جستجوی استاد راهنمای دانشگاهی

Xin Guoمشاهده پروفایل

استاد

• Mathematical Finance
• Financial Engineering
• FinTech
• +۷ مورد دیگر

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

Aarti Singhمشاهده پروفایل

استاد

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
• +۷ مورد دیگر

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

Furong Huangمشاهده پروفایل

دانشیار

• Machine Learning
• Trustworthy Machine Learning
• Generative AI
• +۷ مورد دیگر

Furong Huang is an Associate Professor at the University of Maryland's Department of Computer Science, with affiliations at the Institute for Advanced Computer Studies, Center for Machine Learning, Maryland Robotics Center, and Applied Mathematics, Statistics, and Scientific Computation Program. Her research bridges trustworthy machine learning, sequential decision-making, and foundation models for robotics, emphasizing reliability, interpretability, and ethical standards. Research Interests: Trustworthy AI Generative AI Reinforcement Learning AI Security Algorithmic Fairness Foundation Models for Robotics Recent Publications span leading conferences (NeurIPS, ICML, ICLR, CVPR) and journals, focusing on: Robustness in Vision-Language Systems Trustworthy Generative AI Foundation Models for Sequential Decision-Making AI Security and Watermarking Scientific Awards MIT TR35 Innovator Under 35 (Asia Pacific 2022) Best Paper Award, AdvML Frontier Workshop, NeurIPS 2024 NSF NAIRR Pilot Awardee Microsoft Accelerate Foundation Models Research Award (2023) JP Morgan Faculty Research Awards (2019–2022) Advising and Grants : Her lab has graduated students to roles at OpenAI, Google, Meta, and Netflix. Research funded by DARPA, NSF, ONR, AFOSR, and industry partners like Microsoft, Adobe, and Capital One. Labs & Teams : Leads research groups focused on Trustworthy AI and Robotics at the University of Maryland, collaborating with the Maryland Robotics Center and Applied Mathematics Program.

Bo Daiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Machine Learning
• Data-Driven Decision Making
• +۳ مورد دیگر

Bo Dai is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology, and a Staff Research Scientist at Google DeepMind. His research focuses on Agent AI, Generative Models, and Representation Learning, aiming to create decision-making agents through world modeling. He holds a Ph.D. from Georgia Tech (2013–2018) and previously worked at Google Brain. Dai has authored numerous influential papers in top conferences like NeurIPS, ICML, and ICLR, and received the AISTATS Best Paper Award (2016). Education: Ph.D., School of Computational Science and Engineering, Georgia Tech (2013–2018) Research Interests: Reinforcement Learning and Representation Learning for decision-making agents Generative Models and their integration with Agent AI Provable and scalable algorithms for real-world applications His work bridges theory and practice, emphasizing spectral representations and provable guarantees in complex systems. Key Article Trends: His recent work emphasizes scalable spectral methods for multi-agent systems, diffusion policies, and representation-based techniques in reinforcement learning. He also explores LLM alignment and sim-to-real transfer learning. Awards: AISTATS Best Paper Award (2016) NeurIPS Workshop Best Paper (2017) Ross Fellowship (2011–2012) Advising & Grants: Supervises 6 current Ph.D. and M.S. students. Active in organizing workshops on reinforcement learning and serves as an Area Chair for top conferences. Labs & Software: Leads development of Representation-based Reinforcement Learning and Repr-Control toolboxes for nonlinear control and stochastic systems.

Anca Draganمشاهده پروفایل

دانشیار

• Human-AI Interaction
• Human-Robot Interaction
• AI Safety and Alignment
• +۵ مورد دیگر

