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Nick Abel is an Honorary Associate Professor at the Fenner School of Environment & Society, Australian National University. With a distinguished international career spanning Africa and Australia, he has worked in Zimbabwe, Kenya, Ethiopia, Botswana, Somalia, Swaziland, Zambia, and Australia. His academic credentials include a Ph.D., M.Sc., and B.Sc.(Hons.), reflecting a strong foundation in environmental sciences and complex systems thinking. Abel's research program centers on the critical challenge of human adaptation to climate change, with particular focus on why adaptation efforts are insufficient to prevent environmental and social tipping points. He critically examines Australia's role as the world's third largest exporter of fossil fuels while facing escalating climate impacts, investigating how fossil fuel companies have maintained influence over public policy. His work integrates social-ecological systems theory, resilience thinking, and practical governance frameworks to address climate adaptation challenges. His publication record demonstrates a clear evolution from foundational work in rangeland management and complex adaptive systems toward urgent climate policy interventions. Early research established theoretical frameworks for understanding resilience in dryland ecosystems, while recent work focuses on transformation pathways, Indigenous engagement, and community-based adaptation strategies. A consistent thread throughout his career is the integration of diverse knowledge systems and the development of practical approaches for environmental governance in rapidly changing conditions. Abel has registered to supervise research students at ANU and participates in significant collaborative projects, including the current 'Partnering with local communities in regional Australia to increase resilience to flood events' (2022-2025). His high-impact publications, including the highly cited 'Resilience management in social-ecological systems' (1,120 Scopus citations) and 'From Metaphor to Measurement: Resilience of What to What?' (2,596 Scopus citations), demonstrate his foundational contributions to the field. Through his work with various research groups and projects, Abel has helped establish frameworks for understanding social-ecological systems and climate adaptation. His contributions to the Fenner School include advancing methodologies for integrating scientific and Indigenous knowledge systems, developing practical approaches to environmental governance, and critically examining the political economy of climate policy in Australia.

• Climate Change
• Social-Ecological Systems
• Rangeland Management
• +4 more

Hamsa Bastani is an Associate Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania, with a secondary appointment in Statistics and Data Science. She co-directs the Wharton Healthcare Analytics Lab and serves as an Associate Editor for Operations Research, M&SOM and OR Letters. Her academic journey began with summa cum laude graduation from Harvard in 2012 with an A.M. in physics and A.B. in physics and mathematics. She completed her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, followed by a Herman Goldstine postdoctoral fellowship at IBM Research. Professor Bastani's research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. Her work demonstrates particular expertise in sequential decision-making (bandits, reinforcement learning), learning from auxiliary data sources (transfer learning, meta-learning), and designing effective human-AI interfaces (interpretability, fairness). She has made significant contributions to understanding how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. Her publications reveal a strong trend toward high-impact applications of machine learning in critical societal domains. A significant portion of her recent work focuses on healthcare applications, including optimizing health supply chains in low- and middle-income countries, designing clinical trial protocols, and creating targeted public health interventions. Another major theme examines the complex relationship between humans and AI systems, particularly how AI affects learning outcomes and decision-making processes. Her work frequently bridges theoretical advances with practical implementation, as evidenced by country-scale deployments in Greece and Sierra Leone. Wagner Prize for Excellence in Operations Research Practice (2021) Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Behavioral OM Best Paper Award (2021) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Award (2019, 2020, 2021) Professor Bastani has advised numerous PhD students who have gone on to prominent positions, including Pia Ramchandani (Director of Responsible AI at PwC), Arielle Anderer (Assistant Professor at Cornell Johnson), and Kan Xu (Assistant Professor at ASU Carey). Her research has been supported by collaborations with national governments, including the Greek government where she co-designed Eva, the national-scale reinforcement learning system for targeted COVID-19 testing, and the Government of Sierra Leone where she improved patient access to essential medicines by nearly 20% via decision-aware learning. She has also conducted the first large field study deploying generative AI tutors in high school math classes. She leads the Wharton Healthcare Analytics Lab and serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, demonstrating her commitment to translating academic research into practical applications.

