جست‌وجوی استاد و پژوهشگر

Jungbin Hwangمشاهده پروفایل

دانشیار

• Econometrics Theory
• Applied Econometrics
• Financial Econometrics
• +۵ مورد دیگر

Jungbin Hwang is an Associate Professor in the Department of Economics at the University of Connecticut. He specializes in econometrics theory, with a focus on improving the accuracy and robustness of Generalized Method of Moments (GMM) methods in handling time series and panel data with dependence and heterogeneity. His research also extends to financial econometrics, Bayesian methods, and cointegration analysis. Education: Ph.D., Economics, University of California, San Diego (2016) M.A., Economics, Seoul National University (2010) B.A., Economics, Seoul National University (2008) Research Interests: Efficiency and approximation in GMM estimation Cluster-robust inference and bootstrap methods Cointegration in non-stationary systems Applications to financial markets and policy analysis Teaching: Courses include Empirical Methods in Economics, Econometrics I, and advanced topics in panel data analysis. Key Contributions: His work addresses challenges in GMM inference for time series and panel data, including finite-sample corrections and robust variance estimation. Recent studies explore low-frequency cointegration and quantile regression in dynamic settings. Grants & Collaborations: Collaborations with scholars like Yixiao Sun and Gonzalo Valdés have produced influential methods for accurate econometric testing and inference. Contact: Located in 333 Herbst Hall, Storrs, CT. Office hours: Wednesdays 3:00-4:00 PM or by appointment.

Tom Bootمشاهده پروفایل

دانشیار

• Macroeconomic Forecasting
• High-Dimensional Data Analysis
• Econometric Theory
• +۴ مورد دیگر

Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.

Zhenke Wuمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Precision Medicine
• Latent Variable Models
• +۵ مورد دیگر

Zhenke Wu is an Associate Professor (with tenure) in the Department of Biostatistics at the University of Michigan School of Public Health. He holds affiliate appointments at the Michigan Institute for Data and AI in Society (MIDAS) and leads the Michigan Statistics for Individualized-healthcare Lab (MiSIL). His research bridges statistical methodology and public health applications, with particular focus on precision medicine. Educational background includes: PhD in Biostatistics from Johns Hopkins University (2014) BS in Mathematics from Fudan University (2009) Dr. Wu's methodological research focuses on: Structured Bayesian latent variable models for disease subtyping and clustering Causal inference methods for sequential interventions in mobile health studies Reinforcement learning frameworks for personalized health interventions Scalable computation for high-dimensional biomedical data His applied work spans infectious diseases, mental health, autoimmune disorders, and cancer through collaborations with multiple research consortia including the Intern Health Study and UZIMA-DS project in Africa. Recent publications demonstrate strong focus on Bayesian methods, causal inference, and reinforcement learning applications in digital health. Article themes include mobile health interventions, synthetic EHR development, fair machine learning algorithms, and novel approaches for longitudinal and survival data analysis. Methodological innovations consistently address challenges in precision medicine and individualized health decision-making. Dr. Wu leads several collaborative initiatives including the Precision Health Use Case for mental health treatment (PROMPT) and partners with the Rogel Cancer Center. He advises multiple PhD students and postdoctoral researchers in statistical methodology development and health applications.

• Digital signal processing
• Smart grids
• Measurement systems
• +۵ مورد دیگر

David Macii is Associate Professor at the Department of Industrial Engineering, University of Trento, Italy, where he teaches "Digital Signal Processing for Mechatronics" and co-leads the "Laboratory of Internet of Things." His core expertise lies in digital signal processing, measurement science, smart-grid instrumentation, indoor positioning and industrial IoT applications. Research interests revolve around four pillars: (i) advanced estimation algorithms for frequency, ROCOF and synchrophasors to enhance power-quality monitoring in future smart-grids with high PV and EV penetration; (ii) design and metrological characterisation of low-cost PMU and smart-meter solutions; (iii) radar- and RFID-based indoor localisation and tracking for robotics and assisted-living scenarios; and (iv) embedded, IoT-enabled measurement systems bridging DSP, mechatronics and industrial electronics. Recent publications (2023-2025) reveal a clear methodological trend: development of fast, uncertainty-aware DSP algorithms (interpolated DFT, Kalman filtering, harmonic whitening) validated against real-world noise, interference and contingency conditions, followed by their embedding into resource-constrained hardware platforms for EV charging coordination, grid-support converters and robotic navigation. Although the supplied text does not list specific grants or doctoral students, the steady stream of joint publications with European colleagues and his leading teaching role in two inter-departmental master courses indicate an active, well-integrated research and educational profile within the University of Trento.

