جست‌وجوی استاد و پژوهشگر

Johanna Ziegelمشاهده پروفایل

استاد

• Statistical Forecasting
• Probability Theory
• Financial Risk Management
• +۳ مورد دیگر

Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.

• Artificial Intelligence
• Multi-agent systems
• Machine learning
• +۵ مورد دیگر

Toshiharu Sugawara is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering, a position he has held since April 2007. With a Ph.D. in Engineering from Waseda University, his research spans multiple domains in artificial intelligence and multi-agent systems, maintaining active collaborations across international institutions and contributing significantly to the field through numerous publications and awards. Dr. Sugawara received his BS and MS degrees in Mathematics from Waseda University in 1980 and 1982, respectively, followed by his Ph.D. in 1992. Before joining Waseda University as faculty, he worked as a Research Scientist at NTT Laboratories from 1982 to 2007, with a visiting researcher position at the University of Massachusetts at Amherst in 1992-1993. He also held part-time lecturer positions at University of Electro-Communications (2003-2007), Waseda University (2004-2006), and Tokyo University of Agriculture and Technology (1990-1991). His research interests focus on artificial intelligence with particular expertise in multi-agent systems, machine learning, cooperation and coordination mechanisms, soft computing, computational social science, and social informatics. His work bridges theoretical foundations with practical applications in network management and information systems. Recent publications demonstrate a strong trajectory toward interpretable multi-agent reinforcement learning, efficient path planning algorithms, and modeling social behaviors in complex networks. His research group has made significant contributions to multi-agent path finding, cooperative task execution, and understanding virtual economies in social media platforms. Dr. Sugawara has received numerous prestigious awards including multiple Best Paper Awards at JAWS conferences (2014, 2015, 2018), ACM SAC 2015, and various research paper awards from Japanese academic societies. His work on multi-agent systems has been consistently recognized for its theoretical rigor and practical impact. As an advisor, Dr. Sugawara has mentored numerous students who have become prominent researchers in their own right, with many co-authoring papers that have received awards. His laboratory maintains strong collaborations with industry partners, particularly in the areas of network management and intelligent systems. Current research directions include developing interpretable multi-agent reinforcement learning frameworks, optimizing multi-agent coordination in constrained environments, and analyzing social dynamics in virtual economies.

Franz Huberمشاهده پروفایل

دانشیار

• Formal Epistemology
• Philosophy of Science
• Logic
• +۱ مورد دیگر

Franz Huber is an Associate Professor in the Department of Philosophy at the University of Toronto (St. George campus). He holds an MA from the University of Salzburg, Austria, and a PhD from the University of Erfurt, Germany. His primary research areas include Formal Epistemology, Philosophy of Science, Philosophical Logic, and Metaphysics. Huber’s work focuses on belief revision, counterfactual reasoning, and means-end rationality, often employing formal methods to address philosophical problems. His publications include influential books like A Logical Introduction to Probability and Induction (2018) and Belief and Counterfactuals (2021), as well as numerous peer-reviewed articles in journals such as Synthese and Journal of Philosophical Logic . His research explores the foundations of epistemology and metaphysics, particularly through ranking theory and its applications to belief change, counterfactuals, and causal reasoning. Huber has also contributed to the philosophy of science, examining confirmation theory and the logic of scientific reasoning. His recent work emphasizes a means-end approach to philosophical problems, integrating instrumental rationality with normative theory. Huber’s publications demonstrate a commitment to bridging formal methods with traditional philosophical inquiry. His academic contributions include editing volumes on belief revision and co-authoring works on ranking theory. He maintains an active research agenda, reflected in his latest book Causality, Counterfactuals, and Belief (2025). His office is located in the Jackman Humanities Building, and he is affiliated with the University of Toronto’s philosophy faculty and research initiatives such as the Balzan Research Project on Styles of Reasoning.

