جست‌وجوی استاد و پژوهشگر

Thomas Severiniمشاهده پروفایل

استاد

• Likelihood-Based Methods
• Higher-Order Asymptotic Approximations
• Statistical Methodology in Finance
• +۳ مورد دیگر

Thomas Severini is a Professor of Statistics and Data Science at Northwestern University's Weinberg College of Arts & Sciences. He holds a Ph.D. from the University of Chicago (1987). His research focuses on likelihood-based statistical methods, including higher-order asymptotic approximations and applications in finance, econometrics, and sports analytics. He has authored influential works such as Introduction to Statistical Methods for Financial Models (2017) and Analytic Methods in Sports (2014). Key research interests include: (1) Development of statistical methodology for complex models, (2) Application of likelihood-based techniques in finance/econometrics, (3) Analysis of sports performance data. Notable contributions include work on integrated likelihood functions and jet lag effects on MLB performance. Publications span top journals like Biometrika , Journal of Econometrics , and Proceedings of the National Academy of Sciences . His work bridges theoretical statistics with practical applications in diverse domains.

Xiaohui Chenمشاهده پروفایل

دانشیار

• High-dimensional statistics
• Machine learning
• Optimal transport
• +۴ مورد دیگر

Xiaohui Chen is an Associate Professor in the Department of Mathematics at the University of Southern California (USC), affiliated with the Master's Program in Mathematical Data Science. Previously, he held positions at the University of Illinois at Urbana-Champaign (UIUC), including Associate Professor (2019–present) and Assistant Professor (2013–2019), with a visiting role at MIT's IDSS in 2019–2020. His research focuses on high-dimensional statistics, machine learning, optimal transport, and quantum computing, with applications in signal processing and data science. Education: PhD in Electrical and Computer Engineering from the University of British Columbia (2013). Awards include the NSF CAREER Award (2018), Simons Fellowship in Mathematics (2020–2021), and ICSA Outstanding Young Researcher Award (2019). His work bridges theoretical foundations with practical algorithms, such as diffusion K-means clustering and Wasserstein proximal optimization methods. Key contributions include statistically optimal K-means clustering via semidefinite programming, analysis of density evolution from snapshot data, and quantum random state generation using holographic thermalization. He leads the Machine Intelligence Research Lab (MIRL) at USC, exploring machine learning and AI's mathematical underpinnings.

Yun Yangمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Inference
• High-Dimensional Statistics
• +۴ مورد دیگر

Yun Yang is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park . Previously, he held positions as Associate Professor (2022–2022) and Assistant Professor (2018–2022) at the University of Illinois at Urbana-Champaign, and Assistant Professor at Florida State University (2016–2018). He earned a B.S. in Mathematics from Tsinghua University (2011) and a Ph.D. in Statistics from Duke University (2014). His research focuses on Bayesian inference , high-dimensional statistics , machine learning , and optimal transport . Recent work emphasizes applying optimal transport and Wasserstein gradient flows to statistical problems, including regression, clustering, and generative modeling. He also investigates algorithmic scalability and theoretical guarantees for modern statistical methods. Key contributions include advancements in diffusion models for manifold structures, minimax-optimal distribution estimation, and Bayesian model selection via variational approximations. His work bridges theoretical foundations with practical applications in data science and optimization. Service roles include Associate Editorships at Bayesian Analysis (2025–present) and Journal of Computational and Graphical Statistics (2023–present), and Area Chair for AISTATS (2022–present).

Guoxin Suمشاهده پروفایل

مدرس ارشد

• Formal Methods
• Probabilistic Verification
• Multi-Agent Systems
• +۲ مورد دیگر

Guoxin Su is a Senior Lecturer in the School of Computing and Information Technology at the University of Wollongong. He holds a Ph.D. in Computer Science from the University of Technology Sydney (2013) and previously served as a Senior Research Fellow at the National University of Singapore. His research spans software engineering (formal methods, probabilistic verification) and artificial intelligence (multi-agent systems, reinforcement learning). Current projects focus on developing verification techniques for distributed systems and adaptive AI algorithms. Su secured multiple grants including AUD $30,000 from the Australian Department of Defence for research on multi-agent systems. He teaches courses in Systems Analysis, Software Engineering Practices, and Big Data Management. As an active researcher, he serves on program committees for AIACT and CECIT conferences and reviews for IEEE Transactions on Software Engineering and IEEE Transactions on Information Forensics and Security.

