جست‌وجوی استاد و پژوهشگر

Sakshi Aryaمشاهده پروفایل

استادیار

• Multi-armed bandit problem
• Sequential decision making
• Nonparametric estimation
• +۲ مورد دیگر

Sakshi Arya serves as an Assistant Professor in the Department of Mathematics, Applied Mathematics and Statistics at Case Western Reserve University, located in Room 205D at 2145 Adelbert Road. She maintains active research and teaching responsibilities within the department, with contact accessible via sxa1351@case.edu and phone 216-368-0470. Her research focuses on advanced statistical methodologies and decision theory, specifically: Multi-armed bandit problem for optimal resource allocation Sequential decision making under uncertainty Nonparametric estimation techniques for complex data structures Statistical learning theory foundations Spatio-temporal analysis for geographically distributed data Dr. Arya's work bridges theoretical statistics with practical applications in adaptive systems, emphasizing algorithmic efficiency and robustness in dynamic environments. Her research contributes to machine learning frameworks requiring real-time data processing and decision optimization, particularly relevant in fields like autonomous systems and large-scale data analytics where traditional parametric models prove insufficient.

• Spatial Statistics
• Statistical Consulting
• Biostatistics
• +۳ مورد دیگر

Ronald Barry is a Professor of Statistics at the University of Alaska Fairbanks, where he has maintained an active research and consulting practice since 1991. Holding a PhD from the University of California, Irvine, he operates from office CH 201C (contact: 907-474-7226, rpbarry@alaska.edu) and specializes in methodological innovations with cross-disciplinary applications. Education PhD in Statistics, University of California, Irvine (1991) Research Focus Barry's core expertise lies in spatial statistics, developing advanced techniques for geolocated data analysis in regions with irregular boundaries and holes. His work includes diffusion-based density estimators, nonparametric co-kriging, and lattice-based smoothing algorithms implemented through R packages. This methodological research extends to biostatistical applications in wildlife biology, nutrition, and fisheries, alongside chemometrics projects involving NMR spectroscopy for chemical mixture analysis and medical statistics research on sentinel node biopsy failures in melanoma using SEER cancer data. Publication Trends His recent publications (2015-2021) reveal three dominant trajectories: (1) continuous refinement of spatial modeling techniques for complex geometries, (2) expansion into biomedical and petroleum engineering applications through collaborative consulting, and (3) development of statistical software tools that bridge theoretical methodology with practical implementation. The interdisciplinary nature of his work is evident in publications spanning environmental statistics, reservoir engineering, and adolescent health studies. Scientific Awards No specific awards, fellowships, or honors were documented in the source material. Advising and Collaborations Barry actively mentors graduate students at UAF, with Bernard, Paryani, Canary, and Maier serving as primary authors on collaborative publications. His extensive consulting portfolio—covering experimental design, analysis, and reporting across wildlife biology, nutrition, and fisheries—suggests significant engagement with external research funding, though specific grants aren't detailed. Current projects indicate ongoing collaboration with medical researchers on melanoma diagnostics and petroleum engineers on reservoir modeling.

• Econometrics for cross-sectional and panel data
• Semiparametric and high-dimensional estimation methods
• Nonparametric identification
• +۳ مورد دیگر

Jesper Riis-Vestergaard Sørensen is a Tenure Track Assistant Professor in the Department of Economics at the University of Copenhagen, which is part of the Faculty of Social Sciences. His work focuses on developing advanced econometric methods for analyzing complex economic data. His academic background includes: PhD in Economics from University of California at Los Angeles (2012-2018), with dissertation titled "Essays on Nonparametric and High-Dimensional Econometrics" Sørensen's research centers on methodological innovations in econometrics, particularly addressing challenges in high-dimensional data analysis. His work on semiparametric and nonparametric estimation provides economists with flexible tools that avoid restrictive assumptions about functional forms. His contributions to generalized entropy models offer innovative approaches to modeling economic choices that balance theoretical rigor with empirical adaptability. He has also made significant advances in specification testing for semiparametric models and discrete choice modeling under uncertainty, bridging economic theory with practical applications in consumer behavior analysis. His publication record from 2011-2025 reveals a clear progression from foundational work on household demand analysis toward increasingly sophisticated methodological contributions in high-dimensional econometrics. Recent publications (2021-2025) demonstrate particular expertise in parameter selection methods for complex models, with his 2025 paper on vector autoregressions representing cutting-edge work in time series analysis. A recurring theme across his work is the integration of mathematical optimization techniques (particularly convex analysis) with econometric methodology, as exemplified by his research connecting McFadden's discrete choice theory with Rockafellar's optimization principles. Sørensen has been actively engaged with the professional community through presentations at major conferences including the Econometric Society European Meeting (2021), Econometric Society North American Summer Meeting (2021), and the Econometric Society/Bocconi University Virtual World Congress (2020). His invited talk at Duke University's Class of 2018 Microeconometrics Conference highlights recognition of his expertise in specialized methodological areas.

