جست‌وجوی استاد و پژوهشگر

Richard Arratiaمشاهده پروفایل

استاد

• Probability
• Combinatorics
• Number Theory
• +۵ مورد دیگر

Richard Arratia is a Professor of Mathematics at the University of Southern California, affiliated with the College of Letters, Arts and Sciences and the Center for Applied Mathematical Sciences. His research focuses on discrete probability, combinatorics, and number theory, particularly the interplay between dependence and independence in probabilistic systems. Education : B.S. from Massachusetts Institute of Technology, Ph.D. from University of Wisconsin, Madison. His work explores approximations of probabilities for rare events, continuum limits of discrete systems (e.g., coalescing Brownian motions), and logarithmic combinatorial structures such as permutations with cycle decompositions and prime factorizations of integers. He has co-authored a foundational book on logarithmic combinatorial structures and published extensively on topics including Poisson approximation, random walks, symmetric exclusion processes, and interlace polynomials for graphs. His recent publications highlight applications of probabilistic methods to integer partitions, graph theory, and statistical analysis. Key themes include exact simulation techniques, graphical sequences, and connections between analytic number theory and probabilistic models. Professor Arratia serves as faculty advisor to Pi Mu Epsilon, the undergraduate mathematics honors society, and has contributed to research in probability asymptotics, logistic regression, and algebraic function fields.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Dale Schuurmans is a Professor in the Faculty of Science at the University of Alberta, specializing in Computing Science. His research focuses on developing systems that learn predictive models from massive data sources, particularly in complex domains like perception, language interpretation, and bioinformatics. Education: B.Sc. Mathematics, University of Alberta (1985) B.Sc. Computing Science, University of Alberta (1986) M.Sc. Computing Science, University of Alberta (1988) Ph.D. Computer Science, University of Toronto (1996) His research interests span artificial intelligence, machine learning, reinforcement learning, probability modeling, optimization, and search algorithms. Schuurmans investigates fundamental challenges in knowledge representation for learning and navigating complex model spaces to prevent over/under-fitting. Schuurmans' recent publications demonstrate a strong focus on advancing fundamental machine learning techniques, particularly in reinforcement learning, optimization methods, and generative models. His work shows consistent emphasis on theoretical foundations of deep learning, model generalization, and efficient training algorithms. While no specific awards are mentioned, Schuurmans maintains an active research program investigating statistical natural language modeling, reinforcement learning, and learning search control. He has developed novel methods for probabilistic inference, optimization, and constraint satisfaction.

Dawn Holmesمشاهده پروفایل

استاد آموزشی

• Bayesian Networks
• Machine Learning
• Maximum Entropy Formalism
• +۴ مورد دیگر

Dr. Dawn E. Holmes is a Teaching Professor at the University of California, Santa Barbara (UCSB), affiliated with the Department of Statistics and Applied Probability and the College of Letters and Science. She holds dual office locations: South Hall 5506 (primary) and Cheadle Hall 1117F. Her work focuses on Bayesian Networks, Maximum Entropy Formalism, Machine Learning, and statistical education innovation. She received the Academic Senate Distinguished Teaching Award 2007-2008 for her contributions to pedagogy. Research Interests: Holmes' research spans foundational topics in Bayesianism, data mining, and intuitionistic mathematics. She has pioneered methods for handling incomplete information in causal networks using entropy optimization and has contributed to probabilistic decision support systems. Her work bridges theoretical advancements with practical applications in education and healthcare. Editorial Contributions: Holmes serves on editorial boards for journals such as The Reasoner , Journal of Supercomputing , and International Journal of Knowledge-Based and Intelligent Information Systems . She has guest-edited special issues on maximum entropy applications and authored/co-edited influential books in data mining and Bayesian networks. Teaching and Mentorship: Holmes teaches both undergraduate and graduate courses in statistics, stochastic processes, and Bayesian methods. She has supervised PhD students and internships, emphasizing statistical computing and pedagogical innovation. Her courses include PSTAT 5A (Statistics), PSTAT 160A/B (Stochastic Processes), and specialized topics like Bayesian Networks (PSTAT 262BN). Awards and Recognition: Beyond her teaching award, Holmes has published over 50 peer-reviewed articles and authored/co-edited eight books. Her work appears in venues like Statistical Methods in Medical Research and Bayesian Inference and Maximum Entropy Methods . Labs and Teams: While no specific lab is named, her research collaborations involve interdisciplinary teams addressing challenges in data mining, healthcare analytics, and educational technology. Her editorial roles reflect active participation in global academic networks.