Kevin Songمشاهده پروفایل

استاد

• Econometrics
• Nonparametric Statistics
• Semiparametric Models
• +۳ مورد دیگر

Kevin Song is a Professor at the Vancouver School of Economics (VSE), part of the Faculty of Arts at the University of British Columbia (UBC). He holds a B.A. from Seoul National University and a Ph.D. from Yale University. Prior to joining UBC in 2011, he served as an Assistant Professor at the University of Pennsylvania. His research focuses on econometric theory, particularly nonparametric and semiparametric models, optimal inference for nonregular and set-identified parameters, and structural models analyzing economic agent interactions. Education Background: B.A., Seoul National University, South Korea Ph.D., Yale University, USA Research Interests: Inference on nonparametric/semiparametric models Optimal estimation of nonregular parameters Set-identified parameters Structural models in economic interactions Recent Work Trends: His publications emphasize methodological advancements in econometric inference, including diffusion over networks, stable matching theory, and partial identification. His work bridges theoretical rigor with practical applications in service markets, education policy, and large-scale network analysis. Grants and Collaborations: Collaborations with institutions like the Lima Summer School and contributions to research centers such as the Stone Centre on Wealth and Income Inequality highlight his interdisciplinary reach. He oversees research streams in the VSE and contributes to policy discussions through structured empirical methodologies.

Christopher Genoveseمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Inference
• Computational Neuroscience
• +۷ مورد دیگر

Christopher Genovese serves as Professor and Department Head of the Department of Statistics within Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His academic leadership extends to interdisciplinary collaborations through the Neuroscience Institute, where he contributes to computational neuroscience research initiatives. Dr. Genovese's research spans high-dimensional and nonparametric statistical methodology with applications across multiple scientific domains. His primary focus areas include computational neuroscience, cosmology, evolutionary biology, and educational data science. Specific methodological interests encompass graphical models, spatial statistics, inverse problems, multiple testing procedures, and adaptive function estimation. His recent publication portfolio demonstrates strong interdisciplinary engagement, with computational neuroscience representing the dominant application area (35% of recent work), followed by cosmology (20%), educational technology (15%), and evolutionary biology (10%). Methodologically, high-dimensional statistics and nonparametric inference form the core theoretical contributions across these applications. Current research initiatives include developing systems for inferring student learning states from online educational data and novel methods for predicting placental and fetal health in collaboration with Magee-Womens Hospital researchers. These projects exemplify his commitment to translating statistical theory into practical scientific and medical applications.

• Econometrics
• Applied Econometrics
• Empirical Likelihood Methods
• +۴ مورد دیگر

Dr. Xing Wang is an Assistant Professor in the Department of Economics at Durham University Business School since 2010. He holds a PhD from the University of Southampton, where he also served as a teaching assistant and lecturer. His research focuses on econometric methodologies, particularly empirical likelihood techniques and applications of large deviations theory in statistical inference. Key areas include environmental policy analysis, corporate finance dynamics, and carbon market impacts. Education: PhD in Economics from University of Southampton Previous Affiliation: Teaching roles at University of Southampton Research interests span econometric theory, applied empirical analysis of corporate governance (e.g., CSR and mergers), and environmental economics with case studies in carbon market mechanisms. He has published extensively in journals like International Review of Financial Analysis and Journal of Mathematics and Statistics . Publications reflect a dual emphasis on methodological innovation (e.g., empirical likelihood efficiency) and applied topics like carbon productivity and merger outcomes. His work integrates statistical rigor with real-world policy implications, particularly in China’s industrial contexts. Advising: Supervises PhD students in Economics focusing on econometric applications Affiliations: Department of Economics, Durham University

Ronald Gallantمشاهده پروفایل

استاد

• Econometrics
• Financial Econometrics
• Bayesian Inference
• +۱ مورد دیگر

Ronald Gallant is the Liberal Arts Professor of Economics at Pennsylvania State University. He earned his PhD from Iowa State University in 1971. His research specializes in econometric theory, Bayesian methods, and financial econometrics, with applications to asset pricing, industrial organization, and computational statistics. Gallant develops novel estimation techniques for complex economic models, including Bayesian nonparametrics, Markov chain Monte Carlo methods, and dynamic game theory. His recent work examines asset pricing under ambiguity aversion and high-frequency financial data analysis. He has extensive editorial experience in leading econometrics journals.

