جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Probability
• Mathematical Physics
• +۶ مورد دیگر

Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .

Anoosheh Heidarzadehمشاهده پروفایل

استادیار

• Information and coding theory
• Private and secure computing
• Fault-tolerant distributed computing
• +۳ مورد دیگر

Anoosheh Heidarzadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at Santa Clara University's School of Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Carleton University (2012) and previously served as a Visiting Assistant Professor at Texas A&M University (2018-2022) and Associate Research Scientist at the same institution (2015-2017). His postdoctoral research was conducted at the California Institute of Technology (2013-2014). His research focuses on: Information and coding theory : Fundamental limits of data transmission and storage systems Private and secure computing : Protocols for confidential data processing in networked environments Fault-tolerant distributed systems : Resilient computation frameworks for large-scale applications Distributed machine learning : Scalable algorithms for collaborative learning architectures Recent publications (2021-2022) demonstrate strong emphasis on privacy-preserving computation (covering 73% of articles) and distributed coding techniques (67% of articles), with innovations in private information retrieval, matrix operations, and group testing methodologies. Theoretical contributions dominate (87%), while 13% address applied challenges like COVID-19 screening.

Anush Tserunyanمشاهده پروفایل

استاد

• Mathematics
• Ergodic Theory
• Combinatorics
• +۳ مورد دیگر

Anush Tserunyan is a Professor of Mathematics at McGill University, Canada, and a Visiting Professor at the Unit of Pure and Applied Mathematics (UMPA) at École normale supérieure de Lyon from December 1, 2024, to January 31, 2025. She holds a bachelor's and master's in computer science and applied mathematics from Yerevan State University (2005–2007) and a Ph.D. in Mathematics from UCLA (2013), focusing on finite generators for group actions, equivalence relations, and recursive program complexity. Research Interests: Anush Tserunyan specializes in Ergodic Theory , Combinatorics , and Group Actions , with notable contributions to graph theory, hypergraphs, and Ramsey theory. Her work bridges combinatorial structures with analytic methods, particularly in dynamical systems and descriptive set theory. Collaborations: During her visit to UMPA, she collaborates with teams in Geometry, Groups and Dynamics , and Number Theory , alongside researchers like Benjamin Schraen and Sophie Morel. Her stay includes seminars on GGD, Number Theory, and the 'Actions!' working group. Publications: Her research spans topics like ergodic theorems, disjoint matchings in graphs, and algebraic hypergraphs, reflecting a focus on foundational mathematical structures and their applications.

Benjamin Brooksمشاهده پروفایل

استاد

• Microeconomic Theory
• Mechanism Design
• Auction Theory
• +۳ مورد دیگر

Benjamin Brooks is a Professor in the Department of Economics and the College at the University of Chicago, serving as Director of Graduate Studies. He holds a Ph.D. from Princeton University (2014), an M.A. from Princeton (2010), and a B.A. in Mathematics and Quantitative Economics from Tufts University (2008). His research focuses on microeconomic theory, mechanism design, auction theory, repeated games, and computational game theory. Brooks has published extensively in top journals such as the American Economic Review, Econometrica, and the Journal of Political Economy. His work often explores informationally robust mechanisms and strategic behavior in auctions and stochastic games. Brooks has received grants from the National Science Foundation for projects on mechanism design and stochastic game algorithms. He is an Associate Editor of the American Economic Review: Insights, Theoretical Economics, and Economic Theory. His teaching spans game theory, price theory, and advanced topics in economic theory at both undergraduate and graduate levels. Brooks has also contributed to software tools like BCESolve and SGSolve for analyzing Bayesian correlated equilibria and stochastic games. Research Highlights: Contributions to auction design under incomplete information, robust mechanism design, and algorithmic solutions for stochastic games. Awards: Excellence in Refereeing Award (2017), NSF grants (2015-2025), and Fellow of the Society for the Advancement of Economic Theory (2024).

Theo Damoulasمشاهده پروفایل

استاد

• Probabilistic Machine Learning
• Bayesian Statistics
• Digital Twins
• +۵ مورد دیگر

Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.

Heather Batteyمشاهده پروفایل

استاد

• Statistical Theory
• High-Dimensional Inference
• Sparsity in Statistics
• +۳ مورد دیگر

Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.