• Machine Learning
• Bayesian Deep Learning
• Gaussian Process Models
• +۷ مورد دیگر

Rui Li is a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland. Their research focuses on Bayesian deep learning, Gaussian processes, and robust machine learning methods. They collaborate closely with Professor Arno Solin and colleagues on problems involving uncertainty quantification, model generalization, and sensor fusion applications in robotics. Recent work highlights advancements in few-shot classification generalization, scalable Bayesian deep learning predictions, and hyperparameter optimization for Gaussian process models. Publications span conferences like WACV, ICLR, ICML, and ICCVW, addressing theoretical and applied challenges in machine learning and computer vision. Rui Li contributes to interdisciplinary research groups tackling problems in semantic segmentation robustness and visual-inertial SLAM optimization. Their collaborative efforts include participation in the Robust Semantic Segmentation UNCV2023 Challenge with a multi-institutional team.

Jason Fletcherمشاهده پروفایل

استاد

• Health Economics
• Social Genomics
• Economics of Education
• +۵ مورد دیگر

Jason Fletcher is a Professor of Public Affairs at the University of Wisconsin–Madison with additional appointments in Applied Economics and Population Health Sciences. He serves as a Vilas Distinguished Achievement Professor and previously directed the Center for Demography of Health and Aging (2018-2023) and the Wisconsin Research Data Center (2018-2022). His extensive institutional affiliations include the Center for Demography and Ecology, Institute for Research on Poverty, Holtz Center for Science & Technology Studies, Data Science Institute, National Bureau of Economic Research (NBER), and Institute for the Study of Labor (IZA). Fletcher specializes in health economics, social genomics, and child and adolescent health policy. His research examines social network effects on adolescent outcomes, gene-environment interactions, long-term consequences of childhood mental illness, and early life conditions affecting later health. His work bridges economics, genetics, and public health to understand how social and biological factors jointly shape life trajectories. His recent publications (2024-2025) demonstrate continued productivity with a focus on gene-environment interactions, historical determinants of health, and life course approaches to understanding health disparities. Fletcher employs sophisticated methodologies including polygenic indices, sibling fixed effects, and historical data analysis to address causal questions in health economics. Fletcher has received significant recognition including a William T. Grant Foundation career development award (2012) and a Guggenheim Fellowship (2023). His scholarly impact is evident through his numerous publications in top journals across economics, public health, and social sciences. As an educator, Fletcher teaches courses including Microeconomic Policy Analysis and Health Policy and Policy Design. He has mentored numerous students and collaborated extensively across disciplinary boundaries, reflecting his integrative approach to understanding health and social outcomes.