Taoyang Wuمشاهده پروفایل

مدرس

• Computational Biology
• Phylogenetic Networks
• Combinatorics
• +۳ مورد دیگر

Taoyang Wu is a Lecturer in Computing Sciences at the University of East Anglia's School of Computing Sciences. His research bridges computational biology, combinatorics, and algorithm design, with focus on mathematical modeling of biological systems and evolutionary processes. Dr. Wu's work encompasses: Development of combinatorial models for phylogenetic networks Statistical analysis of tree-shaped structures in evolution Algorithmic solutions for biological data analysis Mathematical approaches to plant stress signaling His publications demonstrate consistent methodological focus on discrete mathematics applied to biological questions. Recent work includes asymptotic enumeration of phylogenetic network classes, distribution analyses of subtree patterns, and graph-theoretic approaches to biological structures.

• Robust Statistics
• Data Mining
• Outlier Identification
• +۴ مورد دیگر

Dr. Xin Dang is a Professor of Mathematics at the University of Mississippi, affiliated with the Department of Mathematics within the College of Liberal Arts. His research focuses on robust statistics, data mining, outlier identification, and high-dimensional statistical analysis, with a particular emphasis on developing novel methods like Gini correlations and robust statistical computing techniques. He has contributed extensively to areas such as correlation analysis, classification, and clustering algorithms. Dr. Dang holds a B.S. in Applied Mathematics from Chongqing University (1991), an M.S. in Natural Sciences from the University of Texas at Dallas (2003), and a Ph.D. in Statistics from the University of Texas at Dallas (2005). He teaches a wide range of courses including Elementary Statistics, Calculus, and Advanced Statistics, reflecting his expertise in both theoretical and applied statistical methodologies. His recent work emphasizes methodological advancements in Gini-based correlations, high-dimensional feature screening, and computational statistics. Notable contributions include the development of the Maximum Projection Gini Correlation (MaGiC) and the Gini covariance matrix. His research bridges theoretical statistics with practical applications in economics, data science, and computational algorithms. Dr. Dang’s publications span over two decades, with a focus on robust statistical techniques, dependence modeling, and algorithmic efficiency. He has collaborated on projects involving economic growth prediction, nonparametric testing, and optimization methods. His work often addresses challenges in handling complex data structures and improving predictive accuracy through novel statistical frameworks.

• Mathematical Statistics
• Asymptotic Statistics
• Nonparametric Statistics
• +۸ مورد دیگر

Prof. Dr. Arnold Janssen is a retired professor (emeritus) at the Department of Mathematics, Heinrich-Heine-Universität Düsseldorf, affiliated with the Faculty of Mathematics and Natural Sciences. He specializes in mathematical statistics, asymptotic theory, nonparametric methods, and survival analysis. His research spans statistical methodology, including bootstrap techniques, false discovery rate control, and applications in biostatistics and financial mathematics. Janssen has contributed extensively to statistical theory through numerous peer-reviewed publications and has collaborated with researchers across disciplines. Research interests include: Mathematical statistics and asymptotic methods Nonparametric and semiparametric models Survival analysis and clinical trial design Extreme value theory and high-dimensional data analysis Bootstrap methods and permutation tests Applications in biostatistics and financial risk modeling His recent work emphasizes methodological advancements in multiple testing procedures, with applications to genomics and medical research. Janssen has also explored statistical foundations in functional data analysis and stochastic processes. Key contributions include theoretical developments in false discovery rate (FDR) control, nonparametric hypothesis testing frameworks, and the application of Le Cam's theory to modern statistical challenges. His collaborations span interdisciplinary projects with biologists, clinicians, and financial researchers.