• Machine Learning
• Artificial Intelligence
• Healthcare Operations
• +5 more

Jon M. Peha is a Full Professor in the Department of Engineering and Public Policy and the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He serves as Founder and Director of the Center for Executive Education in Technology Policy (CEE-TP) and maintains affiliations with the CyLab Security and Privacy Institute and the Information Networking Institute. His career uniquely bridges government service (as FCC Chief Technologist, White House OSTP Assistant Director, and congressional advisor), industry leadership (as CTO for three tech startups), and academic research. Education: Ph.D. in Electrical Engineering, Stanford University (1991) M.S. in Electrical Engineering, Stanford University (1986) B.S. in Electrical Engineering and Computer Science, Brown University (1984) Peha's research spans the technical and policy dimensions of information networks, with particular focus on spectrum management, broadband Internet architecture, wireless communications, cybersecurity, and communications for emergency response. His work integrates engineering principles with economic and regulatory analysis to address real-world challenges in telecommunications infrastructure. Current projects examine connected vehicle communications, spectrum sharing models, and consumer-centric broadband labeling systems that emerged from his large-scale user studies. His research publications demonstrate consistent leadership in both technical innovation and policy analysis, with recent work addressing satellite interference mitigation, V2X communications, and pandemic-era internet performance. The breadth of his scholarship reflects his unique position at the intersection of engineering practice and policy formulation. Scientific Awards: IEEE Fellow AAAS Fellow FCC Excellence in Engineering Award IEEE Communications Society TCCN Publication Award Brown Engineering Medal AAAS Featured Science and Technology Policy Fellow (40@40) Peha actively engages with policymakers through congressional testimony, FCC consultation, and White House advisory roles. His government service has directly informed his academic work on spectrum policy, network neutrality, and public safety communications. He has supervised numerous graduate students across EPP, ECE, and INI programs, with research spanning vehicular networks, spectrum sharing, and emergency communications systems. As Director of CEE-TP, he leads executive education initiatives that bridge technical expertise and policy decision-making. His work with CyLab focuses on cybersecurity and privacy challenges in broadband systems and connected vehicles. Current projects include developing cost-effective spectrum sharing models for intelligent transportation systems and analyzing the economic implications of multi-network access architectures in 5G networks.

• Cybersecurity and Privacy
• Spectrum Management
• Wireless Networks
• +7 more

Dr. Edoardo Gallo is a University Associate Professor at the Faculty of Economics, University of Cambridge, and a member of Magdalene College. His research focuses on Experimental Economics , Networks , and Behavioural Economics . Harvard University: A.B. in Physics and Mathematics University of Oxford: M.Phil. and D.Phil. in Economics Gallo's research explores how network structures influence economic behavior, cooperation, and decision-making. His work spans topics like cybercrime disruption , social exclusion , COVID-19 behavioral responses , and unethical behavior in rivalry . Gallo's recent publications (2020-2025) analyze networked social dynamics , including cooperation mechanisms , monetary exclusion , and pandemic-induced behavioral changes . His experiments often employ interactive online platforms like UbiquityLab. Unicredit Foundation Best PhD Thesis Award CRASSH Early Career Fellowship Gallo supervises PhD students such as Rebecca Heath and Christian Höhne , with interests in online social networks and network cognition . He has received grants from the International Foundation for Research in Experimental Economics and the Isaac Newton Trust .