• Reliability, risk and life-cycle analysis
• Probabilistic mechanics
• Sustainable and resilient infrastructure
• +۶ مورد دیگر

Paolo Gardoni is the Alfredo H. Ang Family Professor and an Excellence Faculty Scholar in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, with additional professorial appointments in Industrial & Enterprise Systems Engineering and Biomedical & Translational Sciences. He also serves as Director of the MAE Center and Editor-in-Chief of Reliability Engineering & System Safety. Education Ph.D. in Civil Engineering, University of California, Berkeley (2002) M.A. in Statistics, University of California, Berkeley (2001) M.Eng. in Structural Engineering, University of Tokyo (1997) Laurea (BS+MS equivalent) in Structural Engineering, Politecnico di Milano (1997) Research Interests Gardoni’s scholarship integrates probabilistic methods with large-scale infrastructure systems to advance reliability, risk, and life-cycle analysis. His work quantifies the performance of deteriorating systems under natural and anthropogenic hazards, models societal impacts of disasters, and develops decision frameworks for sustainable and resilient infrastructure. He also examines ethical, social, and legal dimensions of risk, and investigates optimal strategies for hazard mitigation, disaster recovery, and climate adaptation. Across more than 250 refereed journal papers, he has advanced sub-fields ranging from probabilistic mechanics and earthquake engineering to catastrophe bond pricing and engineering ethics, leveraging tools such as stochastic differential equations, Bayesian networks, and physics-informed machine learning. Awards & Honors Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure (ASCE, 2021) Best Paper Awards in ASCE Journal of Sustainable Water in the Built Environment (2019) and Geotechnical Research (2018 Telford Premium Prize) Fellowships and named professorships: Alfredo H. Ang Family Professor, Excellence Faculty Scholar, and courtesy or honorary professorships at Tsinghua, IIT Guwahati, Tongji, Jianghan, and Loughborough universities. Research Leadership & Funding Gardoni has secured over $58 million in research funding from NSF, DHS, NIST, USAID, Qatar National Research Fund, and other agencies. He directs the MAE Center—formerly an NSF Engineering Research Center—focused on multi-hazard engineering approaches, and is Editor-in-Chief of Reliability Engineering & System Safety (Elsevier, IF 9.4). He founded and formerly led the journal Sustainable and Resilient Infrastructure (Taylor & Francis) and serves on editorial boards of nine additional journals. Advising & Mentorship He has graduated 27 PhD and 35 Master’s students, many of whom now hold faculty positions worldwide. His group maintains an active pipeline of doctoral and post-doctoral researchers working on resilience analytics, infrastructure monitoring, and risk-informed decision-making. Laboratories & Collaborations He leads the MAE Center and is affiliated with the Critical Infrastructure Resilience Institute (CIRI) and the Biomedical and Translational Sciences group. International collaborations span the UK (Loughborough), India (IIT Guwahati), and China (Tsinghua, Tongji, Jianghan), fostering cross-disciplinary research in reliability and resilience engineering.

Katerina Marcoulidesمشاهده پروفایل

دانشیار

• Data Mining
• Structural Equation Modeling
• Longitudinal Data Analysis
• +۳ مورد دیگر

Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.

Kevin Coulembierمشاهده پروفایل

استاد

• Representation Theory
• Noncommutative Algebra
• Monoidal Categories
• +۳ مورد دیگر

Kevin Coulembier is a Professor in the Algebra Research Group at the University of Sydney . He has held a continuing position since 2017 and received prestigious awards including the Christopher Heyde Medal (2021), G. de B. Robinson Award (2022), and the Frontiers of Science Award (2024). Research Focus: Representation theory of algebraic groups, Lie (super)algebras, quantum groups, and monoidal categories; tensor ideals; and homological algebra. Grants: ARC Future Fellowship (2023), ARC Discovery Projects (2021, 2018), and DECRA Fellowship (2017). Students: Supervised postdocs and PhD students including Nick Bridger, Joseph Newton, Bregje Pauwels, Alexander Sherman, and Willow Stewart. Editorial Roles: Editor for Annals of Representation Theory and Mathematische Zeitschrift .

• Natural Language Processing
• Natural Language Generation
• Machine Learning
• +۴ مورد دیگر

Prof. Anya Belz is Full Professor of Computer Science at Dublin City University's School of Computing and Science Lead at ADAPT Research Centre. A leading NLP researcher with PhD-level expertise, she specializes in natural language generation, evaluation methodologies, and multimodal systems. Recipient of multiple best paper awards and NAACL Test of Time Award nomination. Research innovations include foundational work on statistical language generation (deployed in weather forecasting systems), comparative evaluation frameworks, vision-language integration, and reproducibility quantification. Current EPSRC-funded ReproHum project coordinates 20 global labs studying evaluation consistency. Achievements : Developed industry-deployed generation systems for accessibility applications Pioneered cross-modal alignment techniques for image description Authored 100+ publications spanning generation, evaluation, and reproducibility

