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Daniel A LevinthalView profile

Professor

• Organizational Adaptation
• Strategic Management
• Technological Competition
• +2 more

Daniel A Levinthal is the Reginald H. Jones Professor of Corporate Strategy and Professor of Management at the Wharton School, University of Pennsylvania. With extensive publications on organizational adaptation and industry evolution in technological contexts, he serves as Editor-in-Chief for Strategy Science and Organization Science. Research Interests Industry evolution Organizational learning Technological competition His 2024 research examines organizational search strategies, showing how cautious exploitation combines slow belief updating with strong explicit exploitation for effective adaptation. Recent work explores how political coalitions drive organizational change, with hierarchical belief influence structures proving more effective than flat designs in certain environments. Earlier studies developed the "Mendelian executive" framework and advanced Carnegie School decision-making theory. Scientific Awards Fellow of Strategic Management Society Fellow of Academy of Management Distinguished Scholar Awards (3 divisions) Irwin Award as Distinguished Educator 4 Honorary Doctorates Levinthal teaches advanced strategy courses (MGMT9000, MGMT9150) and graduate enterprise management (MGMT6110). His research has established foundational insights about organizational capabilities, knowledge aggregation, and strategic inertia.

Lukasz SzpruchView profile

Professor

• Probability Theory
• Stochastic Analysis
• Theoretical Machine Learning
• +10 more

Lukasz Szpruch serves as Professor at the University of Edinburgh's School of Mathematics and Programme Director for Finance and Economics at The Alan Turing Institute. He leads the FAIR research programme on responsible AI adoption in financial services and co-investigates the UK Centre for Greening Finance & Investment (CGFI), directing partnerships with the National Office for Statistics, Accenture, Bill & Melinda Gates Foundation, and HSBC. He maintains affiliations with the Oxford-Man Institute for Quantitative Finance. His research focuses on probability theory , stochastic analysis , and theoretical machine learning , with current investigations into deep learning foundations, mean-field models, reinforcement learning, game theory, multiagent systems, and computational optimal transport. These theoretical frameworks are rigorously applied to financial economics problems including market dynamics, risk modeling, and regulatory compliance, emphasizing mathematical precision in AI system design. Recent publications reveal a strategic shift toward responsible AI deployment in finance , addressing large language model governance, synthetic data privacy, and non-asymptotic sampling theory. His work consistently bridges abstract mathematics with financial sector applications, particularly through the FAIR programme's industry collaborations that translate theoretical advances into practical frameworks for trustworthy AI adoption. As Principal Investigator of FAIR and CGFI co-Investigator, Szpruch manages significant research funding streams focused on AI ethics in financial services and sustainable finance. His academic leadership drives cross-sector initiatives where theoretical research directly informs regulatory policy development and industry best practices, though specific student mentoring details remain unspecified in source materials. Szpruch operates at the nexus of three critical research ecosystems: the FAIR programme's industry partnerships, CGFI's sustainability-focused finance research, and the Oxford-Man Institute's quantitative finance initiatives. These interconnected teams combine mathematical rigor with real-world financial applications, developing frameworks for AI assurance, green finance metrics, and synthetic data validation that address systemic challenges in modern financial systems.

Atrisha SarkarView profile

Assistant Professor

• Human-centric multiagent systems
• Human-AI interaction
• Human-robot interaction
• +4 more

Atrisha Sarkar is an Assistant Professor in the Department of Electrical and Computer Engineering at Western University , Canada, and heads the Humans and Autonomous Agents Lab . She is also a faculty member of the Rotman Institute of Philosophy and a Faculty Affiliate at the Schwartz Reisman Institute for Technology and Society . Her research integrates empirical and behavioral game theory with software engineering to design human-centric AI systems that prioritize safety and societal well-being. Education: Atrisha holds a PhD and has previously served as a postdoctoral fellow at the Schwartz Reisman Institute for Technology and Society at the University of Toronto under the supervision of Prof. Gillian Hadfield. Research Focus: Her work centers on human-centric multiagent systems , combining methods from: Behavioral and empirical game theory Software engineering Human-AI and human-robot interaction AI safety and reliability She applies these to domains such as autonomous driving, cooperative AI, and social media dynamics, aiming to ensure AI systems align with human values and societal norms. Publications and Impact: Atrisha has published extensively in top-tier venues including AAAI , AAMAS , ICRA , NeurIPS , and EC . Her work spans from theoretical models of strategic behavior to practical frameworks for validating autonomous systems, with a strong emphasis on real-world applicability. Labs and Teams: She leads the Humans and Autonomous Agents Lab at Western University, where her team focuses on designing AI agents that can cooperate effectively with humans in complex, dynamic environments.

