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John P. O'DohertyView profile

Professor

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +3 more

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

Laura ToniView profile

Associate Professor

• Large-scale signal processing for machine learning
• Graph signal processing
• Reinforcement learning
• +9 more

Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.

Cleotilde GonzalezView profile

Research Professor

• Decision Science
• Cognitive Psychology
• Human-Computer Interaction
• +5 more

Cleotilde (Coty) Gonzalez is a Research Professor of Decision Sciences at Carnegie Mellon University, with primary affiliation in the Department of Social and Decision Sciences (SDS). She serves as the Founding Director of the Dynamic Decision Making Laboratory (DDMLab) and Research Co-Director of the NSF National Institute for AI for Societal Decision Making (AI-SDM). Her extensive academic affiliations include the Security and Privacy Institute (CyLab), the Societal Computing program in the Software and Societal Systems Department (S3D), the Human-Computer Interaction Institute (HCII) in the School of Computer Science, and the Center for Behavioral Decision Research (CBDR) and Center for Neural Basis of Cognition (CNBC). Dr. Gonzalez holds a Ph.D. in Management Information Systems and has developed Instance-Based Learning Theory (IBLT), a significant contribution to cognitive science that explains how people make decisions based on past experiences. Her research spans experimental studies and computational modeling of cognitive processes in dynamic decision environments, with applications in cybersecurity, human-machine teaming, and societal decision making. Her recent publications reveal a strong focus on human-AI collaboration, collective intelligence, cybersecurity, and cognitive modeling. The research trends show increasing integration of AI systems with human decision processes, particularly examining how humans and AI can complement each other in complex decision environments. Her work increasingly addresses cybersecurity challenges through behavioral science perspectives, exploring how cognitive models can improve defense mechanisms against social engineering attacks. Lifetime Fellow of the Cognitive Science Society Lifetime Fellow of the Human Factors and Ergonomics Society Member of the Governing Board of the Cognitive Science Society Member at Large of the Policy Council of the System Dynamics Society Committee member of the National Academies Division Committee for the Behavioral and Social Sciences and Education Dr. Gonzalez has mentored over 50 post-doctoral fellows and doctoral students, with many going on to successful careers in academia, government, and industry. Her research has been supported by major collaborative efforts including Collaborative Research Alliances (CRA) and Multi-University Research Initiative grants from the Army Research Laboratories (ARL) and Army Research Office (ARO), as well as projects with the Defense Advanced Research Projects Agency (DARPA). She directs the Dynamic Decision Making Laboratory, which conducts research involving laboratory experiments and cognitive computational models to derive theoretical conclusions about dynamic decision making and develop applications for societal problems.

Joe PatonView profile

Professor

• Neural Mechanisms of Time
• Interval Timing
• Rodent Neurophysiology
• +5 more

Joe Paton is a Professor and Principal Investigator at the Champalimaud Neuroscience Programme, Champalimaud Foundation in Lisbon, Portugal. He leads the Paton Lab which focuses on understanding how animals determine which environmental cues are predictive of behaviorally relevant events, known as the credit assignment problem. His research combines behavioral experiments with neurophysiological recordings in rodents to investigate neural mechanisms of time perception and decision making. Dr. Paton's research interests center on interval timing, temporal processing in the brain, and the neural basis of learning. His work particularly examines how the striatum and dopamine systems contribute to time perception and how animals solve the credit assignment problem through statistical inference in the time domain. His lab employs advanced techniques including optogenetics, neural recordings, and computational modeling to address these questions. Analysis of Dr. Paton's recent publications reveals a strong focus on striatal function in timing processes, with particular attention to how neural populations encode temporal information. His work bridges behavioral neuroscience with computational approaches, demonstrating how timing mechanisms influence decision making and learning processes. The research spans multiple levels from cellular mechanisms to behavioral outputs. Midbrain dopamine neurons control judgment of time (2016) Striatal dynamics explain duration judgments (2015) A Scalable Population Code for Time in the Striatum (2015) The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions (2018) Dr. Paton has mentored numerous PhD students and postdoctoral researchers through the INDP (International Neuroscience Doctoral Program) and supervises a diverse team including research technicians, postdocs, and students. His lab has contributed significantly to understanding the neural basis of time perception and its role in learning and decision making. The Paton Lab also develops experimental tools and frameworks like Bonsai for behavioral neuroscience research.