David Kinneyمشاهده پروفایل

استادیار

• Formal Epistemology
• Causality
• AI Ethics
• +۳ مورد دیگر

David Kinney is an Assistant Professor in the Department of Philosophy and Philosophy-Neuroscience-Psychology (PNP) at Washington University in St. Louis. His research bridges formal epistemology, causality, and AI ethics, focusing on topics such as algorithmic fairness, causal representation, and interdisciplinary approaches to understanding cognition and decision-making. He explores how mathematical and computational models can clarify ethical challenges in predictive systems, precision medicine, and large language models. Key research interests include causal theories, stochastic modeling, and the philosophical underpinnings of scientific explanation. His work often integrates cognitive science, computer science, and mathematics to address questions about knowledge representation, resource allocation in reasoning, and the social implications of AI. Recent publications highlight his focus on aggregating fairness/accuracy metrics in predictive systems, causal model compression, and geo-culturally grounded AI. His articles frequently intersect with ethics, statistics, and computational methods, reflecting a commitment to interdisciplinary problem-solving. No scientific awards are listed, though his work demonstrates significant contributions to formal epistemology and applied ethics. Dr. Kinney’s advising and grants activities are not detailed in the provided text. He is affiliated with the Philosophy department at Washington University and contributes to PNP initiatives exploring mind-brain connections. No lab affiliations or teams are explicitly mentioned, though his research collaborations likely span computer science, neuroscience, and ethics programs.

Yuhao Baمشاهده پروفایل

استادیار

• Environmental Governance
• Public Policy Process
• Non-state Governance
• +۳ مورد دیگر

Yuhao Ba is an Assistant Professor at the Lee Kuan Yew School of Public Policy, National University of Singapore. His research focuses on non-state governance, environmental policy, and public policy processes. He holds a PhD in Public Administration from North Carolina State University. Research interests include corporate-led environmental governance, interactions between governance systems and policy processes, and sustainability outcomes. He is also committed to promoting equity and inclusion in public policy through his work with APPAM’s Equity & Inclusion fellowship. Education: PhD (Public Administration), North Carolina State University Office: 469C Bukit Timah Road, Singapore 259772 Email: yba@nus.edu.sg His recent work explores themes like policy innovation diffusion, climate change mitigation, and organizational performance evaluation. Publications span journals in public policy, environmental governance, and statistical methodologies. No grants or student advisement information is listed in the provided materials.

Sarah Filippiمشاهده پروفایل

استاد

• Statistical Machine Learning
• Bayesian Statistics
• Biostatistics
• +۵ مورد دیگر

Sarah Filippi is a Professor and Reader in Statistical Machine Learning at the Department of Mathematics, Imperial College London, and Joint Director of the EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) at Imperial and Oxford. She holds affiliations with multiple interdisciplinary networks, including the CNRS-Imperial Abraham de Moivre UMI, AI for Healthcare, and the Wound Healing and Regeneration Network. Her research focuses on Bayesian methods, nonparametric approaches, and their applications in biomedical problems, including systems biology, pharmacology, and epidemiology. Dr. Filippi earned her Ph.D. in Statistical Machine Learning from Télécom ParisTech in 2010. She has held roles at Imperial College London (2011–2014 as an MRC Fellow, 2017–present in various senior positions) and the University of Oxford (2014–2017). Her work bridges statistical theory and practical biomedical challenges, emphasizing uncertainty quantification and scalable algorithms. Her research group develops methods for causal inference, kernel-based learning, and decision-making under uncertainty. Recent publications highlight contributions to clustering algorithms, reinforcement learning, and epidemiological studies. She has received the Medical Research Council Fellowship (2011–2014) and leads collaborative projects with clinicians and computational biologists. Dr. Filippi advises a vibrant research group, including Ph.D. students working on continual learning, Bayesian optimization, and healthcare applications. Her interdisciplinary collaborations span computational biology, chronic disease modeling, and material flow analysis, reflecting her commitment to translational research.

