جست‌وجوی استاد و پژوهشگر

Sean Carrollمشاهده پروفایل

استاد

• Cosmology
• Quantum Mechanics
• General Relativity
• +۴ مورد دیگر

Sean Carroll serves as the Homewood Professor of Natural Philosophy at Johns Hopkins University and holds External Faculty status at the Santa Fe Institute. His research bridges cosmology, quantum mechanics, and philosophy, focusing on foundational questions about spacetime emergence, quantum interpretation, and complexity across cosmic scales. Carroll earned his Ph.D. from Harvard University in 1993. His academic trajectory reflects deep engagement with theoretical physics and philosophical inquiry, culminating in his current named professorship at Johns Hopkins. Carroll's research centers on the intersection of physics and philosophy, with significant contributions to quantum foundations, cosmology, and the nature of emergence. He is a leading proponent of the many-worlds interpretation of quantum mechanics and has pioneered work on the thermodynamic arrow of time, quantum decoherence, and the fine-tuning of initial cosmic conditions. His recent investigations explore discretized quantum systems, holographic principles in gravity, and the philosophical implications of quantum gravity. Analysis of his 2022-2025 publications reveals a pronounced shift toward computational approaches in quantum gravity, with increasing emphasis on finite-dimensional Hilbert spaces and GPU-accelerated modeling. His work consistently integrates quantum information theory with cosmological questions, particularly examining how spacetime geometry emerges from quantum entanglement and how complexity evolves in closed systems. Carroll's scientific recognition includes: National Science Foundation Fellowship NASA Fellowship Sloan Research Fellowship Packard Fellowship Fellow of the American Physical Society American Institute of Physics Award Fellow of the Royal Society Guggenheim Fellowship Fellow of the American Association for the Advancement of Science His research has been sustained through major fellowships from NSF, NASA, Sloan, and Packard foundations, enabling interdisciplinary collaborations across physics and philosophy. Carroll actively mentors graduate students at Johns Hopkins and contributes to public discourse through his popular science books (including the Biggest Ideas in the Universe series) and the weekly Mindscape podcast. As Fractal Faculty at the Santa Fe Institute, Carroll participates in cross-disciplinary research on complex systems, exploring how emergent phenomena arise from fundamental physical laws. His work bridges theoretical physics with broader questions about complexity in biological, cognitive, and social systems.

J. Eric Bickelمشاهده پروفایل

استاد

• Decision Analysis
• Probabilistic Modeling
• Climate Engineering
• +۴ مورد دیگر

J. Eric Bickel is a Professor at The University of Texas at Austin, serving as Director of the Operations Research & Industrial Engineering (ORIE) and Engineering Management programs. He holds a courtesy appointment in the Department of Petroleum and Geosystems Engineering and directs the Center for Engineering & Decision Analytics (CEDA). His academic background includes a PhD and MS in Engineering-Economic Systems from Stanford University and a BS in Mechanical Engineering from New Mexico State University. His research focuses on decision analysis under uncertainty, addressing topics like probabilistic modeling, climate engineering, risk management, and applications in sports and energy sectors. His work has been featured in major media including The New York Times and Wall Street Journal , and his climate engineering research was endorsed by Nobel Laureates as a top climate change response strategy. Professor Bickel has extensive industry experience, having previously served as Senior Engagement Manager and Co-Director of Client Education at Strategic Decisions Group (SDG), where he remains on the Board of Directors. His consulting spans oil/gas, energy trading, and financial services sectors. He has received recognition as a Fellow of the Society of Decision Professionals and contributed to the Copenhagen Consensus on Climate Project. His teaching extends to executive education through Texas Executive Education and McCombs School of Business. Research highlights include novel methods for probabilistic dependence modeling, value-of-information analysis in shale reservoirs, and critiques of risk assessment tools like heat maps. His climate engineering work emphasizes economically viable solar radiation management strategies.