Lawrence M. AusubelView profile

Professor

• Microeconomic Theory
• Game Theory
• Economics of Asymmetric Information
• +4 more

Lawrence M. Ausubel is a Professor of Economics in the Department of Economics at the University of Maryland. He received his PhD in Economics (1984), Master of Legal Studies, and MS in Mathematics from Stanford University. His research focuses on microeconomic theory, game theory, and the economics of asymmetric information, with applications to auction theory and market design. PhD in Economics (Stanford University, 1984) Master of Legal Studies (Stanford Law School) MS in Mathematics (Stanford University) AB in Mathematics (Princeton University) A leading expert in auction theory, Ausubel has made significant contributions to dynamic auctions, combinatorial bidding, and market mechanisms. His work includes developing the efficient ascending-bid auction format (known as the 'Ausubel auction'), analyzing durable goods monopoly, and studying bargaining under incomplete information. He has also explored credit card market dynamics, financial rescue strategies, and electricity market design. Recent research involves cryptographic choice mechanisms, core-selecting auctions with incomplete information, and iterative pricing models. His publications span journals like Review of Economic Studies , American Economic Review , and Econometrica , with patents in auction technology and applications in telecommunications, energy, and finance. 23 U.S. patents related to auction technology Testified before U.S. Congress on credit card markets Founded Power Auctions LLC (2003) Co-inventor of auction formats used by FCC, ISED Canada, and ACMA

Sebastian OtteView profile

Professor

• Artificial Intelligence
• Machine Learning
• Recurrent Neural Networks
• +12 more

Sebastian Otte is a Professor at the Institute for Robotics and Cognitive Systems at the University of Lübeck, where he leads the Adaptive AI research group. Prior to this, he was a postdoctoral researcher and substitute professor at the University of Tübingen, contributing significantly to the Cognitive Modeling and Distributed Intelligence groups. University of Lübeck, Professor (since 2023) University of Tübingen, Postdoc and Substitute Professor (2016–2023) Centrum Wiskunde & Informatica (CWI), Humboldt Fellow (2022–2023) His research focuses on recurrent and spiking neural networks, bio-inspired computing, efficient learning, and adaptive AI systems. He explores how neural models can perform online learning, handle multiple time scales, and solve complex cognitive tasks such as binding, prediction, and motor control. His work bridges machine learning with cognitive science and robotics. The recent publications show a strong trend toward physics-informed neural networks, finite volume methods for PDE modeling, and explainable AI via counterfactual reasoning. His work integrates deep learning with scientific computing, emphasizing robust, interpretable, and efficient models for real-world applications. Scientific awards include: Best Paper Award at ICANN 2019 Humboldt Research Fellowship Editor's Highlight in Water Resources Research He has supervised over 70 bachelor’s and master’s theses and actively mentors students in areas such as spiking neural networks, reservoir computing, and robotics. His teaching includes core computer science and advanced neural network courses. He has been involved in research projects with industry partners like Daimler AG and Mercedes-Benz AG. Otte leads the Adaptive AI research group, which focuses on developing next-generation AI systems that learn efficiently, adapt dynamically, and model complex cognitive and physical processes using biologically inspired architectures.

• Property Law
• Eminent Domain
• Housing Law
• +6 more

Carol Necole Brown is a Professor of Law at the University of Richmond School of Law , with a focus on property law, housing policy, and civil rights frameworks. Her scholarship bridges legal doctrine with socioeconomic impacts, addressing issues like gentrification, eminent domain, and subprime lending. Key affiliations: University of Richmond School of Law Research Interests: Dr. Brown specializes in Property Law , Eminent Domain , and Housing Equity . Her work explores intersections between governance , social justice , and land use through empirical and doctrinal lenses. Her recent publications (2025) examine civil rights defenses of gentrification and urban reinvestment strategies , while earlier works (2016–2014) analyze Kelo dissent , Lucas rule , and land development policies. Recurring themes include fair housing , zoning , and tax credit programs . Publications include co-authored textbooks like Landlord & Tenant Law in a Nutshell and Sum and Substance Quick Review on Property , alongside 17 scholarly papers on SSRN with 2,410 downloads and 7 citations. Notable collaborations include work with Dwight H. Merriam (land use) and Serena Maria Williams (adverse possession).