Kean Ming Tanمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• High-Dimensional Data Analysis
• Quantile Regression
• +۷ مورد دیگر

Kean Ming Tan is an Associate Professor of Statistics at the University of Michigan, Department of Statistics (College of Literature, Science, and the Arts). His research focuses on statistical machine learning methods for analyzing complex datasets, including multivariate statistical techniques such as probabilistic graphical models, cluster analysis, discriminant analysis, and dimension reduction. He also develops robust statistical methods, quantile regression approaches, non-convex optimization algorithms, and data integration strategies. Recently, his work has involved applying instrumental variables to address unmeasured confounders in models. Education background and academic training details are not explicitly listed in the provided texts. His research interests span both theoretical and applied domains, with contributions to high-dimensional data analysis, computational statistics, and interdisciplinary applications in neuroscience and econometrics. He co-organized the 2022 'Modern Statistical and Machine Learning Methods for Big Data Workshop' in Ann Arbor, Michigan. His publications emphasize methodological advancements in quantile regression, robust estimation, and scalable inference techniques. Key themes include handling large-scale datasets, improving model interpretability, and addressing challenges posed by high-dimensionality and latent confounding variables. While specific advising/grant details are not detailed here, his work reflects active engagement in mentoring students and collaborative research initiatives.

Leonid Sigalمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Computer Graphics
• +۸ مورد دیگر

Leonid Sigal is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), holding the NSERC Canada Research Chair (CRC II) in Computer Vision and Machine Learning and a CIFAR AI Chair at the Vector Institute. He also serves as an Academic Advisor to Borealis AI. His research focuses on visual understanding, including object recognition, scene analysis, motion capture, and the intersection of computer vision with machine learning and graphics. Prior roles include Senior Research Scientist at Disney Research and Adjunct Faculty at Carnegie Mellon University. Education: Ph.D. (Computer Science, Brown University, 2008), M.Sc. (Computer Science, Brown University, 2003), B.Sc. (Computer Science & Mathematics, Boston University, 1999). Research interests span computer vision challenges such as human motion analysis, activity recognition, and generative models, alongside cross-disciplinary work in robotics and psychology. Notable achievements include Best Paper Awards at WACV 2015 and AMD 2012/2006, and leadership in organizing workshops like VisStory and tutorials on 'Looking at People.' Awards include the Killam Accelerator Fellowship, NSERC DAS award, and recognition as Area Chair for CVPR 2022, ICML 2022, and IJCAI 2022. His lab advises numerous graduate students and postdocs, with active collaborations spanning industry (e.g., Disney, Borealis) and academia (e.g., CMU, Vector Institute).

• Machine Learning
• Artificial Intelligence
• Bayesian Methods
• +۵ مورد دیگر

Mark van der Wilk is an Associate Professor in the Department of Computer Science at the University of Oxford and a Tutorial Fellow at Hertford College. His research focuses on machine learning, particularly Bayesian methods, equivariance in neural networks, and applications in science and industry. He leads a research group addressing challenges in generalization, architecture design, and efficient learning. Education: PhD in Machine Learning from the University of Cambridge (2017), undergraduate degree in Engineering. Previously held academic positions at Imperial College London and worked in industry with Dr. James Hensman. Research Interests: Automatic discovery of inductive biases, architecture optimization, Bayesian deep learning, causal inference, and applications in bioengineering and chemistry. Collaborates with industry and academia on practical machine learning solutions. Awards: Best Paper Award at NeurIPS/ICML conferences, Qualcomm Innovation Fellowship (PhD). Active in open-source contributions, including GPflow and GPflux libraries. Key Projects: Development of Competitive Amplification Networks for molecular diagnostics, turbulence testing for LLMs, and sparse Gaussian process approximations. Supervises PhD students focusing on alignment of mathematical and practical machine learning challenges.