• Experimental Economics
• Networks
• Behavioural Economics
• +3 more

Eyal Aharoni is a Professor in the Department of Philosophy at Georgia State University's College of Arts & Sciences. He holds a Ph.D. in Psychology from the University of California, Santa Barbara (2009) and bachelor's degrees in psychology and religious studies from the same institution. Dr. Aharoni's research program bridges psychology, neuroscience, and legal studies with particular focus on: Risk models for antisocial behavior Application of neuroscience to legal contexts (neurolaw) Impact of emotion and cognitive bias on criminal, moral, legal, and political decision making Psychopathy and criminal justice outcomes His scholarly work demonstrates a consistent interdisciplinary approach to understanding the psychological and neurobiological underpinnings of criminal behavior and legal decision-making. Through experimental, longitudinal, and neuroimaging methodologies, Aharoni investigates how cognitive, behavioral, evolutionary, and neurobiological factors influence criminal justice outcomes and potential reforms. Dr. Aharoni maintains an active research laboratory and welcomes PhD students in psychology, with potential funding opportunities in neuroethics. His current research interests include ethical implications of neuroscience technologies and associated issues in cognitive neuroscience, moral psychology, legal psychology, and forensic psychology. His professional experience includes: Research Associate at the RAND Corporation Postdoctoral fellowship at The MIND Research Network for Neurodiagnostic Discovery and the University of New Mexico Psychology Research positions at the Research Center for Virtual Environments and Behavior Research positions at the Institute for Social, Behavioral, and Economic Research

• Moral Psychology
• Forensic Psychology
• Neurolaw
• +7 more

Jana Schaich Borg is an Assistant Research Professor at the Social Science Research Institute at Duke University. She specializes in integrating neuroscience, computational modeling, and emerging technologies to study social decision-making processes and their interactions with internal value representations. As a data scientist, she collaborates with interdisciplinary teams to develop novel statistical approaches for analyzing high-dimensional, multi-modal data. Research interests include Moral psychology and computational ethics Human-AI interaction in decision-making Automated social behavior analysis Neuroscience of social cognition Interdisciplinary data science education Recent publications highlight her focus on ethical AI development, moral preference modeling, and automated behavioral analysis. She teaches IDS 707: Data Visualization at Duke University.

• Neuroscience
• Computational Modeling
• Social Decision-Making
• +4 more

Magnus Liebherr is a Professor at the University of Duisburg-Essen , focusing on Technology Acceptance , Artificial Intelligence , and Sustainable Transportation . His work bridges Business Administration with Human-Computer Interaction , exploring how users adapt to emerging technologies like autonomous vehicles and AI systems. Key research areas include: Acceptance of AI applications in mobility Business model development for climate-neutral transportation Cognitive and psychological factors in technology interaction Digital media effects on adolescent well-being Trust calibration in automated systems His recent publications examine Large Language Model dependency , gamified language learning , and mental workload metrics for autonomous vehicles. While no formal awards are listed, his interdisciplinary work spans psychology , engineering , and business strategy .

• Technology Acceptance
• Artificial Intelligence
• Sustainable Transportation
• +4 more

Cathy Wu is an Associate Professor at MIT, with affiliations in the Laboratory for Information and Decision Systems (LIDS), Department of Civil and Environmental Engineering (CEE), and Institute for Data, Systems, and Society (IDSS). Her research group focuses on integrating machine learning with model-based optimization to solve complex problems in transportation systems and cyber-physical systems. Academic Leadership: Class of 1954 Career Development Associate Professor (MIT) Research Grants: NSF CAREER Award, Amazon Robotics, Mathworks, MIT Mobility Initiative, US DOT, Microsoft Research, Cintra, Symbotic Research Interests : Wu's work bridges AI and engineering challenges in transportation. Key areas include: Hybrid ML/Model-based Optimization (large neighborhood search, branch-and-cut) Sustainable Mobility (Project Greenwave, eco-driving) Multi-Agent Coordination (warehouse automation, cooperative driving) Cyber-Professional Systems (generalization in RL, transfer learning) Recent work demonstrates significant advances in eco-driving (11-22% emissions reduction), large-scale multi-agent path finding (1000+ agents), and foundational RL methods for traffic control. Her group has produced 15+ major publications since 2015, with notable media coverage in Science, Wired, and NewScientist. Selected Scientific Awards NSF CAREER Award (2023) Ole Madsen Mentoring Award (2025) IEEE ITSS WiE/YP Fellowship (2024) Harold L. Hazen Teaching Award (2022) Her lab has advised 12+ graduate students and postdocs, including: Vindula Jayawardana (PhD '24, now at Anthropic) Sirui Li (PhD '25, now at Microsoft Research) Yining Ma (Postdoc, active researcher) Zhongxia Yan (PhD '24, now at Anthropic)