Anca Dragan is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she runs the InterACT Lab focused on algorithms for human-AI and human-robot interaction. Currently on leave from Berkeley, she leads AI Safety and Alignment at Google DeepMind, overseeing safety for Gemini models and preparing for future advancements. Dragan has been a co-PI of the Center for Human-Compatible AI and served on the steering committee for the Berkeley AI Research (BAIR) Lab. B.Sc. in Computer Science from Jacobs University Bremen, Germany Ph.D. from Carnegie Mellon University Dr. Dragan's research focuses on enabling AI agents to work effectively with, around, and in support of people. Her work bridges robotics, machine learning, and game theory to create systems that better understand human preferences and coordinate with users. Key areas include AI alignment (ensuring AI does what people actually want), learning reward functions from diverse human feedback forms, and developing algorithms for human-AI collaboration across domains like autonomous vehicles, brain-machine interfaces, and recommender systems. Her research emphasizes maintaining uncertainty about human preferences and accounting for the plurality of human values. Dr. Dragan's recent publications reveal a strong focus on addressing fundamental challenges in AI safety and alignment. Her work spans theoretical foundations of reward learning, practical implementations for human-AI coordination, and critical examinations of limitations in current approaches. There's a clear trajectory toward more robust, safe, and value-aligned AI systems that can handle complex human preferences while avoiding both present-day harms and potential catastrophic risks. IEEE RAS Early Academic Career Award in Robotics and Automation (2021) McEntyre Award for Excellence in Teaching (2020) PECASE (Presidential Early Career Award for Science and Engineering) (2019) Sloan Fellowship (2018) NSF CAREER Award (2017) Okawa Foundation Award (2017) MIT Tech Review 35 Innovators Under 35 (2017) Multiple best paper awards at top robotics and AI conferences Dr. Dragan has mentored numerous successful students who have gone on to faculty positions at MIT, Stanford, CMU, and Princeton, as well as industry roles at DeepMind, Waymo, and Meta. Her advising philosophy emphasizes both technical rigor and consideration of broader societal impacts. She has secured significant research funding including NSF CAREER, ONR Young Investigator, and Okawa Foundation awards, supporting work on human-AI interaction and alignment. Dragan has also consulted for Waymo for six years, helping develop roadmaps for deploying increasingly learning-based safety-critical systems. Dr. Dragan leads the InterACT Lab at UC Berkeley, which has produced influential work on Cooperative Inverse Reinforcement Learning, Inverse Reward Design, and other foundational concepts in human-AI interaction. The lab's research has significantly shaped the field of AI alignment, with applications spanning autonomous vehicles that coordinate with human drivers, brain-machine interfaces that adapt to user needs, and language models that better understand human preferences. Current work focuses on scaling safety approaches as AI capabilities advance, ensuring alignment keeps pace with technological progress.

• Operations Research
• Machine Learning
• Optimization under Uncertainty
• +۴ مورد دیگر

Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.

Andrew Radinمشاهده پروفایل

دانشگاهی

• European Security
• Military Strategy
• State Building
• +۱۲ مورد دیگر

Andrew Radin is a Senior Political Scientist at the RAND Corporation and Associate Director of the Master of National Security Policy Program at the RAND School of Public Policy. His work focuses on European security, military activities in space, and state-building, with expertise in NATO, Russia, Ukraine, and Afghanistan. Ph.D. in Political Science, MIT B.A. in Political Science and Mathematics, University of Chicago Postdoctoral Fellow, Harvard Kennedy School Postdoctoral Fellow, University of Southern California His research centers on European and Eurasian security , military strategy , and state-building in weak states . He emphasizes the role of local politics in institution-building and has conducted case studies in Bosnia, Kosovo, Iraq, Timor-Leste, and Ukraine. His work explores hybrid warfare, deterrence, security sector reform, and U.S. policy toward Russia and China. His recent publications reveal a strong trend in analyzing space as a domain of military competition , NATO deterrence strategies , and Russian gray-zone aggression . Topics frequently include escalation dynamics, allied cooperation, and the future of the liberal international order. His research combines policy relevance with academic rigor. Scientific Contributions and Recognition: Author of Institution Building in Weak States: The Primacy of Local Politics (Georgetown University Press) Published in Security Studies , Journal of Conflict Resolution , Washington Quarterly , and War on the Rocks Contributor to high-impact RAND reports on Ukraine, NATO, Russia, and international order Andrew Radin has advised on national security policy, including serving as Country Director for Afghanistan in the Office of the Under Secretary of Defense for Policy. He previously taught as an adjunct professor at Georgetown University. He has not received mention of specific grants or student advisees, but his leadership in the Master of National Security Policy program suggests mentorship and program development responsibilities. He is active in shaping U.S. defense and foreign policy through research and commentary. Laboratories, Teams, and Research Groups: He is affiliated with the RAND Corporation’s international security research division and contributes to projects on global competition, military strategy, and political warfare. His work is often collaborative, involving multidisciplinary teams focused on defense policy and strategic stability.

