جست‌وجوی استاد و پژوهشگر

Florian Brandlمشاهده پروفایل

دانشیار

• Microeconomics
• Social Choice Theory
• Decision Theory
• +۴ مورد دیگر

Florian Brandl is an Argelander Professor (associate professor with tenure) at the University of Bonn, holding positions in both the Department of Economics and the Hausdorff Center for Mathematics. Previously, he was a postdoctoral research scholar at Princeton University and Stanford University. His academic career demonstrates a strong foundation in mathematical economics and game theory, with affiliations spanning multiple prestigious institutions. Brandl earned his Doctoral degree in Mathematics (summa cum laude) from the Technical University of Munich in 2018, following a Master's degree (2013) and Bachelor's degree (2011) from the same institution. His doctoral work focused on "Zero-Sum Games in Social Choice and Game Theory" under the supervision of Felix Brandt, establishing the foundation for his research trajectory. Prof. Brandl's research spans microeconomic theory with a focus on social choice theory, decision theory, and game theory. He is particularly interested in decision-making under uncertainty, connections between social choice and game theory, and dynamic processes converging to equilibrium. His work employs mathematical tools to analyze interactions of multiple entities in economic contexts, often incorporating algorithmic approaches and methods from theoretical computer science. He has made significant contributions to fair division, mechanism design, and probabilistic social choice. His publication record shows consistent contributions across multiple subfields, with recent work focusing on patience effects in fair division, social learning barriers, and axiomatic characterizations of equilibrium concepts. Brandl's research demonstrates strong interdisciplinary connections between economics, mathematics, and computer science, with publications in top journals across all three disciplines. Best Student Paper Award at WINE 2021 for "Funding Public Projects: A Case for the Nash Product Rule" Associate Editor for Theoretical Economics Co-organizer of the COMSOC Video Seminar Prof. Brandl actively contributes to academic service and community building. He co-organizes the COMSOC Video Seminar and will host a Trimester Program on "Advances in Mechanism Design" in Bonn in summer 2026. He serves on program committees for major conferences including COMSOC 2023 and EC 2023. His research has been supported through his position as a Bonn Junior Fellow at the Hausdorff Center for Mathematics since 2021. Based at the Institute for Microeconomics and affiliated with the Hausdorff Center for Mathematics, Brandl collaborates with a broad network of researchers across economics and computer science. His work often involves interdisciplinary collaboration, as evidenced by his numerous co-authored publications with researchers from various institutions worldwide. He maintains strong connections with the University of Oxford's Global Priorities Institute as a Research Affiliate.

Kelvin McQueenمشاهده پروفایل

استادیار

• Philosophy of Science
• Quantum Physics
• Philosophy of Mind
• +۵ مورد دیگر

Kelvin McQueen is an Assistant Professor of Philosophy at Chapman University's Schmid College of Science and Technology , with affiliations to the Institute for Quantum Studies . His academic journey includes a Bachelor of Arts and Master of Philosophy from the University of Otago , and a Ph.D. in Philosophy from the Australian National University (2013). Dr. McQueen's research bridges quantum mechanics and philosophy of mind , focusing on topics like the integrated information theory (IIT) of consciousness, Orch OR theory , and many-worlds interpretation . He explores how quantum collapse might relate to consciousness, mereological composition, and the philosophical implications of quantum theories. His recent publications analyze the empirical challenges to IIT , the quantum Zeno effect in measurement-collapse interpretations, and local causal explanations in many-worlds scenarios. Collaborations with prominent figures like David Chalmers and Lev Vaidman highlight his interdisciplinary approach. Dr. McQueen teaches courses in philosophy of science , quantum physics , metaphysics , and symbolic logic , emphasizing the intersection of philosophy and emerging technologies like virtual reality .

Sanjeev Kulkarniمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Statistical Learning Theory
• +۹ مورد دیگر

Sanjeev Kulkarni is the William R. Kenan, Jr. Professor of Electrical and Computer Engineering and Operations Research & Financial Engineering at Princeton University. He is associated with the Department of Philosophy and has held significant administrative roles including Dean of the Graduate School (2014-2017), Director of the Keller Center (2011-2014), and Master of Butler College (2004-2012). His research spans Statistics , Machine Learning , Applied Probability , Information Theory , and Signal Processing , with applications to Wireless Networks , Econometrics , and Control Systems . He has co-authored over 100 publications and supervised numerous PhD and Master’s students.

Xujie Siمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۵ مورد دیگر

Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.