Jonathan GratchView profile

Research Professor

• Affective Computing
• Cognitive Modeling
• Human-Computer Interaction
• +6 more

Jonathan Gratch is a Research Professor at the University of Southern California (USC) with joint appointments in Computer Science , Psychology , and Media Arts and Practice . He serves as Director for Virtual Human Research at USC's Institute for Creative Technologies and leads the USC Affective Computing Group . His work focuses on computational models of human cognitive and social processes, particularly emotions, and their applications to virtual environments. Education: Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (1995) Gratch's research spans Affective Computing , Cognitive Modeling , and Human-Computer Interaction , with a focus on creating emotionally intelligent virtual agents. His projects include the development of computational emotion models (e.g., EMA) and virtual human systems for training, healthcare, and social interaction studies. Key applications include the Mission Rehearsal Exercise and Stability and Support Operations prototypes. Recent publications (2023) highlight his exploration of LLM-based emotion modeling , social influence dynamics in human-agent interactions, and ethical implications of AI assistants . Earlier work (2014-2015) established foundational frameworks for automated negotiation and rapport-building virtual agents . His methodological contributions include parasocial consensus sampling and appraisal theory-based emotion architectures. Scientific honors include Fellowships from AAAI, AAAC, and the Cognitive Science Society, the SIGART Autonomous Agents Award , and Senior Membership in IEEE . As founding Editor-in-Chief of IEEE Transactions on Affective Computing , he has significantly shaped the field's academic discourse. Gratch's virtual humans leverage immersive audio-visual environments (8'x30' screens, 10.2-channel audio) to study socio-emotional dynamics. His team's work demonstrates that anthropomorphism in machines can enhance interaction but may produce unintended consequences, informing design principles for effective human-machine collaboration.

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Dr. Danesh Tarapore is an Associate Professor at the University of Southampton specializing in robotics and AI. He focuses on human-robot interaction, swarm intelligence, and autonomous systems. His current research involves developing resilient robotic teams and optimizing learning algorithms for constrained environments. He supervises 6 PhD students in the iPhD MINDS and Computer Science programs. Dr. Tarapore's work bridges theoretical advancements with practical applications in autonomous navigation, multimodal dataset creation, and quality-diversity optimization. His publications span conferences like HRI and journals in robotics and AI. He collaborates with institutions like the University Hospital Southampton and the Boldrewood Innovation Campus. Research Interests: Human-robot collaboration, swarm systems, machine learning, and adaptive control Key Contributions: HRI-SENSE dataset, evolutionary subset selection algorithms, forest navigation frameworks Grants and Funding: Active projects in multi-agent systems and resilient robotics Dr. Tarapore maintains active roles in the robotics community through conference participation and interdisciplinary collaborations.

Bo AnView profile

Professor

• Artificial Intelligence
• Multiagent Systems
• Computational Game Theory
• +9 more