Srinivas Aluruمشاهده پروفایل

استاد

• High Performance Computing
• Data Analytics
• Bioinformatics
• +۳ مورد دیگر

Srinivas Aluru is a Regents' Professor and Senior Associate Dean at the Georgia Institute of Technology's College of Computing , within the School of Computational Science and Engineering . His research focuses on High Performance Computing , Bioinformatics , Systems Biology , and Applied Algorithms . He has pioneered parallel methods in computational biology, contributing to plant genome assembly and analysis. Current work includes bioinformatics for high-throughput DNA sequencing and systems biology network inference using Bayesian and mutual information approaches. Aluru holds Fellowships from AAAS and IEEE and has received awards such as the NSF Career Award (1997), IBM Faculty Award (2002), and Swarnajayanti Fellowship (2007). He serves on editorial boards for journals like IEEE Transactions on Parallel and Distributed Systems and International Journal of Data Mining and Bioinformatics . His affiliations include the Institute for Data Engineering and Science (IDEaS) and Machine Learning@GT . Research trends in his articles span genomic data processing, parallel algorithms, and network inference, emphasizing scalability and computational efficiency. He leads efforts in error correction, genome assembly, and large-scale gene regulatory network construction.

Adel Daoudمشاهده پروفایل

دانشیار

• Global Poverty
• Political Economy
• Development Studies
• +۳ مورد دیگر

Adel Daoud is a Senior Associate Professor at Linköping University's Institute for Analytical Sociology (Department of Management and Engineering) and an Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences at Chalmers University of Technology. His research focuses on global poverty, political economy, and causal inference using machine learning. He leads the AI and Global Development Lab, combining AI and earth observation to study poverty dynamics in Africa. Education: PhD in Sociology (2011, University of Gothenburg), M.A. in Sociology (2006), and pedagogical training for higher education (2011-2012). Former positions include roles at Harvard University, the University of Cambridge, and the Alan Turing Institute. Research emphasizes causal mechanisms linking economic shocks, governance, and child poverty. Methodological innovations include integrating satellite imagery with machine learning for poverty measurement. Major grants include SEK 18 million for historical poverty analysis via satellite data. Awards include the Academy Research Fellowship (2018) and Young Academy of Sweden membership (2022-2027). His work spans 150+ publications in journals like PNAS, World Development, and Sociological Methods & Research. Teaching covers quantitative methods, political economy, and social theory. Labs: AI and Global Development Lab (focusing on satellite-based poverty analytics). Podcast: 'The Journeys of Scholars' interviews top academics on research strategies. Active in policy advocacy through the Young Academy of Sweden.

Joost-Pieter Katoenمشاهده پروفایل

استاد

• Formal Methods
• Probabilistic Programming
• Model Checking
• +۴ مورد دیگر

Joost-Pieter Katoen is a Professor at RWTH Aachen University's Department of Computer Science, part of the School of Computer Science. He leads the MOVES group and focuses on formal methods, probabilistic systems, and program verification. His research includes probabilistic programming, Markov models, Bayesian networks, and stochastic systems. He has co-authored over 200 publications in top venues like AAAI, CAV, and CONCUR, and developed tools like AMBER and SAFEST for probabilistic analysis. Notably, his student Kevin Batz received the ETAPS Dissertation Award in 2025 for work on deductive verification of probabilistic programs. He serves as an editor for Formal Methods and has advised PhD students such as Kevin Batz and Bahare Salmani Barzoki. His work bridges theoretical foundations with practical tools for analyzing complex systems.