Alicia L Carriquiryمشاهده پروفایل

استاد

• Forensic Science
• Statistics
• Nutrition Epidemiology
• +۳ مورد دیگر

Alicia L Carriquiry is a Distinguished Professor and President's Chair at Iowa State University, serving as Director of the Center for Statistics and Applications in Forensic Evidence (CSAFE). She holds a PhD in Statistics from Iowa State University (1989), an MS from the University of Illinois at Urbana-Champaign/ISU (1985/1986), and a BS from Universidad de la Republica in Uruguay (1981). Her research focuses on applying statistical methods to forensic science, nutrition epidemiology, and plant and animal breeding, with a particular emphasis on Bayesian frameworks. Recent research emphasizes forensic evidence analysis, including footwear impression algorithms, handwriting software development, and probabilistic evidence assessment tools. She also explores dietary intake patterns in populations across Latin America and Southeast Asia, addressing nutrient deficiencies and public health interventions. Her work bridges statistical rigor with practical applications in criminal justice, improving forensic methodologies through algorithmic innovation and interdisciplinary collaboration. As CSAFE Director, she leads initiatives to enhance statistical foundations in forensic disciplines, train practitioners, and develop open-source datasets. Notable contributions include database search methodologies, error rate analyses, and software tools like handwriter for handwriting analysis. Her research underscores the importance of probabilistic reasoning in legal contexts and addresses challenges in multi-camera source identification and nonlinear image distortion correction. Carriquiry’s leadership extends to editorial roles and professional service, advancing statistical standards in forensic science. She remains active in training programs and collaborative research projects, fostering reproducibility and relevance in scientific inquiry.

Xu Zeshuiمشاهده پروفایل

استاد

• Information Fusion
• Decision Making
• Cluster Analysis
• +۳ مورد دیگر

Zeshui Xu is a Full Professor at the Business School of Sichuan University, China, with concurrent roles as Adjunct Professor at multiple prestigious institutions including Nanjing University of Aeronautics and Astronautics and Southeast University. He has held distinguished positions such as Chair Professor at PLA University of Science and Technology and was part of the Cheung Kong Scholars Program. His research focuses on decision-making theory, information fusion, clustering algorithms, and fuzzy systems. He is a highly cited researcher (2014–2021) and holds fellowships from IEEE, RSA, IFSA, and others. Education: M.Sc. in Operations Research and Cybernetics, Qufu Normal University Ph.D. in Management Science, Southeast University Research Contributions: Dr. Xu has authored 18 Springer monographs and 700+ SSCI/SCI papers, achieving over 73,000 citations with an h-index of 138. His work includes groundbreaking contributions to intuitionistic fuzzy aggregation operators, hesitant fuzzy systems, and probabilistic linguistic decision-making frameworks. Awards & Recognition: Member of EASA and IASCYS Fellowships: IEEE, RSA, ORS, AAIA, and others Natural Science Awards (Ministry of Education) State Council Expert with Special Allowance Editorial Roles: Associate Editor/Editorial Board Member for over 30 journals, including IEEE Transactions on Cybernetics and Fuzzy Systems. Invited to 50+ academic conferences.

Marie-Colette van Lieshoutمشاهده پروفایل

استاد

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +۶ مورد دیگر

Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.

Timothy Williamsonمشاهده پروفایل

استاد

• Epistemology
• Metaphysics
• Philosophy of Language
• +۳ مورد دیگر

Timothy Williamson is a prominent Professor of Philosophy at the University of Oxford, holding the prestigious Wykeham Professorship of Logic. His academic career spans several decades with continuous contributions to analytic philosophy, particularly in epistemology, metaphysics, and philosophical logic. His work has positioned him as one of the leading philosophers of his generation. Williamson's research interests concentrate on epistemology (particularly his influential knowledge-first approach), metaphysics, philosophy of language, and philosophical logic. His work on vagueness champions epistemicism—the view that vague predicates have sharp boundaries that we cannot know. His book Knowledge and its Limits revolutionized epistemological thinking by placing knowledge rather than belief at the center of epistemological theory. Williamson has also made significant contributions to modal metaphysics, higher-order thought, and the philosophy of logic. His publication record reveals consistent engagement with foundational philosophical problems across several decades. His most recent work continues his exploration of knowledge-first epistemology while expanding into moral epistemology and methodological questions in philosophy. Williamson's articles demonstrate a distinctive blend of logical precision and philosophical depth, often using formal methods to address traditional philosophical problems. His work frequently intersects with debates in philosophy of language, metaphysics, and philosophical methodology. Williamson has been actively engaged in scholarly debate through numerous replies to critics, demonstrating his commitment to rigorous philosophical dialogue. His publications span major philosophy journals including Synthese , Canadian Journal of Philosophy , Analysis , and Philosophy and Phenomenological Research . Throughout his career, Williamson has mentored numerous graduate students and collaborated with leading philosophers across the globe. His methodological approach emphasizes the value of formal modeling in philosophical inquiry, arguing that philosophy can benefit from the model-building approaches common in natural sciences.