Hengrui Caiمشاهده پروفایل

استادیار

• Causal Inference
• Reinforcement Learning
• Graphical Models
• +۷ مورد دیگر

Dr. Hengrui Cai is an Assistant Professor of Statistics at the University of California Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She holds a Ph.D. in Statistics from North Carolina State University (NCSU) and a B.S. in Statistics from Zhejiang University. Her research focuses on causal inference, reinforcement learning, and graphical models, with applications in precision medicine, healthcare analytics, and epidemiology. She develops interpretable solutions for individualized decision-making, particularly in healthcare settings such as ICU patient treatment optimization and pandemic analysis. Notable achievements include the NSF CDS&E-MSS Award (2024), ICS Research Awards (2023–2024), and recognition for contributions to causal discovery and policy evaluation. Dr. Cai advises graduate and undergraduate students on projects involving causal AI, machine learning, and healthcare data analysis. She teaches courses like 'Causal Machine Learning' and 'Introduction to Probability and Statistics,' emphasizing interdisciplinary approaches to real-world problems. Her work integrates statistical theory with practical applications, exemplified by software tools like ANOCE-CVAE for causal mediation analysis and the Sepsis EHR Benchmark Environment for reinforcement learning. Dr. Cai collaborates widely, contributing to projects such as quantifying the impact of the 2020 Hubei lockdowns on virus spread in China through causal graph analysis.

Christian Listمشاهده پروفایل

استاد

• Philosophy
• Decision Theory
• Social Choice Theory
• +۵ مورد دیگر

Christian List is a Professor of Philosophy and Decision Theory at Ludwig Maximilian University of Munich (LMU Munich) and Co-Director of the Munich Center for Mathematical Philosophy (MCMP). He holds concurrent visiting professorships at the London School of Economics (LSE) and has held prestigious positions globally, including at Harvard Law School and Princeton University. His work bridges philosophy with social sciences, focusing on decision theory, free will, consciousness, and group agency. Education: D.Phil. in Politics (Oxford, 2001), M.Phil. and B.A. in Mathematics & Philosophy (Oxford). Key roles include Fellow of the British Academy and Member of the Bavarian Academy of Sciences. Research interests span metaphysical questions (free will, consciousness), formal methods in decision theory, and the philosophy of the social sciences. His monograph Why Free Will is Real (2019) is a landmark work. Recent articles explore AI ethics, probability aggregation, and consciousness theories. Scientific awards include the Joseph B. Gittler Award (2020) and the Social Choice and Welfare Prize (2010). He has edited major journals like Economics and Philosophy and contributed to the Stanford Encyclopedia of Philosophy . His advisory and editorial work spans multiple disciplines, reflecting his interdisciplinary approach to philosophy and social science.

Changyou Chenمشاهده پروفایل

دانشیار

• Multi-Modal Learning
• Vision and Language
• Foundation Models
• +۷ مورد دیگر

Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.

Krzysztof Burdzyمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Brownian Motion
• +۷ مورد دیگر

Krzysztof Burdzy is a Professor of Mathematics and Adjunct Professor of Statistics at the University of Washington, where he is affiliated with the Department of Mathematics in the College of Arts and Sciences. He maintains an active research and teaching profile, currently offering undergraduate courses in probability. His research interests span Probability Theory , Stochastic Processes , Neumann Eigenfunctions , Hot Spots Problem , and the Philosophy of Probability . He is particularly known for his work on Brownian motion, eigenfunction behavior, and spectral theory in geometric domains. His recent work includes contributions to the resolution of the hot spots conjecture for Euclidean triangles and the discovery of interior hot spots in convex sets. The most recent articles reflect a deep engagement with both theoretical mathematics and foundational philosophy. Topics include spectral geometry, probabilistic methods in PDEs, critiques of philosophical theories of probability, and interdisciplinary reflections on epistemology. The publication trend shows sustained contributions from the 1990s through 2024, with a dual focus on rigorous mathematical proofs and meta-scientific analysis. Euclidean triangles have no hot spots (Annals of Mathematics, 2020) Convex sets can have interior hot spots (preprint, 2024) Hypocrisy++: On Philosophy of Probability and Sociology of Ideologies (2023) Burdzy has advised students in mathematics and probability, though specific names are not listed. He has received recognition through publications in top-tier journals such as Annals of Mathematics and Journal of Functional Analysis , though formal awards are not explicitly mentioned. He has delivered numerous talks on the philosophy of probability and its relationship to statistics. He is actively involved in public scholarship, maintaining a personal website with essays on quantum probability, real estate, philosophy, and AI. His work on the limitations of mathematics, suicide prevention, and critiques of post-modern thought reflect a broad intellectual engagement beyond technical mathematics. He does not appear to lead a formal lab or research team, but collaborates with scholars such as R. Bañuelos, W. Werner, and others in probability and analysis.