• Reinforcement Learning
• Transportation Systems
• Optimization and Control
• +3 more

Daphna Renan is the Peter B. Munroe and Mary J. Munroe Professor of Law at Harvard Law School. Her work focuses on the U.S. presidency and the design of American democracy from the perspective of administrative and structural constitutional law. She has published extensively in leading law journals including the Columbia Law Review, Harvard Law Review, Stanford Law Review, Virginia Law Review, and Yale Law Journal. Renan received her B.A. in East Asian Studies from Yale College in 2000, summa cum laude. She earned an M.A. in International and Comparative Legal Studies from the University of London's School of Oriental and African Studies in 2001, and her J.D. from Yale Law School in 2004, where she served as an articles editor of the Yale Law Journal. Professor Renan's research centers on constitutional theory, particularly the relationship between judicial supremacy and democratic governance. She argues for 'a more political constitutionalism' that would distribute constitutional authority beyond the Supreme Court to democratically elected bodies. Her work explores presidential powers, the separation of powers, and how constitutional meaning is developed through political contestation rather than judicial finality. She contends that constitutional rights are 'only as secure as our politics can make them,' emphasizing the role of Congress and the executive in shaping constitutional interpretation. Renan's publications reveal a consistent focus on the provisional nature of constitutional order and the role of political institutions in constituting constitutional meaning. Her scholarship spans from historical analyses of political abolitionism's constitutional theories to contemporary critiques of judicial supremacy. A recurring theme is how unwritten norms and practices shape constitutional governance, particularly regarding presidential power. Her recent work with Nikolas Bowie examines the historical roots of judicial supremacy and advocates for congressional reclamation of constitutional authority. Professor Renan has held significant government positions, having served from 2009-2012 in the U.S. Department of Justice as a Counsel to the Deputy Attorney General and then as an Attorney Advisor in the Office of Legal Counsel. She also served on President-Elect Obama's Justice Department transition team. Prior to her government service, she clerked for Justice Ruth Bader Ginsburg of the U.S. Supreme Court and Judge Harry T. Edwards of the U.S. Court of Appeals for the District of Columbia Circuit.

• Constitutional Law
• Separation of Powers
• Presidential Powers
• +5 more

Heng Ji is a Professor at the Siebel School of Computing and Data Science , affiliated with the Department of Computer Science , Electrical and Computer Engineering Department , and multiple research labs including the Coordinated Science Laboratory and Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign. She serves as an Amazon Scholar and Founding Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE) and CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS) . B.A. and M.A. in Computational Linguistics from Tsinghua University M.S. and Ph.D. in Computer Science from New York University Her research bridges Natural Language Processing with Vision-Language Models , Knowledge-Enhanced LLMs , and AI for Science (e.g., chemical language modeling). She leads major multi-institutional projects such as DARPA ECOLE MIRACLE , KAIROS RESIN , and DEFT Tinker Bell , while advising governments (U.S. Air Force Data Analytics Expert Panel) and industry (Amazon, Google, IBM). Her work on multimodal reasoning, agent-based systems, and chemical language models (e.g., mCLM ) has been supported by NSF, DARPA, and corporate partners. Recent publications (2025) focus on LLM agents , vision-language integration , and scientific knowledge acquisition . Awards include NSF CAREER , IEEE Intelligent Systems' AI's 10 to Watch , and multiple Outstanding Paper Awards at ACL/NAACL. She advises students like Chi Han (ACL/NAACL awardee) and post-docs Xiusi Chen and Yuji Zhang , and leads the BLENDER Lab , which develops frameworks like WiNELL (Wikipedia updating) and ProteinZero (protein generation). She has also served as NAACL Secretary and Program Co-Chair for ACL-IJCNLP2022. Outstanding Paper Award at ACL2024 Two Outstanding Paper Awards at NAACL2024 Young Scientist by World Laureates Association (2023-2024) AI's 10 to Watch by IEEE (2013) NSF CAREER (2009) Google/IBM/Bosch Research Awards