• Algorithmic Game Theory
• Computational Social Choice
• Artificial Intelligence
• +۳ مورد دیگر

Edith Elkind is a Professor of Computer Science at the University of Oxford and a Non-Tutorial Fellow at Balliol College (leaving October 2024). Her research focuses on algorithmic game theory and computational social choice. She joined Oxford in 2013 after roles at Nanyang Technological University (Singapore), Princeton University (PhD 2005), and postdoctoral positions at the University of Warwick, University of Liverpool, Hebrew University of Jerusalem, and University of Southampton. Her work addresses voting systems, cooperative games, and complexity analysis. Key contributions include studies on multiwinner elections, justified representation, and coalition formation. She has advised six PhD/Master’s students and authored/co-authored over 30 publications. Notable works include Properties of multiwinner voting rules (2017) and Justified representation in approval-based committee voting (2017), advancing fairness and efficiency in voting mechanisms. Her research spans theoretical computer science and AI, with applications in social choice theory and game dynamics. Collaborations include projects on foundational AI and computational social choice. She has contributed to foundational texts like Computational Aspects of Cooperative Game Theory (2011).

Keith J. Holyoakمشاهده پروفایل

استاد

• Cognitive Psychology
• Human Reasoning
• Analogy
• +۵ مورد دیگر

Keith J. Holyoak is a Distinguished Professor in the Department of Psychology at the University of California, Los Angeles (UCLA), where he conducts foundational research in cognitive psychology. His work centers on human reasoning, learning, decision making, and problem solving, with a specific focus on the psychological mechanisms of analogy and relational knowledge across diverse domains including law, politics, mathematics, and science. He directs the Reasoning Lab at UCLA, integrating experimental, computational, and neuroimaging approaches to investigate cognitive processes. Education: Ph.D. from Stanford University Research Focus: Holyoak's research program systematically explores how analogy facilitates knowledge transfer and learning, examining the neural underpinnings of complex reasoning with emphasis on prefrontal cortex functions. His work bridges theoretical cognitive science with practical applications, investigating causal learning, deductive processes, and the constraints shaping human inference. Through computational modeling and cross-domain studies, he reveals universal principles governing relational reasoning while addressing domain-specific manifestations in scientific, legal, and social contexts. Publication Trends: His scholarly output demonstrates consistent evolution from foundational work on pragmatic reasoning schemas (1980s) toward integrated models of causal learning and Bayesian inference (2000s-2010s). Recent publications emphasize rational analysis frameworks, cross-species comparisons in causal cognition, and the role of invariance principles in knowledge generalization. The corpus reveals deep methodological pluralism—combining behavioral experiments, computational modeling, and neuroimaging—to address fundamental questions about the architecture of human thought. Research Infrastructure: Holyoak leads the Reasoning Lab (https://reasoninglab.psych.ucla.edu), which maintains a collaborative environment for interdisciplinary research. The lab's infrastructure supports advanced experimental paradigms, computational modeling suites, and neurocognitive investigations, facilitating research on analogical transfer, causal inference, and decision-making under uncertainty across the lifespan.

Carolyn Conner Seepersadمشاهده پروفایل

استاد

• Design for Additive Manufacturing
• Engineering Design
• Additive Manufacturing
• +۴ مورد دیگر

Carolyn Conner Seepersad serves as the J. Mike Walker Professor of Mechanical Engineering at the University of Texas at Austin and directs the Center for Additive Manufacturing and Design Innovation. She holds membership in the U.T. System Academy of Distinguished Teachers and maintains active leadership in the additive manufacturing community through roles such as co-organizer of the Solid Freeform Fabrication Symposium and ASME Design Engineering Division Executive Committee membership. Her academic credentials include: PhD in Mechanical Engineering from Georgia Tech (2004) MA/BA in Philosophy, Politics and Economics from Oxford University (1998, Rhodes Scholar) BS in Mechanical Engineering from West Virginia University (1996) Dr. Seepersad's research centers on computational design methodologies and additive manufacturing innovation , with particular expertise in simulation-based design of complex systems, environmentally conscious product development, and materials engineering. Her work bridges theoretical design frameworks with practical manufacturing applications, emphasizing sustainability and performance optimization across aerospace, automotive, and energy systems. Current projects explore reactive extrusion additive manufacturing, negative stiffness materials, and machine learning integration for process-aware design. Analysis of her 15 most recent publications reveals a dominant focus on process innovation in additive manufacturing (70%), particularly stereolithography and selective laser sintering, with growing emphasis on data-driven design approaches (20%) and sustainable engineering applications (10%). Her work demonstrates consistent progression from fundamental material design toward integrated system optimization and industrial scalability. Her scientific recognition includes: International Outstanding Young Researcher Award in Freeform and Additive Manufacturing (2009) UT System Regents’ Teaching Award (2010) ASME Design Automation Committee Outstanding Young Investigator Award (2010) ASEE Outstanding New Mechanical Engineering Educator Award (2013) Multiple ASME and ASEE best paper awards U.T. System Academy of Distinguished Teachers membership Dr. Seepersad maintains an extensive advising portfolio with 48 graduate students (16 PhD, 24 MS, and 8 current) plus 2 postdoctoral researchers, reflecting sustained research productivity and educational impact. Her Product, Process, and Materials Design Lab fosters interdisciplinary collaboration between mechanical engineering, materials science, and computational design teams.