Ruben PortuguesView profile

Professor

• Neuroscience
• Neural Circuits
• Sensorimotor Integration
• +3 more

Ruben Portugues is a Professor at the Technical University of Munich (TUM) within the Institute of Neuroscience, part of the TUM School of Medicine. His academic journey includes a PhD in theoretical physics from the University of Cambridge, followed by postdoctoral research in neurobiology at Harvard University and the Max Planck Institute of Neurobiology. He leads the Focus Group on 'Brain Circuit Function and Dysfunction.' His research focuses on understanding how the brain encodes sensory information and translates it into motor actions. Using zebrafish as a model organism, he employs advanced techniques like whole-brain calcium imaging, electrophysiology, and computational modeling to study sensorimotor integration and neural circuit dynamics. Key projects involve mapping neural activity during behaviors, analyzing cerebellar circuits, and investigating mechanisms of learning and adaptation. Portugues has contributed to breakthroughs in understanding rheotaxis, optomotor responses, and the neural basis of decision-making. His lab develops tools like the stytra software for behavioral experiments and collaborates on anatomical atlases using brainrender . His work bridges theoretical physics and experimental neuroscience, emphasizing interdisciplinary approaches. He holds the Rudolf Mößbauer Tenure Track Fellowship and has mentored numerous researchers. His lab is affiliated with the TUM Institute for Advanced Study (TUM-IAS), contributing to initiatives in advanced computation and neuroscience.

Stephen SpurrView profile

Professor

• Labor Economics
• Economic Analysis of Law
• Health Economics
• +3 more

Stephen Spurr is a Professor of Economics at Wayne State University’s College of Liberal Arts and Sciences. His research focuses on labor economics and the economic analysis of law, with notable contributions to legal economics, healthcare policy, and tort law. He holds a Ph.D. in Economics from the University of Chicago (1986), an LL.M. in Taxation from New York University (1972), a J.D. from the University of Michigan Law School (1969), and an A.B. from Oberlin College (1966). Dr. Spurr’s work bridges economic theory and legal practice, examining topics such as subrogation in tort litigation, medical malpractice, bankruptcy law, and occupational competition in healthcare. His recent articles address subrogation’s impact on litigation outcomes and deterrence, while older works include foundational analyses of lawyer referral systems and gender dynamics in the legal profession. Education: Ph.D. in Economics, University of Chicago (1986) LL.M. in Taxation, New York University (1972) J.D., University of Michigan Law School (1969) A.B., Oberlin College (1966) His courses include Economic Analysis of Law , Advanced Economic Analysis of Law , and Economics of Human Resources . While no specific grants or labs are listed, his extensive publications reflect a sustained engagement with policy-relevant economic research.

Regino CriadoView profile

Professor

• Complex Networks
• Discrete Mathematics
• Cybersecurity
• +4 more

Dr. Regino Criado is Full Professor of Applied Mathematics at Rey Juan Carlos University (URJC), where he has held academic positions since June 2000. He currently serves as Academic Director of the Data, Complex Networks and Cybersecurity Sciences Institute (since March 2017) and was elected to the Academia Europaea in May 2023. His career spans European research projects (ESPRIT II/III) and interdisciplinary collaborations at the intersection of mathematics, computer science, and cybersecurity. His research focuses on Complex Networks , including hyper-networks, multiplex networks, and mesoscale structures, with applications to cybersecurity, intentional risk management, and synchronization phenomena. Key innovations include: A game theory-based intentional risk model integrating accessibility, anonymity, and value metrics Hybrid network mining algorithms for credit card fraud detection PageRank extensions to multiplex networks Framework for discrete resilience analysis in technological systems His work on the 2014 Physics Reports monograph The structure and dynamics of multilayer networks (3,500+ citations) established foundational concepts in multilayer network theory. Current research explores linguistic pattern analysis through multilayer hypergraphs for automatic text summarization. Member of Academia Europaea (2023) Chaos Journal Best Paper Award (2011) Academic Entrepeneurs Prize (2002) EUROPA-1992 Prize (1992) As director of the DCNC Institute (2017-present) and BBVA-URJC Chair (2017-2018), he has led major academic-industry collaborations. His 180+ publications (6,500+ citations) demonstrate sustained impact across network science, applied mathematics, and cybersecurity.