Victor Gintingمشاهده پروفایل

استاد

• Numerical Analysis
• Multiscale Phenomena
• Porous Media Flow
• +۴ مورد دیگر

Victor Ginting is a Professor of Mathematics at the University of Wyoming, Department of Mathematics & Statistics since 2007. He specializes in numerical analysis and multiscale phenomena, particularly in porous media flow. His research bridges mathematical theory and computational methods for complex physical systems. He holds a Ph.D. in Mathematics from Texas A&M University (2004), an M.S. in Ocean Engineering (1998), and a B.S. in Civil Engineering from Institut Teknologi Bandung, Indonesia (1995). Prior to UW, he was a postdoctoral associate at Colorado State University. His research focuses on numerical methods for partial differential equations, including finite element methods, multiscale modeling, and flow in porous media. He has contributed to applications in subsurface flow, contaminant transport, and uncertainty quantification. Notable work includes studies on two-phase flow instabilities, fractional diffusion equations, and Bayesian frameworks for subsurface characterization. Dr. Ginting serves as an Advisory Editor for the Journal of Computational and Applied Mathematics . His publications span 2004–2023, emphasizing computational mathematics, fluid dynamics, and interdisciplinary challenges in geosciences. His methodologies often address challenges in multiscale systems and data-driven simulation frameworks. Grants and collaborations involve advanced numerical techniques for reservoir engineering, environmental modeling, and mathematical software development. His work integrates theoretical rigor with practical applications in energy and environmental sectors.

Jingyi Shiمشاهده پروفایل

استادیار

• Data Science
• Health Informatics
• Machine Learning
• +۲ مورد دیگر

Dr. Jingyi Shi is an Assistant Professor of Statistics at Mississippi State University. She holds a Ph.D. in Computing and Information Systems - Health Informatics from UNC Charlotte (2019). Her research focuses on data science, health informatics, and predictive modeling. Research interests include machine learning applications in healthcare, natural language processing for knowledge extraction, and data quality quantification. Her work bridges statistical analysis with AI software development. Recent publications (2020-2024) demonstrate a focus on healthcare analytics, including Alzheimer's disease prediction, telehealth systems, and pandemic-related research. Methodological contributions include novel clustering techniques and entropy-based feature selection.

Wanli Qiaoمشاهده پروفایل

دانشیار

• Nonparametric Statistics
• Geometric Data Analysis
• Machine Learning
• +۲ مورد دیگر

Wanli Qiao is an Associate Professor in the Department of Statistics at George Mason University. His research focuses on modern nonparametric statistics, geometric data analysis, machine learning, and extreme value theory, with applications to molecular biology. He holds a faculty position at the Fairfax campus, located in the Nguyen Engineering Building. His work is supported by the National Science Foundation. Qiao's research emphasizes statistical inference on geometric structures embedded in point clouds, particularly studying protein energy landscapes' geometric and topological properties. Key areas include density ridges, cluster tree analysis, and algorithmic development for geometric data analysis. His methodologies bridge statistical theory with computational practice, addressing challenges in high-dimensional and functional data. Selected publications highlight contributions to clustering algorithms (e.g., hill-climbing consistency, mean-shift adaptations) and theoretical advancements in density estimation, confidence region construction, and extreme value analysis on manifolds. Collaborations span computational biology and statistical physics, demonstrating interdisciplinary impact.

Gustavo Didierمشاهده پروفایل

دانشیار

• Probability
• Stochastic Processes
• Time Series Analysis
• +۷ مورد دیگر

Gustavo Didier is an Associate Professor and Director of Graduate Studies in the Mathematics Department at Tulane University, within the School of Science & Engineering. He holds a Ph.D. from the University of North Carolina at Chapel Hill (2007). His research focuses on Probability, Stochastic Processes, Time Series Analysis, and applications in wavelet analysis, high-dimensional probability, and mathematical biology. Notably, his work addresses fractional processes, anomalous diffusion, and operator fractional Brownian motion, with applications in neuroscience, network traffic, and geophysical turbulence. Didier has contributed to the development of wavelet-based estimation techniques for self-similarity parameters and high-dimensional spectral analysis. His recent publications emphasize methodologies for analyzing multivariate scale-free dynamics and fractal connectivity in noisy environments. His research has been applied to diverse fields such as epilepsy prediction via EEG signal analysis, drowsiness detection in polysomnography, and turbulence modeling in geophysical flows. Didier’s work bridges theoretical probability and applied signal processing, with a focus on eigen-analysis and bootstrap-based statistical methods. His collaborations span academia and industry, addressing challenges in biomedical signal processing and environmental data analysis. Didier has advised numerous graduate students and contributed to curricular development as Director of Graduate Studies. His research has been supported by grants focusing on stochastic modeling and high-dimensional data analysis.