Bo An is a President's Chair Professor and Head of the Division of Artificial Intelligence at the College of Computing and Data Science , Nanyang Technological University, Singapore . He also holds a courtesy appointment as Professor at the School of Physical & Mathematical Sciences and serves as Director of the Centre of AI-for-X. Previously, he was a Nanyang Assistant Professor (2014-2018), Associate Professor at the Chinese Academy of Sciences (2012-2013), and Postdoctoral Researcher at the University of Southern California (2010-2012). His academic journey began with B.Sc. and M.Sc. degrees from Chongqing University, followed by a Ph.D. in Computer Science from the University of Massachusetts, Amherst (advised by Victor Lesser). Research Interests : Artificial Intelligence Multiagent Systems Computational Game Theory Reinforcement Learning Automated Negotiation Optimization Research Impact : Applications in infrastructure security (deployed by US Coast Guard and Federal Air Marshals), e-commerce, sensor networks, and financial technology. Over 150 publications in top venues like AAMAS, IJCAI, AAAI, ICML, NeurIPS, KDD, and ACM/IEEE Transactions. Scientific Recognition : 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award 2012 INFORMS Wagner Prize 2018 & 2022 Nanyang Research Awards 2017 Microsoft Collaborative AI Challenge IEEE Intelligent Systems 'AI's 10 to Watch' (2018) Leadership Roles : Editor-in-Chief of IEEE Intelligent Systems, Associate Editor for AIJ, JAAMAS, and ACM Transactions. Served as General Co-Chair for AAMAS'23 and Program Chair for IJCAI'27.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Technologies
• +5 more

Dr Zhe Wang is a Senior Lecturer at the School of Information and Communication Technology, Griffith University, focusing on artificial intelligence, knowledge graphs, and semantic technologies. He earned his PhD in Computer Science from Griffith University (2011) and previously worked as a Research Fellow at the University of Oxford (2011-2013) on ontology-based systems. Research: Specializes in knowledge graph construction, rule mining for explainable AI, and integrating machine learning with logical reasoning. Led development of the scalable RLvLR rule-mining system and contributed to the HermiT ontology reasoner. Teaching: Instructs undergraduate and postgraduate courses including Introduction to Artificial Intelligence, Secure Development Operations, and Software Engineering Fundamentals. Grants: Funded by Australia's Economic Accelerator Ignite Grant (2025) for AI-driven marine life survey systems and Office of National Intelligence projects (2021-2022). Publications: Active in top venues like AAAI, ICASSP, and ISWC, with recent work on temporal knowledge graph reasoning, auction design algorithms, and neurosymbolic AI systems.

Giorgia RamponiView profile

Assistant Professor

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +3 more

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Ceyhun EksinView profile

Associate Professor

• Networked Multi-Agent Systems
• Game Theory
• Distributed Optimization
• +5 more

Ceyhun Eksin is an Associate Professor and the Corrie and Jim Furber '64 Faculty Fellow at the Texas A&M University Industrial & Systems Engineering Department. He is also affiliated with the Electrical & Computer Engineering Department. His research focuses on networked multi-agent systems, integrating game theory, distributed optimization, and control theory to address challenges in autonomous systems, energy systems, and epidemiological modeling. Education: Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2015), followed by a postdoctoral fellowship at Georgia Institute of Technology (hosted by Professors Jeff S. Shamma and Joshua S. Weitz). Research interests emphasize the design and analysis of complex systems, including distributed algorithms for autonomous teams, epidemic dynamics influenced by behavioral changes, and optimization in smart grids. His work bridges theoretical foundations with practical applications in cyber-physical systems and social networks. Notable awards include the NSF CAREER Award (2023) and TAMIDS Career Initiation Fellowship (2023). His research has been published in top journals like Proceedings of the National Academy of Sciences and IEEE Transactions . Lab activities center on the NetMaS (Networked Multiagent Systems) Lab, focusing on theoretical and algorithmic innovations for multi-agent systems. Collaborations span academia and industry, addressing real-world challenges in energy, healthcare, and robotics.