Lionel Levineمشاهده پروفایل

استاد

• Applied Mathematics
• Combinatorics and Discrete Geometry
• Probability and Statistics
• +۴ مورد دیگر

Lionel Levine is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His academic research focuses on abelian networks, interacting particle systems, and the emergence of complex patterns from simple rules. He has held prestigious fellowships, including the Simons Fellowship and Sloan Research Fellowship, and has been honored with an endowed professorship. Levine's work bridges probability theory, combinatorics, and statistical physics, with notable contributions to the study of sandpile models and internal diffusion-limited aggregation (IDLA). Education: Ph.D. in Mathematics (2007), University of California, Berkeley. Research Interests: Applied Mathematics, Combinatorics, Probability, Abelian Networks, Sandpile Models, and their intersections with computer science and statistical physics. His research explores how local rules generate large-scale structures, such as in abelian networks and sandpile models. Awards and Honors: Simons Fellowship, Sloan Research Fellowship, Endowed Professorship in the College of Arts and Sciences. Teaching: Courses include Probability Theory (MATH 6710/6720), Topics in Probability: Math for AI Safety (MATH 7710), and undergraduate mathematics courses like Strategy, Cooperation, and Conflict (MATH 1340). Grants and Funding: Supported by the National Science Foundation (NSF), Simons Foundation, Sloan Foundation, and Institute for Advanced Study. Collaborations: Collaborates with prominent researchers such as Yuval Peres, Cris Moore, and Jim Propp. His work has been published in leading journals like the Annals of Probability and Duke Mathematical Journal. Future Work: Continues investigating AI safety, causal models, and multi-agent learning, including research on mathematical frameworks for transformer circuits and hidden incentives in AI systems.

Mustafa Bilgicمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Active Learning
• +۵ مورد دیگر

Mustafa Bilgic is a Professor and Chair of the Computer Science Department at Illinois Institute of Technology, where he also directs the Master of Artificial Intelligence program and the Machine Learning Laboratory. His research focuses on machine learning, active learning, explainable AI, and probabilistic graphical models, with applications in healthcare, social media analysis, and biomedical engineering. He has received funding from NSF, NIH, and Samsung, among others. Education: PhD in Computer Science, University of Maryland at College Park (2010) M.S. in Computer Science, University of Maryland at College Park (2006) B.S. in Computer Science, University of Texas at Austin (2004, with High Honors and Special Honors) Research Highlights: Dr. Bilgic's work emphasizes AI ethics, algorithm transparency, and interactive machine learning systems. Notable projects include analyzing political news engagement dynamics and developing frameworks for eliminating explanation noise in AI models. His lab explores tools like OrganoID for tracking organoid growth and IDGI for improving model interpretability. Awards: NSF CAREER Award (2014) ACM SIGKDD Best Student Paper Award (2008) Illinois Tech College of Computing Teaching Excellence Award (2021) Teaching and Leadership: Bilgic teaches advanced courses in AI, machine learning, and data mining. He leads initiatives to bridge AI theory and practical applications, emphasizing interdisciplinary collaboration. His administrative roles include overseeing the AI master’s program and fostering innovation in computing education.

Rudiger Campeمشاهده پروفایل

استاد

• Literary Theory
• Aesthetics
• Rhetoric
• +۷ مورد دیگر

Rudiger Campe is the Alfred C. and Martha F. Mohr Professor of German Languages and Literatures at Yale University, with a joint appointment in Comparative Literature since 2016. He has been a Professor of German at Yale since 2007 and previously held a professorship at Johns Hopkins University from 2001 to 2007. His research spans literary theory, aesthetics, rhetoric, and the philosophical foundations of literature . He explores intersections between literature and probability, the history of aesthetics, and the epistemology of the humanities, with deep engagement in figures such as Vico, Dilthey, Foucault, Husserl, and Blumenberg. Campe’s recent publications reveal a strong focus on theoretical foundations of literature and aesthetics , particularly in German and classical traditions. His work frequently bridges ancient rhetoric, Enlightenment thought, and modern literary theory, with recurring themes of evidence, reference, and the emotional and cognitive structures of literary forms. Scientific Awards and Honors: Humboldt Research Award (2011) Fellow, Whitney Humanities Center (2012–2014) Szondi-Lecture, FU Berlin (2013) Luebeck Lecture, Ohio State University (2014) Fellow, Internationales Forschungszentrum Kulturwissenschaften, Vienna (2016) Campe has been actively involved in academic leadership, including co-convening the Sawyer Seminar The Excessive Screen at Yale. He regularly presents at major international conferences and institutions, demonstrating his role as a leading figure in comparative literary studies. He has also contributed significantly to edited volumes and interdisciplinary dialogues in the humanities.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۲ مورد دیگر