David Stillwellمشاهده پروفایل

استاد

• Psychometrics
• Computational Social Science
• Big Data Analytics
• +۳ مورد دیگر

David Stillwell serves as Professor of Computational Social Science at Cambridge Judge Business School and Academic Director of The Psychometrics Centre, University of Cambridge. His research leverages big data to understand human psychology and behavior, with significant contributions in personality prediction from digital footprints and personalized advertising applications. His educational background includes: BSc in Psychology from the University of Nottingham (2007) MSc in Research Methods from the University of Nottingham (2008) PhD in Decision Making from the University of Nottingham (2012) Dr. Stillwell's research centers on computational social science and psychometrics, pioneering the myPersonality Facebook application that collected data from over 6 million users. His work demonstrates computers can predict personality as accurately as spouses, reveals psychological targeting's advertising effectiveness, and establishes links between personality-matched spending and life satisfaction. He also explores linguistic honesty markers through profanity analysis and personality-based dating patterns. His recent publications (2019-2025) reveal a strong trajectory toward AI evaluation using psychometric frameworks, particularly in medical and general-purpose AI assessment. Key themes include fairness metrics in language models, crisis emotional responses through social media, and computational personality recognition - demonstrating consistent innovation at the psychology-technology intersection. He has received significant recognition: Top 10 most influential papers of 2013 by Altmetric Nieman Journalism Lab highlight of 2013 Named in 'top 30 thinkers under 30' by Pacific Standard Magazine Dr. Stillwell maintains active industry engagement through consultancy with Barclays, Hilton, and Ubisoft on projects spanning computer-adaptive testing systems to interactive experiences like Predictive World for Watch Dogs 2. His policy impact is substantial, with citations by the European Data Protection Supervisor, World Bank, and multiple national governments, leading to speaking engagements at the European Parliament and Bank of England. As Academic Director of The Psychometrics Centre, he leads a global hub for advancing psychological assessment through innovative methods including Concerto open-source software, collaborating with organizations ranging from the European Commission to major corporations on psychometric applications in people analytics and digital behavior.

Zhun Dengمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Theoretical Computer Science
• +۴ مورد دیگر

Zhun Deng is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges machine learning, statistics, and theoretical computer science, focusing on rigorous frameworks for responsible AI systems. He previously held postdoctoral positions at Columbia University and completed his Ph.D. at Harvard's Theory of Computation group under Cynthia Dwork. Ph.D. in Computer Science, Harvard University (2022) B.Sc. in Mathematics, Chu Kochen Honors College, Zhejiang University His research spans theoretical foundations of machine learning, including: Quantile-based risk control and conformal prediction Fairness guarantees in algorithmic decision-making Uncertainty quantification for LLMs Copyright frameworks for generative AI Physics-informed hybrid models Multi-agent reinforcement learning with constraints Recent work analyzes LLM alignment through distribution-free methods (ICML 2025), explores performativity challenges (ICML 2025), and develops calibration techniques (ICLR 2024). Collaborations include research interns from Stanford, MIT, and NYU. Students in his group include: Ruomeng Ding (Ph.D., UNC) Xiaowei Yin (Ph.D., UNC) Kaicheng Zhang (Ph.D., UNC)