• Stochastic Analysis
• Mathematical Finance
• Option Pricing
• +۵ مورد دیگر

Blanka Horvath is a Lecturer at King's College London and Honorary Lecturer at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). Her research focuses on stochastic analysis and mathematical finance, particularly in numerical methods, machine learning applications, and volatility modeling (e.g., SABR and rough volatility models). She holds a PhD from ETH Zurich (2015), a Diplom in Mathematics from the University of Bonn, and an MSc in Economics from the University of Hong Kong. She has organized major conferences such as the SIAM MMF 2017 mini-symposium and co-organized the Rough Volatility Meeting at Imperial College. Her honors include the 2019 Risk Rising Star Award and the 2024-25 LMS Emmy Noether Fellowship. She collaborates with institutions like UBS, The Alan Turing Institute, and Quantennium LTD. Her teaching includes courses on numerical methods in finance and Python/R programming. She supervises PhD and MSc students in areas like rough volatility, machine learning, and quantitative finance. Recent work explores quantum GANs for option pricing and regime detection using Wasserstein distances.

Yanrong Yangمشاهده پروفایل

دانشیار

• High Dimensional Statistics
• Large Dimensional Random Matrix Theory
• Functional Data Analysis
• +۳ مورد دیگر

Yanrong Yang is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, The Australian National University. Her research focuses on high-dimensional statistical inference, large-dimensional random matrix theory, functional data analysis, and responsible statistical learning. She has developed asymptotic theories for high-dimensional statistics and applied them to time series forecasting and panel data analysis. PhD in Statistics, Nanyang Technological University (2009-2013) MSc in Statistics, Shandong University (2006-2009) BSc in Statistics, Shandong University (2002-2006) Her research explores high-dimensional data analysis, including eigenvalue methods, functional principal component analysis, and applications to mortality forecasting and financial portfolio optimization. Recent publications examine fairness-aware models for annuity pricing, robust PCA techniques, and eigen-analysis for time series clustering. She has published extensively in top journals such as the Annals of Statistics, Journal of Econometrics, and Journal of the American Statistical Association. Her current project, Feature Learning for High-dimensional Functional Time Series (2023-2026), investigates representation learning in financial time series.

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +۳ مورد دیگر

Ye Lu is a Senior Lecturer in the School of Economics at the University of Sydney. She holds a PhD in Economics from Indiana University Bloomington (2017). Her research focuses on econometric theory with applications to time series analysis, financial econometrics, and large-dimensional data. Key interests include continuous time modelling with high-frequency data, nonlinear factor models, and econometric methods for event-driven data. Education: PhD in Economics, Indiana University Bloomington (2017) Research emphasizes developing robust methodologies for data-rich environments, addressing challenges in traditional econometric approaches. Recent work includes bootstrap inference for Hawkes processes and zero-inflated GARX models for energy price spikes. Teaching responsibilities include courses like ECMT1020 (Introduction to Econometrics) and ECOS3904 (Applied Macroeconometrics). Her publications appear in top journals such as Journal of Econometrics and Energy Economics .

Francis L. Huangمشاهده پروفایل

استاد

• Quantitative Methods
• Multilevel Modeling
• School Climate
• +۴ مورد دیگر

Dr. Francis L. Huang is a Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri-Columbia 's College of Education and Human Development. An applied quantitative methodologist , he teaches courses in program evaluation, multilevel modeling, and data management while researching school climate, bullying prevention, and large-scale educational data analysis. PhD in Research, Statistics, and Evaluation (University of Virginia) MA in Instructional Technology and Media (Teachers College, Columbia University) BS in Legal Management (Ateneo de Manila University) His methodological expertise spans clustered data analysis , plausible values modeling , and robust standard error estimation . He develops tools like the MLMusingR package for multilevel modeling in education research. Recent work focuses on 2025 grant-funded studies about data weighting in international assessments and 2024 open-access replication frameworks for nonexperimental datasets. He advocates for rigorous causal inference and equitable discipline policy analysis through projects like the National Center for Rural School Mental Health (funded by Institute of Education Sciences). Dr. Huang contributes to Missouri Prevention Science Institute as Methodology Co-Director and collaborates with interdisciplinary teams on school violence prevention and behavioral threat assessment systems. His 2023-2025 publications demonstrate technical innovations in three-level cluster-robust errors and missing data handling for large-scale assessments.