Martin Haenggiمشاهده پروفایل

استاد

• Wireless Networks
• Stochastic Geometry
• Communications Theory
• +۷ مورد دیگر

Prof. Martin Haenggi is the Frank M. Freimann Professor of Electrical Engineering and Concurrent Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a Dr.sc.techn. (Ph.D.) from ETH Zurich and has been at Notre Dame since 2000. His research focuses on stochastic geometry and wireless networks, including cellular, heterogeneous, vehicular, and millimeter-wave systems. He has held sabbaticals at UCSD (2007–2008), EPFL (2014–2015), and ETH Zurich (2021–2022). Education: Dipl.-Ing. (M.Sc.), ETH Zurich, 1995 Dr.sc.techn. (Ph.D.), ETH Zurich, 1999 Research interests emphasize stochastic geometry for analyzing network performance, including coverage, interference, and reliability in wireless systems. Key areas include meta distributions, spatial-temporal analysis, and network optimization. His work has been recognized with IEEE Fellow status, Clarivate Highly Cited Researcher distinction, and NSF CAREER Award (2005). Grants and Awards: NSF Award (Deep Stochastic Geometry: 2020–2023) NSF Award (Toward a Stochastic Geometry for Cellular Systems: 2015–2019) Rice Prize (2017), Best Survey Paper Award (2017), and Best Tutorial Paper Award (2010) from IEEE Communications Society Teaching includes advanced courses on stochastic geometry, wireless networks, and signal processing. His lab focuses on theoretical and applied aspects of network modeling, with collaborations in industry and academia.

Hannes Zacherمشاهده پروفایل

استاد

• Work and Organizational Psychology
• Occupational Health and Well-being
• Proactive Employee Behavior
• +۳ مورد دیگر

Hannes Zacher is a Professor of Work and Organizational Psychology at Leipzig University's Wilhelm Wundt Institute of Psychology. He previously held academic positions at Queensland University of Technology (Australia) and the University of Groningen (Netherlands). His research focuses on occupational health, proactive employee behavior, aging in the workplace, and sustainable organizational practices. He has been awarded grants from institutions like the German Science Foundation (DFG) and the Volkswagen Foundation. Zacher earned his Ph.D. in Psychology from Justus-Liebig-University Gießen in 2009, following a Diploma (equivalent to B.Sc./M.Sc.) in Psychology from the Technical University of Braunschweig (2000–2006). His research interests include occupational health and well-being, proactive and adaptive employee behavior, aging at work and career development, and environmentally sustainable behavior in organizations. He employs methodologies such as longitudinal surveys, experience sampling, and experiments. Key themes in his work address the interplay between work environments and employee well-being, with a focus on fostering sustainable practices and mitigating age-related challenges in the workplace. Zacher leads projects such as the Centre for Digital Work and studies like 'The Role of Work in the Development of Civilization Diseases.' His publications span topics from green workplace behavior to leadership dynamics during crises. He has no listed scientific awards but has secured significant grant funding for his research. Advising and grant details reflect his commitment to impactful research, though specific student names are not provided. His lab and team efforts emphasize translating psychological insights into organizational practices for sustainability and employee well-being.

Gabriele Farinaمشاهده پروفایل

استادیار

• Game Theory
• Machine Learning
• Optimization
• +۷ مورد دیگر

Gabriele Farina is an Assistant Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Laboratory for Information and Decision Systems (LIDS), with additional affiliations at the Operations Research Center (ORC). Holding the X-Window Consortium Career Development Chair, his research focuses on theoretical and algorithmic foundations for learning and computational decision-making under imperfect information, integrating game theory, machine learning, optimization, and statistics. He previously served as a Research Scientist at Meta's Fundamental AI Research (FAIR) group, where he contributed to Cicero, a human-level AI agent combining strategic reasoning and natural language. Ph.D. in Computer Science from Carnegie Mellon University (advisor: Tuomas Sandholm) Facebook Fellowship (2019-2020) in Economics and Computation Recipient of multiple awards including ACM SIGecom dissertation award, NSF CAREER, and AI2050 Early Career Fellow His research spans four key areas: (1) No-Regret Learning Dynamics in extensive-form games; (2) Correlation and Mediated Equilibria in sequential decision-making; (3) Team Games and Team Max-Min Equilibria; and (4) Human Modeling and Equilibrium Perfection. His work addresses challenges in scalable equilibrium computation, stability of learning algorithms, and robustness to mistakes in multi-agent systems. Recent publications highlight advancements in polynomial-time equilibrium computation, cautious optimism algorithms, and connections between regret minimization and mirror descent. These contributions appear in top venues like COLT, NeurIPS, ICML, and AAAI, with keywords spanning game theory, optimization, and machine learning. NSF CAREER award AI2050 Early Career Fellow Facebook Fellowship ACM SIGecom dissertation award GameSec 2024 best paper award ICLR 2023 outstanding paper honorable mention His research group at MIT collaborates on projects involving strategic reasoning, human-level AI agents, and equilibrium refinements, with applications to games like Diplomacy and poker. Current efforts include developing faster algorithms for correlated equilibria and exploring connections between machine learning and economic theory.

Daniel J. Bauerمشاهده پروفایل

استاد

• Quantitative Psychology
• Latent Variable Models
• Developmental Psychopathology
• +۶ مورد دیگر

Dr. Daniel J. Bauer is a Professor and Director of the Quantitative Psychology Program and L.L. Thurstone Psychometric Laboratory at the University of North Carolina at Chapel Hill. His research focuses on advancing quantitative modeling techniques for studying negative social behaviors, health outcomes, and psychopathology, with expertise in generalized and nonlinear latent variable models, including multilevel models, structural equation models, and mixture models. His work emphasizes methodological innovations such as Bayesian regularization, measurement invariance evaluation, and the integration of deep learning with psychometrics. Bauer advises doctoral students in quantitative psychology and collaborates with developmental psychology programs. He leads the Thurstone Laboratory, one of the oldest quantitative psychology training programs in the U.S., emphasizing rigorous methodological training and applications in behavioral sciences. Bauer’s research trends span computational advancements in latent variable analysis, regularization for bias detection, and dynamic modeling of developmental processes. His advising includes over a dozen doctoral students now in academia and industry roles. He contributes to labs and initiatives like the Center for Developmental Science, advancing interdisciplinary research in psychological measurement and intervention evaluation.

Liang Zhaoمشاهده پروفایل

استاد

• Pharmacometrics
• Drug Delivery Modeling
• Artificial Intelligence in Drug Development
• +۵ مورد دیگر

Liang Zhao, PhD, MAS, MBA, is a Professor in the Department of Bioengineering and Therapeutic Sciences within the Schools of Pharmacy and Medicine at the University of California, San Francisco (UCSF). Prior to joining UCSF, he served as director of the Division of Quantitative Methods and Modeling (DQMM) in the Office of Research and Standards in the Office of Generic Drugs in the Center for Drug Evaluation and Research (CDER) at the U.S. Food and Drug Administration (FDA) from 2015 to 2024. His professional career spans over 19 years with experience at Pharsight, Bristol Myers Squibb (BMS), MedImmune, and the FDA. Dr. Zhao's research focuses on pharmacometrics, drug delivery modeling, and artificial intelligence-based tools that impact drug development and regulatory decision-making. His work encompasses mechanistic models for brain drug delivery, regulatory science modeling and simulation, AI-driven drug discovery and development, drug interactions, biological availability, generic drugs, clinical pharmacology, therapeutic equivalency, computer simulation, and FDA regulatory processes. He has pioneered innovative approaches including model master files for model sharing and model-integrated evidence for generic product development and approval. His research integrates machine learning tools into pharmacometrics to advance drug delivery and bioequivalence assessment methodologies. Dr. Zhao has published over 120 articles and book chapters in prestigious journals. His recent publications demonstrate strong focus on applying advanced modeling techniques, machine learning algorithms, and pharmacometric approaches to solve complex problems in drug development and regulatory science. His work shows consistent innovation in developing quantitative methods to enhance bioequivalence assessment, improve drug product characterization, and support regulatory decision-making for generic drugs. FDA Group Recognition Award, FDA, 2024 Gary Neil Prize for Innovation in Drug Development, American Society for Clinical Pharmacology & Therapeutics (ASCPT), 2023 Commissioner's Special Citation, FDA, 2021 Humanitarian Award, Victims' Rights Foundation, 2020 30+ FDA CDER team and Individual Awards, CDER, FDA, 2011 Academic Award for Executive MBA Class 2009, Judge Business School, University of Cambridge, 2011 Dr. Zhao leads the Zhao Lab at UCSF, which advances drug development and regulatory science through cutting-edge research in pharmacometrics, drug delivery modeling, and artificial intelligence. His work bridges academic research with regulatory applications, demonstrating leadership in translating scientific innovations into practical regulatory frameworks. His experience across industry, regulatory agencies, and academia provides a unique perspective on drug development challenges and opportunities.