Tiemen Woutersenمشاهده پروفایل

استاد

• Econometrics
• Duration Models
• Panel Data Models
• +۲ مورد دیگر

Tiemen Woutersen serves as the Eller Professor of Economics within the Department of Economics at the University of Arizona's Eller College of Management. He teaches graduate-level courses including Econometrics and Machine Learning (ECON 515), Econometrics A/B (ECON 522), and Topics in Econometrics (ECON 696E), reflecting his expertise in advanced quantitative methods. His academic foundation includes a PhD in Economics from Brown University (2000). Woutersen's research centers on methodological advancements in econometrics, particularly focusing on relaxing restrictive assumptions in economic models. Key interests include duration models with unobserved heterogeneity, panel data estimation, identification strategies in industrial organization, and robust techniques for instrumental variables regression with heteroscedasticity and many weak instruments. His work bridges theoretical econometrics and practical applications across health economics and industrial organization. Analysis of his publication record reveals a sustained focus on improving estimation precision in challenging econometric scenarios. Recent work emphasizes jackknife methods for cluster-sampled IV models, specification testing power enhancement, and semi-parametric duration modeling without heterogeneity constraints. His collaborations with leading figures like Jerry Hausman and Norman Swanson demonstrate integration within the econometrics mainstream. While the source material does not document specific advising relationships or grant funding, Woutersen's extensive co-authorship network indicates active mentorship and collaborative research practices. His current working papers on data breach sanctions and nonparametric identification pedagogy suggest evolving interests at the intersection of econometric theory and emerging data challenges.

Emilia Sivieroمشاهده پروفایل

پژوهشگر

• Spatial Statistics
• Extreme Value Theory
• Nonparametric Covariance Estimation
• +۴ مورد دیگر

Emilia Siviero is a Post-doctoral Researcher at Università Ca' Foscari, Venezia in the Department of Environmental Sciences, Informatics and Statistics (DAIS) , working on Spatial Statistics and Extreme Value Theory under the supervision of Ilaria Prosdocimi and Carlo Gaetan. Previously, she earned her PhD at Télécom Paris with a thesis titled Statistical Learning for Spatial Data: Theory and Practice , focusing on spatial statistics and spatio-temporal Hawkes processes. Her research spans theoretical and applied work in Spatial Statistics , Nonparametric Covariance Estimation , Spatio-temporal Hawkes Processes , and Extreme Value Theory . Recent publications address parametric inference for Hawkes processes, Kriging from a machine learning perspective, and stochastic algorithms in optimal transport. She has presented at international conferences such as COMPSTAT and CAp, and organized events like the Chaire DSAIDIS day. Her teaching experience includes courses on Statistical Learning , Machine Learning , and Probability for Télécom Paris and Mines ParisTech students. She is actively involved in computational statistics and climate data analysis projects, with software tools available on GitHub.

• Financial Econometrics
• Time Series Analysis
• GARCH Models
• +۴ مورد دیگر

Jean-Michel ZAKOÏAN is a Professor at ENSAE since 2007 and Professor of Applied Mathematics at Lille University (on secondment at ENSAE since 2008). He serves as a Research Fellow at CREST and previously headed the Finance-Insurance lab at CREST from 2007 to 2021. His academic affiliations reflect a strong connection between theoretical econometrics and practical financial applications. His research interests span across Financial Econometrics , Time Series Analysis , GARCH-type models , Dynamic risk measures , Unit roots and explosive processes , Functional and Long-Memory Time Series , and Nonstationary processes . His work bridges theoretical econometric methods with practical financial applications, particularly in volatility modeling and risk measurement. ZAKOÏAN's publications include co-authoring the influential book "GARCH models. Structure, Statistical Inference and Financial Applications" (2nd edition, 2019) and over 80 publications in top journals including Econometrica , Annals of Statistics , Journal of the Royal Statistical Society (Series B) , and Journal of the American Statistical Association . His research shows a consistent focus on developing and refining econometric methods for financial time series analysis, particularly in modeling volatility and risk. Associate Editor for Econometric Theory (2012-present) Associate Editor for Scandinavian Journal of Statistics (2019-2023) Associate Editor for Journal of Time Series Analysis (2013-present) Guest Associate Editor for Annals of Computational and Financial Econometrics (2011-2015) As an educator, ZAKOÏAN has supervised or co-supervised 13 defended PhD theses in Applied Mathematics and Economics. He teaches courses including Dynamic Models with Latent Variables, Financial Econometrics, and Introduction to Time Series Econometrics, reflecting his expertise in both theoretical and applied aspects of time series analysis.