Hyuck Jin Parkمشاهده پروفایل

استاد

• Landslide Hazard Assessment
• Geotechnical Engineering
• Machine Learning Applications
• +۵ مورد دیگر

Hyuck Jin Park is a Full Professor in the Department of Energy Resources and Geosystems Engineering at Sejong University, South Korea, where he has been teaching and conducting research since 2003. With a Ph.D. in Engineering Geology from Purdue University, his expertise spans geotechnical engineering, landslide analysis, and geospatial technologies. Professor Park has built a distinguished career in landslide hazard assessment, combining traditional geotechnical approaches with modern machine learning techniques to improve prediction accuracy and risk management. His educational background includes: B.S. in Geology from Yonsei University (1990) M.S. in Geophysics from Yonsei University (1993) Ph.D. in Engineering Geology from Purdue University (2011) Professor Park's research focuses on the spatial and temporal probability of landslide occurrence, utilizing fuzzy logic, probabilistic analysis, GIS, Monte Carlo simulation, and machine learning for landslide hazard assessment. His work integrates physically based models with statistical approaches to better understand landslide mechanisms and improve prediction capabilities. He has made significant contributions to the development of methodologies that account for geological uncertainties in hazard assessment, with applications ranging from rock slope stability to rainfall-induced shallow landslides. His recent publications demonstrate a clear trend toward integrating explainable artificial intelligence with traditional geotechnical approaches for natural hazard assessment. Professor Park's work increasingly focuses on making machine learning models transparent and interpretable while maintaining high predictive accuracy. The research spans multiple hazard types including landslides, earthquakes, and floods, with a growing emphasis on climate change impacts and data-scarce environments. With an h-index of 28 and over 3,421 citations, Professor Park has established himself as a leading researcher in his field. His work has been published in high-impact journals including Engineering Geology, Landslides, and Catena, reflecting the significance and quality of his contributions to geotechnical engineering and natural hazard assessment. Professor Park has mentored numerous researchers through collaborative projects and has secured funding for his innovative work in landslide prediction and hazard assessment. His research has involved significant international collaboration, particularly with researchers from Malaysia, Australia, and Yemen, addressing landslide and flood risks in diverse geographical contexts. He leads research activities within the Department of Geoinformation Engineering at Sejong University and has contributed to the development of specialized tools like DEWS (Distance, Elevation, Watershed, and Slope unit) for landslide early warning systems.

Christopher Ferrieمشاهده پروفایل

دانشیار

• Quantum Estimation and Control
• Quantum Machine Learning
• Quantum Information Science
• +۵ مورد دیگر

Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.