Joseph Glazمشاهده پروفایل

استاد

• Applied Probability
• Geometrical Probability
• Probability Approximations
• +۷ مورد دیگر

Joseph Glaz is a Professor in the Department of Statistics at the University of Connecticut. His work focuses on applied probability and statistical methodology, particularly in scan statistics and related inference techniques. Contact: joseph.glaz@uconn.edu Phone: (860) 486-4193 (Storrs Campus) Research highlights: Develops scan statistics for discrete , continuous , and conditional data frameworks Specializes in change-point detection for normal data mean/variance Innovates robust methods for outlier-prone datasets Extends scan statistics to network and graph structures Contributed to foundational texts like the Handbook of Scan Statistics Publication trends include: Scan statistics for genomic data (Hi-C translocation detection) Nonparametric and Bayesian extensions of scan methods Approximations and inequalities for sequential testing Applications in quality control , medical imaging , and sensor networks

Pierre-Alexandre Matteiمشاهده پروفایل

پژوهشگر

• Statistical Machine Learning
• Generative Modeling
• Model Uncertainty
• +۷ مورد دیگر

Pierre-Alexandre Mattei is a research scientist at Inria, affiliated with the Maasai team in Sophia Antipolis and the J.A. Dieudonné Laboratory at Université Côte d'Azur. He holds a chair at the 3IA Côte d'Azur institute and has a strong academic background in applied mathematics, having earned his Ph.D. from Université Paris Descartes (now Université Paris Cité) under Charles Bouveyron and Pierre Latouche, followed by a postdoc at the IT University of Copenhagen with Jes Frellsen. Ph.D. in Applied Mathematics, Université Paris Descartes (2017) Postdoctoral Researcher, IT University of Copenhagen His research lies at the intersection of statistical machine learning, generative modeling, and uncertainty quantification, with a focus on hidden variables, missing data, and model interpretability. He has co-organized major workshops such as Artemiss, GenU, SophI.A Summit, and Statlearn, and teaches at the Generative Modeling Summer School (GeMSS). His recent work spans energy-based models, clustering, semi-supervised learning, and medical AI applications. The 15 most recent publications reflect a consistent trend in developing statistically principled methods for generative modeling, with emphasis on likelihood-based inference, missing data, and information-theoretic approaches to clustering and representation learning. His work frequently appears in top venues like NeurIPS, ICML, ICLR, and AISTATS, as well as in journals such as Statistics and Computing and JACC: Advances. Co-organizer, Generative Modeling Summer School (GeMSS) Co-organizer, Workshop on Generative Models and Uncertainty Quantification (GenU) Co-organizer, SophI.A Summit Co-organizer, Statlearn Mattei has advised several PhD students and postdocs, including Raphaël Razafindralambo, Hugo Senetaire, Louis Ohl, Federico Bergamin, and Hugo Schmutz, many of whom have gone on to research positions in Copenhagen, Grenoble, Linköping, and Marseille. He collaborates extensively with researchers across France and Denmark, particularly with Jes Frellsen, Frédéric Precioso, and Charles Bouveyron. He is actively involved in the development of open-source tools and libraries such as the GemClus Python library for discriminative clustering. He is a key member of the Maasai team at Inria Sophia Antipolis, which focuses on models and algorithms for artificial intelligence, and contributes to the broader 3IA Côte d'Azur initiative aimed at advancing AI research through interdisciplinary collaboration.

• Time Series Analysis
• Energy Analytics
• Statistical Clustering
• +۳ مورد دیگر

Dr. Andrés Modesto Alonso is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Energy Analytics research group and Flores de Lemus Institute. His work spans computer science, economics, and statistics through advanced time series analysis and energy forecasting methodologies. Primary affiliation: Department of Statistics, UC3M Research groups: Energy Analytics, Flores de Lemus Institute His research focuses on time series analysis , energy forecasting , and statistical clustering with applications in electricity markets, smart grids, and environmental data. Recent publications emphasize deep learning models for energy prediction, dynamic factor models, and market-based distance metrics. Scientific output trends show 15 recent articles (2018-2024) covering topics like: Electricity market price forecasting Smart grid optimization through clustering Adaptive control charts for industrial processes Precision matrix estimation in high-dimensional statistics Extreme value analysis for environmental monitoring Dr. Alonso has supervised multiple theses on time series modeling and classification techniques, while collaborating on grants related to stochastic optimization and responsible AI applications in economic forecasting.