• Decision Analysis
• Behavioral Decision-Making
• Neuroeconomics
• +۵ مورد دیگر

Franz Heukamp is the Director General of IESE Business School at the University of Navarra and Professor in the Department of Decision Analysis. He holds the Antonio Valero Chair of Business Administration since 2016 and previously served as Vice Dean of the MBA program and Secretary General of IESE. His academic background includes a Ph.D. in Engineering from the Massachusetts Institute of Technology, an Ingénieur Civil des Ponts et Chaussées from Ecole Nationale des Ponts et Chaussées in Paris, and a Diplom-Ingenieur from Technische Universität München. Professor Heukamp's research focuses on behavioral decision-making with a special interest in neuroeconomics, examining how people make decisions under uncertainty, particularly regarding time-probability trade-offs. His work bridges business decision theory with neuroscience, as evidenced by his publications in journals like Management Science , European Neuropsychopharmacology , and British Journal of Pharmacology . In recent years, he has expanded his research interests to include artificial intelligence applications in management and sustainability. His recent scholarly output shows a progression from fundamental decision theory research to practical applications of AI in business leadership and management education. He has published extensively on risk preferences, intuition in decision making, and the neurological basis of economic choices, with increasing focus on how digital transformation and AI are reshaping management practices. As Director General, Heukamp has championed several strategic initiatives including internationalization efforts across North America, Latin America, and Central Europe, development of new degree programs like the Master in Management and Munich Executive MBA, and integration of digital learning technologies while maintaining the school's commitment to in-person educational excellence. Professor Heukamp is an active member of both the Institute for Operations Research and the Management Sciences and the Neuroeconomics Society, reflecting his interdisciplinary approach to business education and research. His leadership prioritizes supporting faculty research, advancing lifelong learning solutions for executives, and maintaining IESE's mission of service to society through academic innovation.

Felipe Csaszarمشاهده پروفایل

استاد

• Strategic Management
• Artificial Intelligence
• Organizational Behavior
• +۳ مورد دیگر

Felipe Csaszar is a Professor of Strategy and Chair of the Strategy Department at the University of Michigan's Ross School of Business. His research focuses on decision structures' impact on innovation, financial performance, and social outcomes, with particular attention to cognitive frameworks, organizational processes, and AI's role in decision-making. He holds a PhD and MA from the Wharton School, University of Pennsylvania. Education: PhD in Strategy, University of Pennsylvania (2009) MA in Strategy, University of Pennsylvania (2007) Research Interests: Strategic decision-making under AI integration Cognitive and structural drivers of innovation Organizational decision processes and design Formal modeling and empirical strategy research Editorial Roles: Senior Editor, Strategy Science and Management Science Former Editor, Organization Science Co-editor, Handbook of AI and Strategy Professional Experience: Prior role: Assistant Professor at INSEAD Previous career: CEO of an internet startup and Head of Research at an asset management firm Labs/Teams: Leading the Strategy Science division at INFORMS Co-chair of the SMS Behavioral Strategy division

Stefanos Zeniosمشاهده پروفایل

استاد

• Operations Research
• Innovation
• Entrepreneurship
• +۳ مورد دیگر

Stefanos Zenios is Professor at Stanford Graduate School of Business with dual appointments in Entrepreneurship and Operations, Information & Technology . He directs Stanford GSB’s Center for Entrepreneurial Studies and co-directs the Doerr School of Sustainability ’s Program in Ecopreneurship. His Startup Garage course has launched companies like DoorDash while alumni have raised over $3B in venture capital. He co-authored the foundational Biodesign textbook and previously served as Editor-in-Chief of Operations Research . PhD, MIT Operations Research (1996) MA, University of Cambridge (1996) BA, University of Cambridge (1992) Research Focus Dr. Zenios’ work bridges operations research and innovation management , with significant contributions to: Healthcare operations : Administrative cost reduction, organ allocation optimization, and clinical workflow improvements Entrepreneurship : Venture creation frameworks and experiential education Ecopreneurship : Sustainable business model innovation His Precedents Thinking framework (HBR 2025) has gained global recognition for solving systemic inefficiencies. Current research includes the biennial Search Fund Study with Peter Kelly. Scientific Recognition INFORMS Fellow Multiple Best Paper Awards, INFORMS CAREER Award, National Science Foundation (2000) Dhirubhai Ambani Faculty Fellow (2017–2020) George E. Nicholson Award (1997) He has received extensive media coverage in Fast Company , Time Magazine , and Stanford Business . His research has been featured in over 150 press mentions including JAMA , Harvard Business Review , and Health Affairs .