Jie NingView profile

Associate Professor

• Operations Management
• Supply Chain Management
• Operations-Finance Interface
• +3 more

Jie Ning is an Associate Professor in the Department of Operations at the Weatherhead School of Management, Case Western Reserve University. She joined the faculty in 2013 after completing her PhD in Industrial and Operations Engineering at the University of Michigan. PhD in Industrial and Operations Engineering, University of Michigan (2013) MS in Physics, University of Michigan (2008) BSE in Industrial and Operations Engineering, University of Michigan (2008) BS in Physics, University of Science and Technology of China (2006) Her research focuses on the intersection of operations management with finance and economics, particularly examining how financial constraints affect operational decisions and interfirm interactions. Key areas include trade credit strategies, risk-averse contracting, stochastic optimization, and empirical analysis of supply chain dynamics. Recent publications (2023–2025) demonstrate her emphasis on financial instruments in supply chains (e.g., expedited payments, blockchain-based debt monitoring), asymmetric innovation capabilities in sourcing decisions, and trade credit mechanisms under competition. Earlier works (2017–2021) explore theoretical foundations of affine Markov decision processes and practical applications in capacity management. Weatherhead Emerging Impact Award (2021) Weatherhead Faculty Scholar (2021) Lewis-Progressive Fellow (2020) She teaches courses in Operations Research and Supply Chain Management, including Operations Management I/II, Strategic Sourcing, and Supply Chain Risk Management. Her work with Fortune 500 companies combines empirical and theoretical research to address real-world operational challenges.

Mingfei SunView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Deep Reinforcement Learning
• +4 more

Mingfei Sun is a Lecturer (Assistant Professor) in the Department of Computer Science at the University of Manchester, UK. His research focuses on artificial intelligence, particularly machine learning techniques including deep reinforcement learning and generative models, with applications in robotics, multi-agent systems, and video games. He holds a PhD in Computer Science & Engineering from the Hong Kong University of Science and Technology (HKUST), where he received the Top Research Postgraduate Award, and a B.S. in Electronic Engineering & Information Science from the University of Science and Technology of China (USTC). Dr. Sun's work spans theoretical advancements and practical applications, including contributions to the SMACV2 benchmark for multi-agent reinforcement learning and the development of the LoKO optimizer for large models. He has secured significant funding, such as the EPSRC Doctoral Landscape Award and the TICM Collaborative R&D program on reinforcement learning for automated system calibration. His research has been recognized through awards like the AAMAS Best Paper Award (2023) and the AAAI-20 Studentship (2020). He has held research positions at Microsoft Research Cambridge, the University of Oxford, and Tencent AI Lab. His academic service includes roles as a Senior Program Committee member for AAMAS 2024 and a reviewer for top conferences like ICML, NeurIPS, and AAAI. His GitHub repository demonstrates contributions to projects like DeepMimic_mujoco, focusing on reinforcement learning for humanoid control using Mujoco simulations.

Paul RobinetteView profile

Associate Professor

• Human-Robot Interaction
• Trust in Autonomous Systems
• Multi-Robot Coordination
• +4 more

Paul Robinette serves as Associate Professor and Associate Chair for Master of Science Programs in the Electrical and Computer Engineering Department at the Francis College of Engineering, University of Massachusetts Lowell. He maintains significant affiliations with the Printed Electronics Research Collaborative (PERC) and the Raytheon UMass Lowell Research Institute (RURI), where his robotics research integrates printed electronics for field-deployable systems. His office resides in Ball Hall's third floor, with primary contact at Paul_Robinette@uml.edu. Dr. Robinette earned his Ph.D. from the Georgia Institute of Technology, establishing his foundation in robotics and human-systems engineering. This academic background directly informs his experimental approach to human-robot interaction challenges. His research centers on trust dynamics in human-robot teams, particularly examining how moral violations versus performance failures impact trust retention. He investigates drone appearance effects on trust calibration, multi-robot coordination during search and rescue, and trust recovery mechanisms after system malfunctions. His experimental work spans marine environments using platforms like Robowhaler, subterranean scenarios, and emergency evacuation simulations—consistently bridging theoretical AI frameworks with real-world human factors. Analysis of his 2023-2025 publications reveals three dominant trends: 1) Quantitative validation of trust metrics across simulation and reality, 2) Development of relational network architectures for resilient multi-agent teams, and 3) Ethical frameworks for moral trust violations in autonomous systems. His work uniquely integrates reinforcement learning with psychological models to create beneficent AI systems that respect human trust boundaries. As Associate Chair for Master's Programs, Dr. Robinette oversees graduate curriculum development and student mentorship within electrical and computer engineering. His research leadership through PERC and RURI facilitates cross-disciplinary collaboration between robotics, printed electronics, and defense applications—though specific grant details remain unspecified in available materials. He directs experimental robotics teams developing platforms such as Robowhaler for marine autonomy and dataset collection. His lab maintains the publicly available Aquaticus dataset for human-robot teaming research and contributes to standardization efforts like the DECISIVE Test Methods Handbook for subterranean robotics evaluation. Current projects focus on real-world trust validation in navigation deviation scenarios and moral trust repair protocols.