• Forensic Science
• Computational Forensics
• Evidential Evaluation
• +۳ مورد دیگر

Charles E.H. Berger serves as Professor by Special Appointment in Criminalistics at Leiden University's Institute for Criminal Law and Criminology since November 2011, a position funded by the Stichting Leerstoel Criminalistiek. He concurrently holds a principal scientist position at the Netherlands Forensic Institute (NFI), where he contributes to education, R&D strategy, and research on forensic evidence interpretation. His research program centers on logically sound interpretation of forensic evidence through probability theory and computational methods. Berger specializes in applying Bayesian statistics to forensic anthropology, personal identification, and evidential evaluation. His work emphasizes moving forensic science toward activity-level interpretations while managing contextual information to prevent bias. Berger plays a pivotal international role as member of ISO technical committee TC272, serving as lead editor for Part 4 (Interpretation) of the ISO-21043 Forensic Sciences standard. His scholarly contributions focus on improving forensic reasoning frameworks and establishing objective evaluation methodologies. His publications demonstrate consistent engagement with foundational forensic science challenges, particularly in developing statistically rigorous approaches to evidence interpretation that maintain scientific integrity within legal contexts. Berger actively promotes scientifically sound practices across the criminal justice system, emphasizing the importance of clear communication between forensic scientists, legal professionals, and other stakeholders to ensure proper understanding and application of forensic evidence.

Nelly V. Litvakمشاهده پروفایل

استاد

• Algorithms for Complex Networks
• Applied Probability
• Stochastic Operations Research
• +۵ مورد دیگر

Nelly V. Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (Mathematics and Computer Science). She works on mathematical methods and algorithms for complex networks (social networks, WWW) using random graph models. She joined TU/e as a part-time professor in 2017 after being an Associate Professor at the University of Twente since 2012. Affiliations: 4TU Applied Mathematics Institute, Data Science Center Eindhoven, CTIT Industry Partners: ABN-AMRO Bank, Philips Lighting, Thales Editorial Role: Managing Editor of Internet Mathematics Her research focuses on extracting value from network data across three areas: (1) Information extraction and prediction, (2) Mathematical analysis of network characteristics, and (3) Efficient algorithms for incomplete network data. Key topics include PageRank, HITS algorithm, random graphs, homophilic networks, and network epidemiology. Recent work (2022-2025) spans network growth mechanisms, fairness in ranking algorithms, educational pedagogy, and pandemic forecasting dashboards. She contributes to SDGs through data-driven approaches to societal challenges. Teaching activities include course development at TU/e and earlier institutions, with innovative methods for computer engineering students' statistical understanding.

Anqi Liuمشاهده پروفایل

استادیار

• Machine Learning
• Trustworthy AI
• Distributionally Robust Learning
• +۷ مورد دیگر

Anqi Liu is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She maintains significant affiliations with the Johns Hopkins Mathematical Institute for Data Science (MINDS) and the Johns Hopkins Institute for Assured Autonomy (IAA), while also collaborating extensively with the Center for Language and Speech Processing (CLSP) and the Laboratory for Computational Sensing and Robotics (LCSR). Her research focuses on developing principled machine learning algorithms for building reliable, trustworthy, and human-compatible AI systems in real-world applications. Key research areas include: Distributionally robust learning under covariate shift Uncertainty quantification for AI safety and fairness Safe exploration in control systems Fair machine learning under distribution shift Active learning under label shift Dr. Liu's work addresses critical challenges in high-stakes AI applications where reliability, safety, and societal impact are paramount. Her methods ensure AI systems remain robust to changing data environments, provide accurate uncertainty estimates, and incorporate human preferences in interactions. Analysis of her recent publications reveals a strong trajectory in trustworthy AI research with significant contributions to distribution shift handling, uncertainty quantification techniques, and safe decision-making frameworks. Her work bridges theoretical foundations with practical applications across healthcare, robotics, and social media analysis. Amazon Research Award Dr. Liu actively mentors eight PhD students and teaches specialized courses on Machine Learning for Trustworthy AI and standard Machine Learning at Johns Hopkins University, preparing the next generation of researchers to address critical challenges in AI safety and reliability.