Geoffrey Hinton is a Professor in the Department of Computer Science at the University of Toronto , where he has been a pivotal figure in advancing artificial intelligence research. His work focuses on neural networks, deep learning, and machine learning, revolutionizing how machines process information and learn from data. With collaborations spanning institutions like NYU and IIT Mumbai, Hinton’s influence extends beyond academia into public discourse through lectures like the Romanes Lecture (2024) . His research explores Deep Belief Networks , Gradient Methods , Neural Network Architectures , and Probabilistic Models , with recent publications addressing novel algorithms like the Forward-Forward Algorithm and frameworks for Panoptic Segmentation . Though he no longer accepts students, current advisees include Jimmy Lei Ba and Cem Anil. Hinton’s contributions to AI are complemented by media engagements, including CBS 60 Minutes (2023) and CNN Amanpour (2023) , reflecting his role as a thought leader. His technical outputs, such as Nature Deep Learning Review (2015) with Y. LeCun and Y. Bengio, remain foundational texts in the field.

Bernhard Salowمشاهده پروفایل

دانشیار

• Epistemology
• Formal Epistemology
• Philosophy of Mind
• +۳ مورد دیگر

Bernhard Salow is an Associate Professor of Philosophy and Tutorial Fellow at Magdalen College, University of Oxford. His academic journey includes a Ph.D. in Philosophy from MIT (2011–2015), a BPhil from New College, Oxford (2009–2011), and an MMathPhil from St Hugh’s College, Oxford (2005–2009). He previously held a Junior Research Fellowship at Trinity College, Cambridge (2015–2018). Salow’s research focuses on formal epistemology, particularly exploring topics like the KK principle, belief revision, and externalism. His work bridges philosophical analysis with formal methods, addressing issues in decision theory, probability theory, and epistemic risk. Recent contributions include examining iterated knowledge, the value of evidence, and normative frameworks for belief revision. His publications span journals such as Mind , Noûs , and Philosophical Studies , with a strong emphasis on rigorous theoretical exploration. He maintains an active academic blog and personal website at http://users.ox.ac.uk/~shug2406/ . Salow’s teaching responsibilities at Magdalen College involve mentoring undergraduates in Philosophy and Mathematics. His research often intersects with interdisciplinary questions in philosophy of science and cognitive science, reflecting a commitment to both theoretical depth and practical relevance.

• Computational Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects

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 .

• Linguistics
• Syntax
• Ellipsis
• +۳ مورد دیگر

Dr. Tyll Robin Lemke is a researcher at Saarland University in the Department of Modern German Linguistics (Neuere deutsche Sprachwissenschaft). He serves as a scientific staff member in Project B3 of the Collaborative Research Center SFB 1102. His academic focus includes experimental linguistics, syntax, and psycholinguistics, with specialized expertise in ellipsis phenomena and fragment analysis. Lemke's research explores the cognitive underpinnings of language production and comprehension, particularly investigating how predictability, context, and information theory shape linguistic structures. His work employs diverse methodologies including corpus analysis, psycholinguistic experiments, computational modeling, and gamified experimental paradigms to examine ellipsis, fragments, and syntactic phenomena in German. Analysis of Lemke's recent publications reveals consistent themes: the role of predictability in language production, constraints on ellipsis resolution, information-theoretic approaches to language efficiency, and experimental validation of syntactic theories. His research bridges theoretical linguistics with cognitive science through innovative experimental designs. In the upcoming 2025/26 winter semester, Lemke is teaching courses on Experimental Linguistics and Ellipsis in Theory and Experiment. He maintains an active research program within the SFB 1102 collaborative framework and regularly presents at international linguistics conferences.