John KennesView profile

Associate Professor

• Directed Search Theory
• Matching Theory
• Labor Economics
• +3 more

John Kennes is an Associate Professor in the Department of Economics at Aarhus University, Denmark. A Canadian citizen, he has resided and worked in Denmark since 2001, focusing on theoretical frameworks for coordination and exchange problems in economic systems. Ph.D. in Economics, University of Western Ontario, 1996 His research centers on directed search theory, analyzing how agents navigate coordination challenges in matching scenarios such as labor markets (workers to firms), marriage markets (men to women), education (schools to children), and credit allocation (borrowers to lenders). He extends this to policy design, examining reputation systems, government transfers, and over-the-counter markets through rigorous theoretical modeling. Currently leading two Danish Council for Independent Research projects, Kennes investigates how children influence marriage prospects for unwed mothers and assesses public daycare assignment rules' impact on child welfare and parental career trajectories in Denmark. His work bridges microeconomic theory with real-world institutional design. As an active researcher, he contributes to economics through externally funded projects addressing societal challenges in household dynamics and public resource allocation, with policy implications for Scandinavian welfare systems.

Enrique Ruiz ZunigaView profile

Associate Professor

• Production Engineering
• Logistics Systems
• Robotics
• +7 more

Enrique Ruiz Zuniga serves as Associate Professor in Production Engineering at the University of Skövde's School of Engineering Science in Sweden, while simultaneously holding a JSPS research fellowship at Kyoto University's Systems Design Laboratory and collaborating with Japan Manned Space Systems Corporation (JAMSS). His career bridges academic research and industrial applications across Europe and Asia, focusing on optimizing complex manufacturing and logistics systems through advanced computational methods. University of Skövde, School of Engineering Science JSPS Research Fellow, Kyoto University Japan Manned Space Systems Corporation (JAMSS) collaborator Dr. Ruiz Zuniga's educational background includes a B.Eng. in industrial engineering from the University of Malaga, Spain, a BSc in automation engineering from the University of Skövde, Sweden, followed by an MSc in industrial informatics and a 2020 PhD in informatics from the University of Skövde, completed in partnership with Xylem Water Solutions Manufacturing. His doctoral research focused on facility layout design using simulation-based optimization methodologies. His primary research interests encompass the design, verification, and improvement of logistics, robotics, and complex production systems, with methodological expertise in Lean Production, Discrete-Event Simulation, System Dynamics, Simulation-Based Optimization, and the Functional Resonance Analysis Method. Dr. Ruiz Zuniga's work demonstrates a consistent focus on international collaboration and practical implementation of theoretical models in real-world industrial settings across healthcare and manufacturing sectors. Analysis of his publication record reveals an evolution from foundational work in facility layout design toward more recent explorations of AI integration, human-centered design, and resilient production systems. His research shows increasing sophistication in combining simulation approaches with functional analysis methods, with a growing emphasis on human factors and system resilience in complex production environments. REFUSE (2023-2026): Resource efficient use of reconfigurable machining systems Dynamic SALSA (2023-2024): AI scheduling for assembly and logistics systems Envisioned world problems (2021-2023): Functional approaches for system design Emergency Department Modeling (2012-2016): Healthcare production systems Dr. Ruiz Zuniga has coordinated international engineering programs in Industrial Engineering, Product Design Engineering, and Mechanical Engineering (all 60 credits), while teaching courses including Introduction to Lean Philosophy, Methods Engineering, Mechatronics/Electronics, and Production and Logistic Simulation. His work demonstrates a strong commitment to bridging theoretical research with practical industrial applications in production engineering through international collaboration and methodological innovation.