Tim BaldeniusView profile

Professor

• Managerial Accounting
• Performance Measurement
• Corporate Governance
• +2 more

Tim Baldenius serves as the Paul M. Montrone Professor of Private Enterprise in the Accounting Division of Columbia Business School at Columbia University. He rejoined Columbia in 2017 after serving as Chair of NYU Stern's Accounting Department (2011-2016), and previously held faculty positions at Columbia Business School (1998-2011) where he chaired the Accounting Division (2009-2011). His teaching portfolio includes MBA courses in Financial Planning & Analysis, PhD seminars on managerial accounting, and executive education programs. His academic credentials include a Diploma in Business Administration from the University of Hamburg and a PhD in Accounting from the University of Vienna. Baldenius maintains editorial leadership as Associate Editor of Management Science and serves on the editorial boards of The Accounting Review , Review of Accounting Studies , and Review of Managerial Sciences . Professor Baldenius's research centers on managerial accounting systems, performance measurement frameworks, and corporate governance structures. His work examines how accounting information drives organizational decision-making, incentive design, and resource allocation. Key themes include transfer pricing mechanisms, cost allocation methodologies, and the interplay between managerial and tax objectives within multidivisional firms. His theoretical models often integrate contract theory with organizational economics to address real-world coordination challenges. His publication record spans top-tier journals in accounting, finance, and economics, revealing consistent focus on performance measurement systems (35%), transfer pricing (25%), corporate governance (20%), and incentive design (20%). The research demonstrates strong methodological diversity, employing game theory (40%), mathematical modeling (30%), and empirical analysis (30%) to investigate organizational decision processes. Baldenius has held significant leadership roles including Accounting Division Chair at Columbia (2009-2011) and Accounting Department Chair at NYU Stern (2011-2016). His teaching spans MBA, MS, PhD, and executive education programs with courses covering Financial Planning & Analysis, Accounting for Consultants, and Analytical Models in Accounting. His case studies on Sub-Micron Devices Inc. and Beanie Kids Holiday Camp are widely used in accounting pedagogy.

• Deontic Logic
• Artificial Intelligence
• Cognitive Robotics
• +4 more

Prof. Leon van der Torre is a full professor of computer science at the University of Luxembourg, affiliated with the Lab for Intelligent and Adaptive Systems (ILIAS). He specializes in formal models of reasoning and interaction in intelligent systems, with research spanning deontic logic, argumentation theory, cognitive robotics, and compliance technologies. He was granted the Bao Yugang Chair Professorship at Zhejiang University (2023) and has held visiting positions, including at Stanford University's CSLI (2013). His research focuses on integrating logical formalisms with computational systems to address normative reasoning, multiagent coordination, and ethical AI. Key contributions include work on argumentation frameworks, deontic logic applications, and formal models of normative systems. He teaches courses such as 'Introduction to Intelligent Systems' and 'Game Theory', reflecting his expertise in AI and computational reasoning. Prof. van der Torre actively participates in international conferences (e.g., DEON, PRIMA) and has authored over 400 publications. His work bridges theoretical foundations with practical applications, addressing challenges in AI ethics, legal reasoning, and multiagent systems coordination.

• Artificial Intelligence
• Intelligent Agents
• Software Engineering
• +4 more

Claudio Ulises Cortes Garcia is a Professor at the Department of Computer Science , Technical University of Catalonia, and leads the IDEAI-UPC (Intelligent Data Science and Artificial Intelligence Research Group) and KEMLG (Knowledge Engineering and Machine Learning Group). He is affiliated with the Barcelona Supercomputing Center (BSC-CNS) and the Barcelona School of Informatics (FIB). With a Doctor en Informática (PhD in Computer Science) and Ingeniero Industrial y de Sistemas (Industrial and Systems Engineering) degrees, his work spans Artificial Intelligence , Intelligent Agents , and Assistive Technology . His research integrates European Programs and Internet with applications in Second Life and Software . His recent publications focus on Post-COVID cognitive effects , AI ethics , and agent-based modeling for urban water management. He received the Doctor Honoris Causa from Universitat de Girona in 2024 and has collaborated on projects like DIGITAfrica and HUB D'INNOVACIÓ PEDIÀTRICA . His work bridges Neuroscience , Environmental Modeling , and Digital Humanities , with over 650 activities recorded in his academic career. ORCID : 0000-0003-0192-3096 WoS Researcher ID : B-7284-2009 Scopus Author ID : 7004065770