• Collective Intelligence
• Temperament and psychobiology of mental illness
• Collective Dynamics
• +۷ مورد دیگر

William Sulis is an Associate Clinical Professor in the Department of Psychiatry and an Associate Member of the Department of Psychology at McMaster University, where he also directs the Collective Intelligence Lab (CILab). With a unique interdisciplinary background spanning mathematics, physics, and psychiatry, Dr. Sulis bridges the gap between theoretical science and clinical practice. His educational journey is exceptionally diverse: B.Sc. (Hon) in Mathematics with minor in Theoretical Physics, Carleton University (1976) M.D., University of Western Ontario (1980) M.A. in Mathematics, University of Western Ontario (1984) Ph.D. in Mathematics, University of Western Ontario (1989) FRCPC in Psychiatry (1984) Ph.D. in Theoretical Physics, University of Waterloo (2014) CRCPC in Geriatric Psychiatry (2015) Dr. Sulis's research explores the intersection of complex systems theory with psychological and psychiatric phenomena. His work on Collective Intelligence investigates how group dynamics emerge from individual interactions, while his research on Temperament and Psychobiology examines the continuum between normal personality variations and mental illness. He has made significant contributions to understanding Synchronization in Complex Systems and developed the concept of Transient Induced Global Response Synchronization (TIGoRS) , which has implications for neural coding and information processing. His theoretical work extends to Quantum Foundations and Process Algebra Theory , where he proposes novel approaches to quantum mechanics. Analysis of his recent publications reveals a consistent thread connecting complex systems theory with psychological and psychiatric applications. His work increasingly focuses on bridging the gap between temperament theory and clinical psychiatry, using mathematical and computational approaches to understand mental illness. Simultaneously, he continues to develop theoretical frameworks in quantum physics through process algebra models, demonstrating remarkable interdisciplinary range. Dr. Sulis has received several prestigious awards including The Governor General's Medal for having the highest overall grade point average in his graduating class, the Henry Marshall Tory Scholarship, and multiple Harry Stevenson Southam Scholarships. Throughout his career, Dr. Sulis has mentored numerous students across disciplines, supervising research projects spanning collective intelligence, semantic space modeling, network dynamics, and temperament studies. His Collective Intelligence Lab has served as a hub for interdisciplinary research connecting computer science, psychology, and psychiatry. Dr. Sulis has also been actively involved in professional organizations, serving as President of The Society for Chaos Theory in Psychology and the Life Sciences (1996-1998) and holding editorial positions for several journals including "Dynamical Psychology" and "Nonlinear Dynamics in Psychology and the Life Sciences." As Director of the Collective Intelligence Lab at McMaster University, Dr. Sulis fosters research exploring how complex adaptive systems can model cognitive and social phenomena. The lab serves as an intellectual nexus where mathematics, computer science, psychology, and psychiatry converge to address fundamental questions about intelligence, both individual and collective.

Freda Shiمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing
• Multilingualism
• +۴ مورد دیگر

Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo, and a Faculty Member at the Vector Institute. She holds a Canada CIFAR AI Chair. Her research focuses on computational linguistics, natural language processing (NLP), and grounded language learning, with emphasis on multilingualism and spatial reasoning in vision-language systems. She earned her Ph.D. in Computer Science from the Toyota Technological Institute at Chicago (2024), advised by Karen Livescu and Kevin Gimpel, supported by a Google Ph.D. Fellowship. Her undergraduate degree is from Peking University (2018), with a minor in Sociology. Her academic career includes affiliations with the CompLING Lab at Waterloo and contributions to major conferences like ACL and NAACL. She has organized tutorials on NLP grounding and is actively involved in research on model robustness and cognitive insights. Awards include the Google Ph.D. Fellowship and Best Paper Nominations at ACL 2024 and EMNLP 2021, alongside her Thesis of Distinction. She teaches courses such as CS 784 (Computational Linguistics) and CS 486/686 (Artificial Intelligence), emphasizing both theoretical and applied aspects of NLP. Research trends in her articles highlight advancements in vision-language spatial reasoning, multilingualism, and model interpretability. Her work bridges cognitive science and computational methods, exploring how human language mechanisms inform the design of more trustworthy AI systems. Scientific Awards: Google Ph.D. Fellowship Best Paper Nominee (ACL 2024) Best Paper Nominee (EMNLP 2021) Thesis of Distinction (2024) Advising and Grants: As an advisor, she encourages prospective students to review her guidelines. Her grants include support from the Canada CIFAR AI Chair program and the Vector Institute. She collaborates in labs such as CompLING at Waterloo and co-organizes events at NAACL and ICLR. Labs/Teams: She leads the CompLING Lab at the University of Waterloo, affiliated with the Vector Institute. Her work integrates interdisciplinary teams focusing on grounded learning and multilingual NLP challenges.