• Bayesian Modelling
• Decision Support Systems
• Explainable AI (XAI)
• +۵ مورد دیگر

Dr. Evangelia Kyrimi is a Lecturer in AI and Data Science at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. Since 2023, she has been a Royal Academy of Engineering Research Fellow. Her PhD in Computer Science from QMUL focused on Bayesian Networks for Clinical Decision Making. Her research bridges Bayesian modeling, decision support under uncertainty in healthcare, and explainable AI (XAI). Key areas include causal graphical models, user-specific explanations for AI systems, and bridging the gap between clinical decision support systems (CDSS) and real-world implementation. Her current fellowship (ExAIDSS) aims to develop trustworthy XAI systems for healthcare through causal explanations. Dr. Kyrimi’s work emphasizes explanation fundamentals, dynamic user-centric outputs, and evaluation protocols for AI systems. She has published extensively on Bayesian networks in healthcare, trauma prediction, pregnancy outcomes, and governance frameworks for AI adoption in clinical settings.

Liam Solusمشاهده پروفایل

استادیار

• Combinatorics
• Algebraic and Geometric Combinatorics
• Discrete Geometry
• +۵ مورد دیگر

Liam Solus is an Assistant Professor of Mathematics at the Department of Mathematics, KTH Royal Institute of Technology in Stockholm, Sweden. He is affiliated with the ALGEBRA COMBINATORICS & TOPOLOGY division and the Digital Futures Faculty, focusing on research in combinatorics, discrete mathematics, algebraic statistics, and their applications in artificial intelligence and causal inference. His work is supported by grants from WASP (Wallenberg AI, Autonomous Systems, and Software Program), the Swedish Research Council, and the Göran Gustafsson Foundation. Education: PhD in Mathematics (University of Kentucky, 2015), postdoctoral fellowships at MIT, IST Austria, and the Max Planck Institute. Current research projects include causal AI for mobility behavior analysis (cAIMBER), combinatorial and geometric approaches to statistical models, and algebraic methods in machine learning. Research Interests: Algebraic and geometric combinatorics, lattice polytopes, causal inference, graphical models, algebraic statistics. Key contributions include studies on Ehrhart theory, Bayesian networks, and combinatorial sequences like Eulerian polynomials and Fibonacci numbers in causal structures. Awards: Large Prize for Young Researchers (Göran Gustafsson Foundation, 2023). Supervision includes PhD students Danai Deligeorgaki and former student Petter Restadh, along with postdocs in algebraic statistics and combinatorics. Labs/Teams: Co-PI of cAIMBER, member of Digital Futures, organizer of international workshops (e.g., AlCoVE 2024 in VR). Teaching includes courses on statistical learning, algebraic statistics, and probabilistic graphical models.

• Bayesian Modelling
• Clinical Decision Support Systems
• Explainable AI (XAI)
• +۳ مورد دیگر

Dr. Evangelia Kyrimi is a Lecturer in AI and Data Science at Queen Mary University of London's School of Electronic Engineering and Computer Science. She holds a Royal Academy of Engineering Research Fellowship (2023–2028) and specializes in Bayesian networks and explainable AI (XAI). Her PhD in Computer Science from QMUL focused on Bayesian Networks for Clinical Decision Making. She is also a medical statistician with expertise in causal graphical models and healthcare decision support systems. Education: PhD in Computer Science, Queen Mary University of London Research Interests: Bayesian modelling under uncertainty in healthcare, causal AI for clinical decision support, XAI frameworks for user trust, and bridging gaps between AI development and clinical adoption. Her work includes developing explanation algorithms incorporating causality, user-specific dynamic explanations, and evaluation protocols for XAI systems. Grants: Rosetrees Trust Interdisciplinary Award (2021) 5-year Royal Academy of Engineering Fellowship (2023–2028) Awards: Royal Academy of Engineering Research Fellowship Labs/Teams: Centre for Multimodal AI at Queen Mary University of London.