Long Nguyenمشاهده پروفایل

استاد

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +۴ مورد دیگر

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

• Mathematics
• Physics
• Biology
• +۲۳ مورد دیگر

Andrei Khrennikov is Professor of Mathematics at the Department of Mathematics, Linnaeus University, where he also serves as director of the International Center for Mathematical Modeling (ICMM) . He leads a vibrant research group focused on interdisciplinary modeling in physics, biology, cognition, and social systems. Research Interests: His work spans a vast interdisciplinary landscape, including mathematical physics, p-adic and non-Archimedean analysis, quantum foundations, quantum-like modeling of cognition and decision-making, econophysics, and biological dynamics . He is a pioneer in applying quantum probability and formalism outside quantum physics, especially in psychology and social sciences. The Växjö series of quantum theory conferences , which he organizes, is the longest-running continuous conference series on quantum foundations, fostering dialogue between theorists, experimentalists, and philosophers. His recent publications (2021–2025) show a strong focus on quantum cognition, p-adic biology, entanglement models, and social laser theory , often leveraging generalized probability and open quantum systems frameworks. Scientific Contributions: Developed quantum-like models for cognition, decision-making, and biological processes. Pioneered use of p-adic and ultrametric analysis in genetics and brain dynamics. Advanced classical random field models as alternatives to quantum interpretations. Introduced the social laser model for collective emotional amplification in societies. He is actively involved in major research projects such as QUARTZ (Quantum Information Access and Retrieval Theory) and DYNALIFE (Information, Coding, and Biological Function) . His work bridges mathematics, physics, and cognitive science, promoting a unified framework for understanding complex systems through quantum-inspired tools.

Josh Pasekمشاهده پروفایل

استاد

• Political Communication
• Survey Methodology
• New and Emerging Technologies
• +۳ مورد دیگر

Josh Pasek is a Professor of Communication & Media and Political Science at the University of Michigan, with additional affiliations as a Research Professor at the Center for Political Studies (Institute for Social Research) and Core Faculty at the Michigan Institute for Data Science. His research focuses on the intersection of political communication, survey methodology, and data science, particularly examining how new media and psychological processes shape political attitudes, public opinion, and measurement accuracy. Co-author of Words That Matter and Democracy Amid Crises Maintainer of R packages anesrake and weights Key research areas include: Political Communication Survey Methodology Media Psychology Social Media Data Electoral Behavior Ballot Design His recent publications address topics such as: Vaccine hesitancy and information environments Phubbing and trust dynamics Supreme Court legitimacy post-Dobbs Partisan polarization during the pandemic Climate change perception and partisanship Measurement error in surveys He has collaborated with institutions like the Annenberg Public Policy Center, Georgetown University, and the Pew Research Center, and serves on the AAPOR Task Force for 2024 Pre-Election Polls.

• Autism Spectrum Disorder
• Biostatistical Methods
• Chronic disease
• +۴ مورد دیگر

Xiaoyu Che is an Assistant Professor of Biostatistics at Columbia University's Mailman School of Public Health, where he serves as the principal biostatistician in the Center for Infection and Immunity (CII) at Columbia University Irving Medical Center. His work bridges statistical methodology with biomedical research, focusing on complex disease mechanisms through advanced data analysis approaches. Dr. Che received his academic training at prestigious institutions: BS in Mathematics from Zhejiang University (2006) PhD in Mathematics from Claremont Graduate University (2013) Dr. Che's research program centers on the development and application of statistical methods for multi-omics analyses, with particular focus on understanding the pathogenesis of chronic and neurodevelopmental conditions. His work spans multiple domains including Autism Spectrum Disorder (ASD), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and Gulf War Illness (GWI). He employs sophisticated biostatistical approaches to integrate diverse biological data types, revealing novel insights into disease mechanisms. His methodological expertise includes Bayesian statistics, metabolomic analysis, immune signature identification, and microbiome characterization, all aimed at translating complex biological data into meaningful clinical insights. Analysis of Dr. Che's publication record reveals a strong thematic focus on applying advanced statistical methods to understand complex disease mechanisms. His work consistently bridges biostatistical innovation with biomedical discovery, particularly in the areas of neurodevelopmental disorders and chronic fatigue conditions. The publications demonstrate progression from foundational methodological work to increasingly sophisticated multi-omics integration approaches, reflecting both technical growth and expanding research impact. His collaborative approach is evident through numerous high-impact publications with interdisciplinary teams across Columbia University and beyond. Dr. Che teaches BIST P8104: Probability in the Biostatistics MS degree program at Columbia, demonstrating his commitment to training the next generation of biostatisticians. While specific grant information isn't detailed in the provided materials, his extensive publication record across multiple high-impact journals suggests successful grant funding supporting his research program. As principal biostatistician in the Center for Infection and Immunity, Dr. Che plays a critical role in the analytical framework of the center's research initiatives. His work supports the center's mission to understand the relationship between infectious agents and human health through rigorous quantitative analysis. The collaborative nature of his research is evident in the diverse range of co-authors spanning immunology, virology, microbiology, and clinical medicine.