Lirong Xiaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +۹ مورد دیگر

Lirong Xia is a Professor of Computer Science at Rutgers University - New Brunswick and Deputy Director of DIMACS (Center for Discrete Mathematics and Theoretical Computer Science). He holds a Ph.D. in Computer Science from Duke University, an M.A. in Economics from Duke, and a B.E. in Computer Science and Technology from Tsinghua University. His research focuses on the intersection of artificial intelligence, machine learning, and social choice theory, addressing challenges in voting systems, fair division, privacy, and multi-agent systems. Key research areas include algorithmic fairness, computational social choice, and mechanism design. Recent work explores equitable voting rules, privacy-preserving mechanisms, and strategic behavior analysis. Notable publications include advancements in computational social choice and privacy in voting systems. Xia has been recognized with prestigious awards such as the NSF CAREER Award and IEEE’s “AI’s 10 to Watch.” Education: Ph.D. Computer Science, Duke University (2011) M.A. Economics, Duke University (2010) B.E. Computer Science and Technology, Tsinghua University (2004) Awards: NSF CAREER Award Simons-Berkeley Research Fellowship 2018 Rensselaer James M. Tien’66 Early Career Award IEEE Intelligent Systems “AI’s 10 to Watch” Advising: Supervised over 30 students, including PhDs and master’s candidates in AI, algorithms, and social choice theory.

Sandrine Dudoitمشاهده پروفایل

استاد

• Statistical Methodology
• Computational Biology
• Genomics
• +۵ مورد دیگر

Sandrine Dudoit is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She earned her PhD in Statistics from UC Berkeley in 1999 and joined the faculty in 2001. Her research focuses on statistical methodology and computing with applications to genomics, biomedical research, and precision health. She co-founded the Bioconductor Project , an open-source software initiative for biological data analysis, and leads interdisciplinary projects in single-cell transcriptomics and computational biology. Education: PhD in Statistics (UC Berkeley, 1999), M.Sc. in Mathematics (Carleton University, Canada). Research interests include high-dimensional statistical learning, single-cell RNA-Seq analysis, stem cell differentiation in the olfactory system, and statistical computing. She collaborates with biologists like John Ngai to study neuroepithelial regeneration using cutting-edge sequencing technologies. Recent work emphasizes trajectory inference, biomarker discovery, and methodological advances in handling high-dimensional genomic data. Her lab develops tools for normalization, clustering, and differential expression analysis in large-scale biological datasets. She teaches courses on statistical genomics and serves as a leader in UC Berkeley’s Division of Computing, Data Science, and Society (CDSS). Advising: Supervises PhD students in statistical methodology, computational biology, and bioinformatics. Grants: Active in securing funding for interdisciplinary research projects in genomics and data science. Labs/Teams: Core member of the Center for Computational Biology (CCB) and contributes to the Bioconductor community.

Richard Bradleyمشاهده پروفایل

استاد

• Decision Theory
• Formal Epistemology
• Philosophy of Probability
• +۴ مورد دیگر

Richard Bradley is Professor of Philosophy at the London School of Economics , focusing on decision theory, formal epistemology, and philosophy of science. His work bridges rational decision-making under uncertainty, conditional reasoning, and policy implications of probabilistic models. Key affiliations: London School of Economics, Department of Philosophy, Logic and Scientific Method Education: University of Witwatersrand (undergraduate), LSE (MSc), University of Chicago (PhD) Research covers: Decision theory with human-centric uncertainty models Formal epistemology of conditionals and belief revision Philosophy of climate change and pandemic policy decisions Bayesian utilitarianism and social choice theory His 2017 book Decision Theory with a Human Face integrates these themes. Scientific contributions include: 15+ peer-reviewed articles on conditionals, uncertainty, and social ethics Collaborations with H. Orri Stefánsson on risk attitudes Work with Roman Frigg on model ensemble decision-making Philosophy of Climate Science in IPCC assessments Major projects address confidence trade-offs in climate science and pandemic policy frameworks.

Per-Arne Andersenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Reinforcement Learning
• Tsetlin Machines
• +۷ مورد دیگر

Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .