جست‌وجوی استاد و پژوهشگر

Ruonan Xuمشاهده پروفایل

استادیار

• Econometrics
• Applied Econometrics
• Finite Population Inference
• +۳ مورد دیگر

Ruonan Xu is an Assistant Professor in the Department of Economics at Rutgers University, specializing in Econometrics. She joined the department in Fall 2020. Her research focuses on finite population inference, spatial correlation, and causal inference methodologies. Education: Ph.D. in Economics, Michigan State University, 2020 B.A. in Mathematical Economics, Fudan University, 2015 Research Interests: Dr. Xu’s work emphasizes econometric methodologies for addressing complex data structures, including spatial correlation, clustered data, and interference effects. She has contributed to instrumental variable estimation with binary endogenous variables and developed design-based approaches for spatial analysis. Her recent focus includes robustness considerations in econometric models and multidimensional clustering techniques. Publications & Work in Progress: Her published work includes studies in The Econometrics Journal and Economics Letters . Current projects explore distributionally robust average treatment effects and difference-in-differences with interference mechanisms. A working paper on multidimensional clustering has been submitted to the Journal of Econometrics . Advising & Grants: No formal advisees or grants explicitly listed in the provided materials.

Olufemi A. Omitaomuمشاهده پروفایل

استاد مدعو

• Energy Infrastructure Siting and Analysis
• Artificial Intelligence in Critical Infrastructure Systems
• Anomaly Detection in Complex Systems
• +۴ مورد دیگر

Olufemi A. Omitaomu is an Adjunct Professor at the Department of Industrial and Systems Engineering within the Tickle College of Engineering at the University of Tennessee, Knoxville. He serves as a Group Leader and Distinguished R&D Staff at Oak Ridge National Laboratory (ORNL), leading the Computational Urban Sciences Group in the Computational Sciences and Engineering Division. Ph.D., Industrial Engineering (Information Engineering concentration), University of Tennessee, Knoxville M.S., Mechanical Engineering, University of Lagos, Nigeria B.S., Mechanical Engineering, Lagos State University, Nigeria Dr. Omitaomu’s research focuses on artificial intelligence in energy systems , cognitive coupling of human-machine systems , anomaly detection in complex systems , energy infrastructure siting and analysis , and disaster risk analysis with urban systems resilience . His work integrates computational models, optimization techniques, and geospatial frameworks to address challenges in critical infrastructure systems. The 15 most recent publications highlight trends in renewable energy integration , climate adaptation strategies , and emergency resource allocation . Key methodologies include agent-based modeling , multicriteria decision analysis , and wavelet shrinkage , applied to domains like energy systems , disaster management , and urban sustainability . Scientific recognition includes: Distinguished R&D Staff, Oak Ridge National Laboratory Senior Member, Institute of Industrial and Systems Engineers (IISE) Senior Member, Institute of Electrical and Electronics Engineers (IEEE) He actively mentors MS and PhD students with expertise in Python programming , game theory , and human-machine systems . His research is supported by collaborations with ORNL and interdisciplinary grants.

Sharan Vaswaniمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Stochastic Optimization
• +۳ مورد دیگر

Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their application to modern machine learning, particularly reinforcement learning and generalization analysis. Education & Career: PhD in Computer Science (2015-2018), University of British Columbia (UBC), supervised by Laks Lakshmanan and Mark Schmidt. Postdoc (2019-2021) at Mila - Quebec AI Institute with Simon Lacoste-Julien, and University of Alberta with Csaba Szepesvári. MSc in Computer Science (2015), UBC, focusing on influence maximization in social networks. BS from Birla Institute of Technology and Science, Pilani (2012), followed by research engineering at Siemens Corporate Research. Research Interests: Algorithmic development for decision-making in uncertain environments (bandits, reinforcement learning). Stochastic optimization methods with provable guarantees. Generalization and theoretical foundations of machine learning models. Recent Trends in Publications: Focus on optimization algorithms (e.g., stochastic gradient methods, line search, momentum techniques) with theoretical analysis. Contributions to reinforcement learning, including policy gradient methods and constrained MDPs. Exploration of adaptive algorithms for continual learning and over-parameterized models. Awards: Best Paper Honorable Mention (AISTATS 2022). Best Paper Award (2nd IEEE International Conference on Parallel Distributed and Grid Computing 2012). Research Group: Leads a team at SFU focused on machine learning optimization and decision-making systems. Active in organizing workshops at NeurIPS and ICML on optimization and reinforcement learning theory.

Hubert Saleurمشاهده پروفایل

استاد

• Quantum Field Theory
• Statistical Mechanics
• Integrability
• +۱۲ مورد دیگر

Hubert Saleur is a Professor of Physics and Astronomy at the University of Southern California and holds a Director of Research position at the IPhT CEA Saclay in France. His work bridges hard condensed matter physics and high-energy physics , with interdisciplinary focus on low-dimensional quantum field theories and statistical mechanics . He has led DOE-funded projects on quantum quench dynamics and non-equilibrium transport in nanostructures, and his research involves advanced mathematical techniques including non-semisimple representation theory . Education: Ph.D. in Physics, University of Paris (1987) Research interests span non-perturbative effects , transport out of equilibrium , topological defects , and AdS/CFT correspondence . Recent work includes geometrical correlation functions in Potts models and quantum simulation of conformal field theories on analog quantum computers. His scientific awards include: Jean Ricard Prize, French Physical Society (2018-2019) ERC Advanced Grant (2015-2016) Silver Medal, CNRS (2011-2012) Humboldt Senior Scientist Award (2001-2008) Packard Foundation Fellowship (1991-2001) Doisteau-Blutel Prize, French Academy of Sciences (1987) As an advisor, Saleur has mentored 15+ students/postdocs now in permanent research or academic roles at institutions like CNRS Paris , Imperial College , and Quantinuum Munich . He co-organizes international conferences such as Quantum Theory and Symmetry XI and serves on editorial boards for Physics Open , SIGMA , and SciPost . Recent courses include Introduction to Topological Phases and Criticality and the Renormalization Group .

• Econometrics
• Panel Data Econometrics
• Structural Breaks
• +۳ مورد دیگر

Joakim Westerlund is a Professor in the Department of Economics at Lund University's School of Economics and Management. With over 134 research outputs and 33 academic activities, his work focuses on econometrics, particularly panel data analysis, structural breaks, and estimation theory. He has contributed to the development of econometric methods for the New Keynesian Phillips Curve and common correlated effects models. Active Wallenberg Academy Fellowship (2019-2028) Supervised 11 doctoral theses and bachelor/master projects Peer-review panel member and journal editor His research aligns with UN Sustainable Development Goals in Economics and Econometrics, with significant contributions to panel unit root testing, interactive effects models, and Stata-based econometric methods. Westerlund received the prestigious Journal of Applied Econometrics Distinguished Author award in 2018. Current PhD supervisees include Christina Maschmann (2023-2028), Tilman Bretschneider (2023-2028), Pelle Almgren (2022-2027), and Shayan Meskinimood (2021-2026).

Chuanhai Liuمشاهده پروفایل

استاد

• Statistical Inference
• Computational Methods
• Bayesian Analysis
• +۴ مورد دیگر

Chuanhai Liu is an Associate Head and Professor of Statistics at Purdue University’s Department of Statistics. He specializes in computational methods for statistical inference, Bayesian analysis, and big data computing systems. His research emphasizes robust algorithms, inferential models, and probabilistic methods. Education: M.S., Wuhan University, Probability and Statistics (1987) M.A., Harvard University, Statistics (1990) Ph.D., Harvard University, Statistics (1994) Research Interests: Computational methods (EM algorithms, MCMC), statistical software systems for big data, theoretical foundations of inference, and prior-free probabilistic modeling. His work bridges computational efficiency and statistical validity, with applications in network analysis, environmental data, and machine learning. Awards: Fellow of the American Statistical Association (2007) Elected Member of the International Statistical Institute (2006) Frank Wilcoxon Prize (2000) Advising & Grants: Supervised 9 Ph.D. students, including interdisciplinary collaborations. His grants focus on distributed statistical computing and inferential frameworks. Active in software development for large-scale data analysis. Labs/Teams: Leads statistical computing initiatives at Purdue, contributing to scalable algorithms and probabilistic inference systems.

Yen-Chi Chenمشاهده پروفایل

دانشیار

• Nonparametric statistics
• Topological data analysis
• Missing data
• +۷ مورد دیگر

Yen-Chi Chen is an Associate Professor in the Department of Statistics at the University of Washington. He also holds positions as a Data Science Fellow at the UW eScience Institute and as a co-investigator and statistician at the National Alzheimer's Coordinating Center. His academic career spans multiple interdisciplinary fields including statistics, data science, and astrostatistics. Chen's educational background includes a Ph.D. from Carnegie Mellon University, where he received prestigious awards including the Umesh K. Gavasakar Thesis Award (2017) and the William S. Dietrich II Presidential Ph.D. Fellowship Award (2015). His research focuses on nonparametric statistics, topological data analysis, missing data methodologies, cluster analysis, manifold learning, and applications in large-scale structure analysis and astrostatistics. Chen has made significant contributions to the development of statistical methods for analyzing cosmic web structures, GPS data, and causal inference with continuous treatments. His work bridges theoretical statistics with practical applications in astronomy, neuroscience, and public health. Analysis of his recent publications reveals a strong emphasis on developing novel statistical frameworks for complex data structures, particularly focusing on density-based methods, manifold learning, and approaches that address challenges in missing data and causal inference without standard assumptions. ASA Noether Early Career Scholar Award, American Statistical Association (2022) CAREER Award, National Science Foundation (2022-2027) Umesh K. Gavasakar Thesis Award, Carnegie Mellon University (2017) William S. Dietrich II Presidential Ph.D. Fellowship Award, Carnegie Mellon University (2015) Chen has advised numerous graduate students across multiple publications, with a focus on developing new statistical methodologies. His research has been supported by major funding agencies including the National Science Foundation and the National Institutes of Health. He is actively involved in several research groups including the UW Geometric Data Analysis Group, the UW Center for Statistics and the Social Sciences, and the National Alzheimer's Coordinating Center.

• Causal Analysis
• Econometrics
• Machine Learning
• +۷ مورد دیگر

Martin Huber is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg, Switzerland, within the Faculty of Management, Economics and Social Sciences, Department of Economics. He leads the Chair of Applied Econometrics and maintains an active research profile with numerous publications in top economics and statistics journals. His work bridges theoretical econometrics with practical policy applications across multiple domains including labor, health, and education economics. Professor Huber earned his Ph.D. in Economics and Finance in 2010 and served as Assistant Professor at the University of St. Gallen until 2014. He has conducted research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019), establishing an international research network. His academic affiliations include the Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), and Centre for European Economic Research (ZEW) Mannheim. Huber's research focuses on data-based causal analysis , machine learning applications in economics , and policy evaluation methods . He specializes in developing and applying statistical and econometric methods for measuring causal effects, with particular emphasis on semi- and nonparametric microeconometrics. His work spans labor economics (gender occupational segregation, maternal labor supply), health economics, education policy, and competition policy (bid-rigging cartels detection). His recent publications (2023-2025) demonstrate a clear trajectory toward integrating machine learning techniques with traditional econometric methods for causal inference. This includes developing frameworks for causal discovery, improving difference-in-differences methods with machine learning, and creating novel approaches for detecting collusion in markets. His 2023 book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R" (MIT Press) has become a key reference in the field. As an active researcher, Professor Huber directs several research projects including experimental evaluations of gender occupational segregation in the Swiss apprenticeship market. His work combines theoretical rigor with practical policy relevance, often employing experimental and quasi-experimental methods to address questions of causal mechanisms in social and economic phenomena. Through his Chair of Applied Econometrics, Huber supervises Ph.D. students and maintains an active research group focused on advancing causal inference methodologies. His work has significant implications for evidence-based policymaking across multiple sectors, particularly in evaluating the effectiveness of social programs and economic policies.

Oskari Ville Pakariمشاهده پروفایل

مدرس

• Nuclear Engineering
• Reactor Physics
• Radiation Detection
• +۳ مورد دیگر

Oskari Ville Pakari is a Lecturer at the School of Basic Sciences, École polytechnique fédérale de Lausanne (EPFL), affiliated with both the Institute of Physics (IPHYS) and the Swiss Plasma Center (SPH-ENS). He contributes to teaching and research, particularly in reactor physics and radiation detection. His research focuses on nuclear reactor diagnostics , gamma noise analysis , and neutron spectroscopy . He actively develops mixed reality visualization tools for radiation detection data and participates in the European CORTEX project for reactor monitoring. Selected publications highlight his work in gamma-ray imaging , neutron noise simulations , and detector system validation using advanced statistical methods like bootstrapping and Welch's technique. Teaching activities include courses on Radiation biology, protection, and applications Radiation and reactor experiments He advises PhD student Saliba Michel and collaborates with international institutions such as CEA, KIT, and LRS (Laboratory of Reactor Physics and Systems Behaviour) at EPFL.

• Quantum Field Theory
• Quantum Gravity
• String Theory
• +۵ مورد دیگر

Hirosi Ooguri is the Fred Kavli Professor of Theoretical Physics and Mathematics at Caltech and Director of the Walter Burke Institute for Theoretical Physics. He holds dual roles as University Professor at the University of Tokyo and former Director of the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU). His research focuses on quantum field theory, quantum gravity, and string theory with emphasis on interplay between physics and mathematics. Ooguri has pioneered work on topological string theory, black hole microstates, and swampland conjectures. Education: B.A. & M.A. from Kyoto University (1984-1986), Ph.D. in Physics from University of Tokyo (1989) Affiliations: Caltech faculty since 2000, Kavli IPMU Director (2018-2023) His research explores mathematical structures in quantum gravity and string theory, particularly in areas like AdS/CFT correspondence, conformal field theory, and holography. Notable contributions include foundational work on topological string invariants, black hole entropy calculations, and constraints on quantum gravity via swampland conjectures. Awards: Medal of Honor with Purple Ribbon (Japan), Guggenheim Fellowship, Hamburg Prize, Eisenbud Prize for Mathematics and Physics, and Kodansha Prize for Science Books. Ooguri has supervised over 30 Ph.D. students and advised impactful science outreach projects. He led establishment of the Walter Burke Institute for Theoretical Physics (2014) and served on global scientific leadership roles including Aspen Center for Physics President (2016-2019).

Ciarán Mac an Bhairdمشاهده پروفایل

دانشیار

• Resourcing Small Firms
• Enterprise Policy
• Entrepreneurial Finance
• +۷ مورد دیگر

Ciarán Mac an Bhaird is an Associate Professor of Business and Management at Dublin City University (DCU), within the Fiontar & Scoil na Gaeilge department. He holds a PhD from Trinity College Dublin, a B.Comm and LL.B from NUI Galway, and an M.Sc. in Finance from DCU. His academic role includes teaching and supervising PhD students focusing on enterprise policy, high-growth firm financing, and entrepreneurship education. Research interests span resourcing small firms, capital structure, alternative finance, and public policy for SMEs. He authored Resourcing Small and Medium Sized Enterprises (Springer-Verlag, 2010), launched by the Irish Minister for Enterprise. His work has been published in journals like Small Business Economics and Venture Capital . He chairs the Entrepreneurial Finance track at the Institute of Small Business and Entrepreneurship Conference and serves as an associate editor for Research in International Business and Finance . Current PhD advisees include Simon Bridge, Nouf Alnasser, Batoul Batoulbanjar, and Sean O Reilly. Professional activities include contributions to policy reports for Enterprise Ireland and the Irish League of Credit Unions, and participation in the U.S. State Department’s International Visitor Leadership Programme.

Ryan Giordanoمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Variational Methods
• +۴ مورد دیگر

Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.

• Programming Language Design
• Program Verification
• Type Theory
• +۳ مورد دیگر

Neelakantan R. Krishnaswami is a Professor of Computer Science at the University of Cambridge's Computer Laboratory , and a Fellow of Trinity College . His research focuses on the intersection of program verification, programming language design, and foundational topics like type theory and semantics. His work spans areas such as refinement types, parser design, separation logic for systems software, and the semantics of reactive programming. Notable contributions include the Datafun language for higher-order Datalog and the λert type theory for explicit refinement types. He has also developed foundational frameworks for verifying imperative programs using advanced type systems and logical relations. Key publications include 'Explicit Refinement Types' (ICFP 2023), 'flap: A Deterministic Parser with Fused Lexing' (PLDI 2023), and 'CN: Verifying Systems C Code' (POPL 2023). His work frequently addresses challenges in efficiency, correctness, and modularity for both functional and imperative systems. His awards include Distinguished Paper Awards at PLDI 2019 and POPL 2020. His research integrates theoretical rigor with practical tooling, exemplified by contributions to languages like Coq, Lean, and Haskell.

Daniel Wilhelmمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Nonparametric Methods
• +۴ مورد دیگر

Daniel Wilhelm is a Professor of Statistics and Econometrics at LMU Munich, with a courtesy appointment in the Department of Economics. His research focuses on econometric theory, nonparametric methods, measurement error modeling, and statistical inference. He leads the Statistics and Econometrics Group at LMU and holds affiliations with the Centre for Microdata Methods and Practice (CeMMAP), Institute for Fiscal Studies (IFS), and the Centre for Research and Analysis of Migration (CReAM). Wilhelm’s work includes groundbreaking contributions to NPIV estimation, robust statistical testing, and the development of R and Stata packages for rank inference and econometric analysis. His recent publications address topics like rank-based inference, measurement error detection, and high-dimensional independence testing. He organizes academic events such as the Munich Econometrics Seminar and the LMU-Todai Econometrics Workshop. His research emphasizes methodological rigor and practical applications, with a focus on improving statistical techniques for social science and policy analysis.

Min Seong Kimمشاهده پروفایل

دانشیار

• Econometrics
• Panel Data Analysis
• Bootstrap Methods
• +۳ مورد دیگر

Min Seong Kim is an Associate Professor in the Department of Economics at the University of Connecticut, affiliated with the College of Liberal Arts and Sciences. His research focuses on econometrics, particularly panel data analysis, bootstrap methods, and cross-sectional dependence. He earned his Ph.D. in Economics from UC San Diego in 2011. His contact information includes email: min_seong.kim@uconn.edu , and office location Oak Hall 330. Education: Ph.D., Economics, UC San Diego, 2011 Research Interests: Econometric theory and applications Bootstrap methods and robust inference Panel data models with cross-sectional dependence Time series analysis and spatial econometrics Publications highlight his contributions to econometric methodology, including robust inference techniques for panel data models, bootstrap methods, and policy analysis. Recent work addresses cross-sectional dependence in large panel models and diffusion index forecasts. No scientific awards are explicitly listed. Advising and grant details are not provided in the text. His research is supported through standard academic channels, and he maintains a professional website at http://minseongkim.weebly.com .