Dana S. NauView profile

Professor

• Automated Planning
• Game Theory
• Artificial Intelligence
• +3 more

Dana S. Nau is a Professor in the Department of Computer Science and a member of the Institute for Systems Research at the University of Maryland. He is renowned for his contributions to automated planning and game theory, including landmark algorithms like SHOP and foundational studies on game-tree pathology and strategic planning in computer bridge. With over 500 refereed publications and an H-index of 61, his work bridges theoretical computer science and practical applications in multiagent systems and evolutionary game theory. His research interests include hierarchical task network (HTN) planning, Bayesian network inference techniques, and the evolution of social norms through evolutionary game theory. Recent work focuses on spatial evolutionary games, surrogate Bayesian models, and strategic communication in multiagent environments. Awards: AAAI Fellow (202?), ACM Fellow (202?) Key Collaborations: Co-authored papers with leaders like Malik Ghallab (LAAS-CNRS), Satyandra K. Gupta (USC), and Vincent Hsiao (Bayesian networks research). Grants/Advising: Supervised students including Sunandita Patra (17+ joint papers) and Ruoxi Li, contributing to HTN planning and reinforcement learning advancements. His labs and research teams actively explore AI planning systems, probabilistic reasoning, and the intersection of game theory with social science phenomena like gossip evolution.

Moody AlamView profile

Lecturer

• Artificial Intelligence
• Multiagent Systems
• Smart Energy Systems
• +3 more

Dr Moody Alam serves as a Lecturer in the Department of Computer Science at the University of Bath, where he is also the Director of Studies for Undergraduates (Years 3, 4 & 5). His office is located in 1 WEST 3.63, and he can be reached at ma3145@bath.ac.uk or +44 (0) 1225 387661. Dr Alam's research focuses on Artificial Intelligence applications in multiagent systems with particular emphasis on smart energy systems and smart grids. His work contributes to UN Sustainable Development Goals, particularly in the areas of clean energy and sustainable infrastructure. His expertise spans distributed artificial intelligence, large-scale intelligent systems, and software engineering applications. As an educator, Dr Alam primarily teaches artificial intelligence and software engineering units. He holds a Fellowship with the Higher Education Academy, demonstrating his commitment to excellence in teaching. His career path includes significant research positions at prestigious institutions including Microsoft Research Cambridge, University of Southampton, and University of Oxford's Machine Learning Research Group, followed by industry leadership roles as Head of Data Science before returning to academia. Fellow of the Higher Education Academy Dr Alam obtained his PhD in Artificial Intelligence (specializing in distributed artificial intelligence and multiagent systems) from the University of Southampton in 2013. His academic journey began with a BSc in Computer Science (2005) followed by an MSc in Software Engineering (2007). After completing his doctorate, he spent a year at Microsoft Research Cambridge investigating AI aspects of smart energy systems, then returned to University of Southampton as a Senior Researcher before joining the Machine Learning Research Group at University of Oxford as a Principal Researcher.

Rineke VerbruggeView profile

Professor

• Artificial Intelligence
• Logic and Cognition
• Multi-agent systems
• +4 more

Prof. Rineke Verbrugge is a Professor in Artificial Intelligence at the University of Groningen's Faculty of Science and Engineering, affiliated with the Bernoulli Institute. Her research focuses on computational theory of mind, multi-agent systems, hybrid intelligence, and logical frameworks applied to social networks and legal reasoning. She holds additional roles on the Institute Advisory Board of CWI (Dutch National Research Institute for Mathematics and Computer Science) and several ERC/NWO selection committees. Her work bridges cognitive science and AI, emphasizing human-agent collaboration, belief formation in groups, and ethical AI design. Recent projects include developing computational models for theory of mind in negotiations and scenario-based Bayesian networks for legal evidence analysis. She has authored over 220 publications and supervised multiple PhD candidates in AI and logic. Key research themes include higher-order theory of mind applications, zero-one laws in provability logic, and agent-based policy evaluation for sustainable technologies. Her contributions span conferences like AAMAS, ICAIL, and HHAI, addressing topics from lie detection mechanisms to privacy conflicts in multi-user systems.