• Natural Language Processing
• Data Mining
• Social Science
• +2 more

Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .

• Artificial Intelligence
• Deep Learning
• Combinatorial Optimization
• +7 more

Julia DiBenigno is a Professor of Organizational Behavior at Yale School of Management, where she has been faculty since 2016 and was promoted to full professor in 2023. She holds a courtesy appointment in Sociology and is an organizational ethnographer whose research focuses on the sociology of work, professional collaboration, upward influence, and organizational change. Her research interests include understanding how professional groups collaborate across boundaries, how frontline workers can effectively voice concerns upward in organizational hierarchies, and how organizations navigate change during crises. She specializes in qualitative, ethnographic methodologies, immersing herself in organizational settings ranging from hospitals to the U.S. Army to uncover the social dynamics that shape workplace behavior. DiBenigno's publication record includes multiple articles in top journals like Administrative Science Quarterly and Organization Science, with her most recent work examining how crises create opportunities for implementing long-resisted changes. Her research demonstrates patterns where frontline staff can successfully push through organizational resistance when they move quickly and demonstrate both short- and long-term value of their proposals. Ned Smith Rising Star Award, Organization & Management Theory Division of the Academy of Management, 2023 Thinkers50 Radar List, Class of 2022 Equity, Diversity, and Inclusion Research Award, NYU Wagner, 2021 Grigor McClelland Best Dissertation Award, EGOS, 2017 W. Richard Scott Outstanding Paper Award, American Sociological Association, 2016 As an educator, DiBenigno teaches Managing Groups & Teams and Power & Politics at Yale SOM. Her research on team dynamics has revealed insights about coordination loss, the common knowledge problem in group decision-making, and how hierarchical structures can both help and hinder effective teamwork. Her work with healthcare providers during the pandemic demonstrated how team identification reduces stress and burnout among frontline workers.

• Sociology of work and occupations
• Collaboration between professional groups
• Upward influence and voice
• +5 more

Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, where he serves as head of the Computer Science programs. He is also associated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. Cesa-Bianchi holds significant leadership roles including Board member, Fellow and co-director of the Milan unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), and membership in the prestigious Accademia Nazionale dei Lincei. He is also involved with The European Lighthouse on Secure and Safe AI (ELSA), The European Lighthouse of AI for Sustainability (ELIAS), and The FAIR foundation. Professor Cesa-Bianchi's research focuses on the theoretical foundations of machine learning, with special emphasis on sequential decision making and online learning algorithms. His work spans multiple areas including multi-armed bandit problems, regret analysis, prediction with expert advice, and learning on graphs. He has made significant contributions to understanding the theoretical limits of learning algorithms and developing efficient methods for various learning scenarios. His research has important applications in online markets, social networks, and bioinformatics. His monographs 'Prediction, Learning, and Games' and 'Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems' are considered seminal works in the field. His recent publications demonstrate continued leadership in advancing the theoretical understanding of machine learning, with 2024-2025 papers covering cooperative online learning, multitask learning, fair trade mechanisms, and refined analyses of bandit algorithms. The research shows increasing focus on practical economic applications while maintaining strong theoretical foundations. Google Research Award Xerox Foundation UAC Award Member of the Accademia Nazionale dei Lincei ELLIS Fellow Cesa-Bianchi has been deeply involved in academic service, having served as action editor for the Machine Learning Journal, IEEE Transactions on Information Theory, and the Journal of Machine Learning Research. He currently serves as associate editor for the Journal of Information and Inference and TheoretiCS. He has held leadership positions including President of the Association for Computational Learning and member of the steering committee for the EC-funded Network of Excellence PASCAL2. He was program chair of the 13th Annual Conference on Computational Learning Theory and the 13th International Conference on Algorithmic Learning Theory. He leads the Laboratory for AI and Learning Algorithms (ALGA) at the University of Milan, which focuses on theoretical and applied research in machine learning. His international collaborations are extensive, with visiting positions at UC Santa Cruz, Graz Technical University, Ecole Normale Supérieure in Paris, Google, and Microsoft Research. As an educator, he teaches advanced courses including Reinforcement Learning and Statistical Methods for Machine Learning, and has supervised numerous students through the years.

• Machine Learning
• Online Learning
• Multi-armed Bandit Problems
• +7 more

Andrew Green serves as Professor and Metcalf Chair in Environmental Law at the University of Toronto Faculty of Law, where he teaches environmental law, climate change law, administrative law, natural resources law, judicial decision-making, and international trade. His scholarly contributions span multiple legal domains with particular emphasis on the intersection of environmental regulation and administrative governance. Green's research interests encompass Environmental Law , Administrative Law , Climate Change Policy , Judicial Decision-Making , and International Trade Law . His empirical approach to legal scholarship has produced significant insights into how judges make decisions, particularly in administrative contexts, and how trade rules interact with domestic environmental regulations. His work often bridges theoretical legal analysis with practical policy implications. His recent publications reveal a strong focus on Canadian climate policy implementation, judicial behavior in high courts, and the evolving standards of review in administrative law. The research demonstrates increasing interdisciplinary integration, incorporating machine learning approaches to analyze judicial decisions and examining the systemic challenges in environmental governance. His scholarship consistently addresses the tension between regulatory effectiveness and legal accountability. Metcalf Chair in Environmental Law Past co-President of the Society for Empirical Legal Studies Past President of the Canadian Law and Economics Association Former Associate Dean at the Faculty of Law Chair of the University of Toronto Academic Appeals Committee (2009-2018) Before joining the Faculty of Law, Green practiced environmental law in Toronto for six years, handling both litigation (including prosecutions, administrative appeals, and civil actions) and transactional work. His practical experience informs his scholarly work on regulatory design and implementation. His research has influenced policy discussions on climate change, securities regulation, and administrative law reform in Canada.

• Environmental Law
• Administrative Law
• Climate Change Law
• +4 more

Anthony Niblett is a Professor and Canada Research Chair in Law, Economics, & Innovation at the University of Toronto Faculty of Law. He is an Affiliate Researcher with the Vector Institute for Artificial Intelligence and co-founder of Blue J, a startup leveraging AI for tax and legal professionals. Education: Ph.D. in Economics, Harvard University (2009) M.A. in Economics, Harvard University (2006) LL.B. (Honours), University of Melbourne (2003) B.Com. (Honours in Economics), University of Melbourne (2003) Bigelow Fellow, University of Chicago His research bridges artificial intelligence , innovation , and legal theory , with a focus on contract law , competition policy , and judicial behavior . He explores how machine learning transforms legal practice, regulatory frameworks, and judicial decisions. His work includes self-driving contracts , computational antitrust , and the personalization of law . Recent publications analyze AI’s role in legal disagreement , gender gaps in employment law , and computational merger reviews . His articles emphasize the intersection of technology , economics , and legal reform . Scientific Awards: Canada Research Chair in Law, Economics, & Innovation Bigelow Fellow, University of Chicago He serves as an Academic Advisor at the Future of Law Lab and contributes to Blue J , advancing AI applications in legal domains. His teaching includes Contract Law , Torts , and Economic Analysis of Law .

• Artificial Intelligence
• Law
• Economics
• +6 more