is an Associate Professor in the Department of Information Technology at the Faculty of Business and Information Technology, Ontario Tech University. His academic journey includes a PhD in Artificial Intelligence (specializing in Machine Learning) and a Master of Mathematics in Scientific Computation, both from the University of Waterloo. He also holds a Bachelor of Math in Computer Science from the same institution. His postdoctoral research at the Canadian Institute of Chartered Accountants (CIAC) focused on insurance fraud detection. His education includes: PhD in Artificial Intelligence, University of Waterloo Master of Mathematics in Scientific Computation, University of Waterloo Bachelor of Math in Computer Science, University of Waterloo Dr. Lu’s research interests span health informatics, fraud detection (including cybercrime and insurance fraud), and the application of machine learning and numerical analysis to business and health challenges. He has developed mobile technology applications to improve youth fitness and pioneered adaptive fraud pattern analysis in health insurance data. His work intersects with cybersecurity, data analytics, and gaming, emphasizing real-world collaborations with organizations such as Manulife Financial, the Canadian Department of National Defence, and Medavie Blue Cross. His recent publications highlight trends in supply chain resilience, optimization methodologies, and disruption risk analysis. These include studies on EV infrastructure planning, stochastic distribution networks, and Benders decomposition strategies for reliable systems design. Dr. Lu has received significant awards, including: First place at CORS 2014 Practice Prize Third place at INFORMS 2013 interactive poster sessions His research has been funded by the National Science Foundation, Ontario Partnership for Innovation and Commercialization, and NSERC. He is affiliated with the Business Analytics & AI Research Group, contributing to decision-making systems and data-driven solutions for business and health sectors.

Vincent ConitzerView profile

Professor

• Game Theory
• Artificial Intelligence
• Computational Social Choice
• +7 more

Vincent Conitzer is a Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Department of Philosophy. His research intersects artificial intelligence, game theory, and ethics, focusing on computational social choice, moral decision-making in AI, and mechanism design. Primary Affiliation: Computer Science Department, Carnegie Mellon University Courtesy Appointment: Department of Philosophy, Dietrich College of Humanities and Social Sciences Labs: Affiliated with the Laboratory for Symbolic and Educational Computing His work addresses game theory , ethical AI , and computational epistemology , with publications exploring moral preference elicitation, strategic behavior in automated systems, and the intersection of economics and artificial intelligence. Recent trends in his publications include game theory , AI ethics , mechanism design , and computational social choice , reflecting his focus on aligning AI systems with human values and strategic reasoning. Vincent's research spans both theoretical and applied domains, including bioethics (e.g., kidney allocation decisions), cooperative AI, and the stability of moral preferences in algorithmic systems. He is associated with the Laboratory for Symbolic and Educational Computing and the Center for Formal Epistemology , contributing to interdisciplinary projects at the interface of computer science and philosophy.