Patrick J.F. Groenenمشاهده پروفایل

استاد

• Data Science
• Multidimensional Scaling
• Optimization Algorithms
• +۵ مورد دیگر

Patrick J.F. Groenen is a full Professor of Statistics at the Erasmus School of Economics (ESE), Erasmus University Rotterdam , and currently serves as its Dean. His academic career spans leadership roles, including Director of the Econometric Institute (2014-2020) and President of the International Association for Statistical Computing (2015-2017). He has held visiting positions at Stanford University and contributed extensively to multidimensional scaling (MDS), data science , and statistical genetics . His research focuses on numerical algorithms for MDS, support vector machines , and optimization techniques applied to diverse fields. He is a founding editor of the open-access journal Journal of Data Science, Statistics, and Visualisation and has authored major textbooks including Modern Multidimensional Scaling and Applied Multidimensional Scaling and Unfolding . His work appears in top journals like Nature Genetics , Psychometrika , and Journal of Machine Learning Research . Recent publications highlight advancements in convex clustering , genomic prediction , and robust statistical methods . He has developed influential R packages such as GenSVM , SVMMaj , and smacof for multiclass classification and MDS software. His supervisory record includes 14 PhD students across economic psychology, genetics, and data science. Academic activities include editorial roles at Advances in Data Analysis and Classification and Psychometrika , alongside organizing international conferences like CARME 2007 on correspondence analysis. His contributions to nonlinear biplots and response style modeling have advanced visual data interpretation in social sciences.

Tasha Beretvasمشاهده پروفایل

استاد

• Quantitative Methods
• Psychometrics
• Multilevel Modeling
• +۳ مورد دیگر

Tasha Beretvas is a Professor of Quantitative Methods in the Department of Educational Psychology and Associate Dean of Research and Graduate Studies at the College of Education, University of Texas at Austin. She holds the John L. and Elizabeth G. Hill Professorship and has led faculty affairs as Senior Vice Provost for Faculty Affairs. Research focuses on psychometric models, multilevel modeling with complex data structures (e.g., student mobility), and meta-analytic techniques for single-case designs. Key contributions include methodological advancements in multilevel modeling, meta-analysis of dependent effect sizes, and synthesis of single-subject experimental data. Her work appears in journals like Multivariate Behavioral Research , Psychological Methods , and Journal of Educational Psychology . Recent articles emphasize meta-analysis of single-case designs, handling missing data in meta-regression, and cluster wild bootstrapping for dependent effect sizes. She has pioneered tools like MultiSCED for multilevel analysis of single-case data. Scientific Awards Regents Award for Excellence in Undergraduate Teaching Grants Principal Investigator (PI) or Co-PI on multiple Institute of Education Sciences (IES) grants Leadership Elected member, Society for Research Synthesis Methodology Past Associate Editor, Journal of School Psychology and Journal of Educational Psychology Member, Design and Analysis Committee for the National Assessment of Educational Progress (NAEP)

Stéphane Bonhommeمشاهده پروفایل

استاد

• Microeconometrics
• Econometric Theory
• Labour Economics
• +۳ مورد دیگر

Stéphane Bonhomme is the Ann L. and Lawrence B. Buttenwieser Professor of Economics and the College at the University of Chicago's Kenneth C. Griffin Department of Economics. His research focuses on microeconometrics, econometric theory, and labor economics, with emphasis on latent variable modeling and panel data analysis. He holds a PhD from the University of Paris I, Panthéon-Sorbonne (2005). Key contributions include methodologies for handling unobserved heterogeneity in panel data, nonlinear persistence in consumption dynamics, and bias reduction in econometric models. His work has been published in top journals like Econometrica, Journal of Econometrics, and the Journal of Political Economy. Recent research explores grouped patterns of heterogeneity, firm-worker sorting effects, and functional differencing in networks. He is a Fellow of the Econometric Society (2017) and has developed widely used Stata/Python packages for bias correction and discrete heterogeneity estimation.

Stefan Van Aelstمشاهده پروفایل

استاد

• Robust statistics
• Analysis of complex data
• Functional data analysis
• +۵ مورد دیگر

Stefan Van Aelst is a Professor at the Department of Mathematics, KU Leuven, and heads the Statistics and Data Science unit. He is actively involved in research on robust statistical methods for complex and high-dimensional data. Affiliation: Faculty of Science, KU Leuven Research: Robust statistics, functional data, fuzzy data, and applications in chemometrics and bioinformatics Teaching: Advanced Statistical Methods, Robust Statistics, Statistical Data Analysis His recent publications focus on robust dispersion estimation, sparse logistic regression, functional data analysis, and multilinear PCA. Collaborations include researchers like Rousseeuw, Zamar, and Verdonck. He contributes to software development, including the MATLAB library LIBRA and R packages like robustbase and cellWise , and participates in organizing seminars and conferences.