Nir Orenمشاهده پروفایل

استاد

• Multi-Agent Systems
• Formal Argumentation
• Computational Trust Theory
• +۱ مورد دیگر

Professor Nir Oren is a faculty member at the School of Natural and Computing Sciences , University of Aberdeen. His research focuses on multi-agent systems , formal argumentation , computational trust theory , and norm-based reasoning . He currently supervises PhD students in Computing Science and serves as Dean for Research Performance. Research Specialisms: Artificial Intelligence, Operational Research Contact: n.oren@abdn.ac.uk Research Trends (2022–2025): Nir Oren's publications span argumentation theory , BDI agent modeling , resilience in autonomous systems , and human-machine collaboration . His recent work addresses responsibility-aware AI , medical explainability , and environmental sensor networks . Key methods include probabilistic reasoning , game theory , and logical formalisms .

Christian Listمشاهده پروفایل

استاد

• Decision-making
• Individual and collective choice
• Preference and judgment aggregation
• +۹ مورد دیگر

Christian List is Professor of Philosophy and Decision Theory at Ludwig Maximilian University of Munich, where he serves as Co-Director of the Munich Center for Mathematical Philosophy (MCMP). Previously, he was Professor of Philosophy and Political Science at the London School of Economics until 2020. His work bridges philosophy, economics, and political science with a particular focus on individual and collective decision-making and the nature of intentional agency. Professor List's research spans multiple interconnected domains: theories of individual and collective choice (particularly social choice theory and judgment aggregation), free will and consciousness, the philosophy of mind and action, and the foundations of the social sciences. His work on group agency, developed in his influential book Group Agency with Philip Pettit, has reshaped debates about corporate entities and collective intentionality. His more recent work on free will, culminating in his book Why Free Will is Real , presents a scientifically grounded defense of free will against reductionist skepticism. His recent publications reveal a sophisticated integration of formal methods with deep philosophical questions, particularly regarding consciousness, probability aggregation, and the relationship between different levels of explanation. List's work consistently demonstrates how mathematical precision can illuminate fundamental philosophical problems while maintaining relevance to broader social and scientific contexts. Scientific Awards and Recognition: Elected Fellow of the British Academy (2014) Member of Academia Europaea (2023) Member of the Bavarian Academy of Sciences and Humanities (2022) Joseph B. Gittler Award from the American Philosophical Association (2020) Philip Leverhulme Prize in Philosophy (2007) 5th Social Choice and Welfare Prize (2010) List has supervised numerous PhD students and early-career researchers, many of whom have gone on to prominent positions in philosophy and related fields. His collaborative work with Franz Dietrich on judgment aggregation has been particularly influential. As Co-Director of the Munich Center for Mathematical Philosophy, he has secured substantial research funding and established MCMP as a leading international hub for formal and mathematical approaches to philosophical problems. Through the Munich Center for Mathematical Philosophy, List leads a vibrant research community that brings together philosophers, economists, political scientists, and mathematicians to tackle foundational questions using rigorous formal methods. The center hosts regular workshops, seminars, and visiting scholars, creating a dynamic intellectual environment that bridges disciplinary boundaries.