Joel Gohمشاهده پروفایل

دانشیار

• Health services and systems
• Transportation, logistics and supply chains
• Operations research
• +۶ مورد دیگر

Joel Goh is Associate Professor at the Department of Analytics and Operations, NUS Business School, National University of Singapore. He serves as Director of the J.Y. Pillay Comparative Asia Research Centre (under NUS Global Asia Institute) and PhD Program Director at the Institute of Operations Research and Analytics (IORA). Previously, he was Assistant Professor at Harvard Business School (2014-2017) and Visiting Scholar (2017-2022). BSc, MSc, PhD in Operations, Information, and Technology from Stanford University His research focuses on healthcare analytics (preventing health conditions, hospital operations, frailty assessment), supply chain analytics (digital business models, platform leakage), and service platform operations (hospital-at-home programs, incentive design). He co-created the Robust Optimization Made Easy (ROME) software. Recent publications analyze workplace psychological safety (2024), hospital-at-home models (2024), and platform leakage dynamics (2023). His work spans 18+ journals with 740+ citations for burnout cost studies (2022) and 606+ citations for physician well-being research (2017). Teaching Honors : 2023: Best MBA Teaching & Skinner Innovation Award 2021: NUS Annual Teaching Excellence Award 2020: Early Career Research Excellence Award & 40 Under 40 Best MBA Professors Advising & Grants : Served as PhD Program Director. Received NUS Start-Up Grant R-314-000-110-133 (2021) and Humanities & Social Sciences Fellowship (2021). Editorial roles include Associate Editor at Management Science , Manufacturing & Service Operations Management , and Senior Editor at Production and Operations Management .

• Financial Contracting
• Corporate Disclosure
• Corporate Investment Behavior
• +۵ مورد دیگر

Clemens Otto is a Full-time Associate Professor of Finance at the Lee Kong Chian School of Business, Singapore Management University. He holds a Ph.D. (Finance) from London Business School (2012), an M.Res. (Finance) from London Business School (2009), and a CEMS Master (International Management) and Dipl.-Kfm. (Business Administration) from the University of Cologne and HEC Paris (2006). His research focuses on Financial Contracting, Corporate Disclosure Practices, Behavioral Finance, and Strategic Investment Decisions. Key research interests include analyzing how disclosure policies impact capital markets, the behavioral drivers of corporate investment, and the effects of regulatory frameworks on firm behavior. His work integrates theoretical finance with empirical evidence from supply chains, healthcare disclosures, and debt markets. Notable awards include the Michael J. Brennan Award (2022) and the SAC Capital Ph.D. Award (2012). His publications span journals such as the Review of Financial Studies , Management Science , and The Accounting Review . Current research explores disclosure externalities in supply chains and the strategic implications of peer disclosures in clinical trials. Prof. Otto has been affiliated with SMU since 2017, advancing through roles from Assistant Professor (2017–2021) to his current position. His expertise aligns with SMU’s strategic priorities in Asia-focused finance and sustainable living through informed corporate governance frameworks.

• Behavioral Economics
• Experimental Economics
• Decision Theory
• +۳ مورد دیگر

Adam Zylbersztejn is an Assistant Professor (Maître de conférences HDR) at Université Lumière Lyon 2 and affiliated with GATE Lyon Saint-Etienne, a research group in economics. He holds a PhD from the Paris School of Economics (2013) and previously conducted postdoctoral research at WU Wien (Vienna University of Economics and Business). His research focuses on behavioral economics using experimental methods to study individual and interactive decision processes, particularly in contexts involving trust, strategic uncertainty, and cultural variability. Key research areas include behavioral game theory, experimental finance, and the impact of nonverbal communication in economic interactions. His work has been published in top journals such as Journal of Economic Psychology , Games and Economic Behavior , and Experimental Economics . Recent studies examine topics like anonymity effects in charitable giving, trust in market exchanges under oath, and cognitive processes in asset markets using eye-tracking technology. His ongoing projects include research on risk-incentive tradeoffs and ambiguity framing in decision-making. While no formal academic awards are listed, his work has received press coverage in outlets like Le Monde and CORTEX Mag . He collaborates widely with institutions such as GATE-LSE and the Paris School of Economics, contributing to both theoretical and applied economic inquiry.