Martin Wahlمشاهده پروفایل

استاد

• Mathematical Statistics
• High-Dimensional Statistics
• Probability Theory
• +۲ مورد دیگر

Martin Wahl is a Professor (W2) in the Faculty of Mathematics at Bielefeld University, appointed in 2023. He obtained his doctorate from Heidelberg University in 2015, followed by postdoctoral research at Humboldt University of Berlin (2015-2022) and a Feodor Lynen Research Fellowship at Georgia Tech. His research centers on mathematical statistics with emphasis on: High-dimensional statistics and probability theory Statistical learning theory and dimension reduction techniques Nonparametric estimation and minimax optimality Spectral methods for covariance operators and random matrices His recent articles (2020-2025) demonstrate strong focus on spectral methods, perturbation analysis, and error bounds in high-dimensional settings, with applications spanning manifold learning, PCA optimization, and stochastic PDEs. Theoretical developments in minimax estimation and concentration inequalities form consistent themes. Awards: Feodor Lynen Research Fellowship (Georgia Tech) He leads the project "Convexity and Grassmann Manifolds in Statistical Inference" (2027) under the Priority Program "Combinatorial Synergies". He is affiliated with the Bielefeld Graduate School in Theoretical Sciences and the Center for Statistics.

Jae-Kwang Kimمشاهده پروفایل

استاد

• Survey Sampling
• Missing Data Analysis
• Semiparametric Estimation
• +۳ مورد دیگر

Jae-Kwang Kim is a Professor in the Department of Statistics at Iowa State University's College of Liberal Arts and Sciences. He holds Fellow status in both the American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS), was named LAS Dean's Professor (2020-2022), and currently serves as President of the Korean International Statistical Society (KISS) for 2023-2024. His educational background includes: PhD in Statistics, Iowa State University (2000) MS in Statistics, Seoul National University (1993) BS in Computer Science and Statistics, Seoul National University (1991) Professor Kim's research spans survey sampling, missing data analysis, semiparametric estimation, and causal inference. He has pioneered methods for handling nonignorable nonresponse, developed fractional hot deck imputation techniques, and advanced data integration approaches combining probability surveys with big data sources. His work bridges theoretical statistics with practical survey methodology challenges. His recent publications (2022-2025) reveal a concentrated focus on semiparametric imputation using Gaussian mixture models, functional calibration under non-probability sampling, and bootstrap inference for complex survey designs. Key themes include handling multivariate missing data, addressing selection bias in voluntary samples, and developing robust estimation techniques under informative sampling frameworks. His major recognitions include: Gertrude M Cox Award (2015) LAS Dean's Professor (2020-2022) Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Professor Kim has held academic appointments at Hankook University of Foreign Studies (2002-2003), Yonsei University (2004-2008), and KAIST (2016-2018), alongside his primary position at Iowa State University (2008-present). His pre-academic career included roles as Mathematical Statistician at the US Census Bureau (1999-2000) and Senior Statistician at Westat (2000-2001).

Søren Wengel Mogensenمشاهده پروفایل

دانشیار

• Causal Inference
• Stochastic Processes
• Survival Analysis
• +۳ مورد دیگر

Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.

• Labor Economics
• Public Finance
• Econometrics
• +۵ مورد دیگر

Thomas Le Barbanchon is a Professor of Economics at Bocconi University, holding the Rodolfo Debenedetti Chair in Labor Economics since 2024. He serves as Director of the research center IGIER since January 2025 and maintains affiliations with CEPR, CREST, J-PAL, IZA, and IFS. His research focuses on labor economics, public finance, and econometrics, with significant contributions to understanding unemployment insurance, job search behavior, gender disparities, and labor market responses to pandemics. PhD in Economics from École Polytechnique (CREST-ENSAE), France M.Sc. in Economics from Universitat Pompeu Fabra, Spain His research explores labor economics through empirical and methodological lenses. Key areas include unemployment insurance effects , job search dynamics , and gender wage gap mechanisms . Recent work investigates AI-driven job matching, hiring difficulties, and wage-setting through his ERC-funded WeSET project (2025-2030). Articles span top journals like American Economic Journal: Applied Economics, Quarterly Journal of Economics, and Journal of Public Economics. Scientific awards include the 2022 Bocconi Impact Award , ERC Consolidator Grant (2024-2029) , and ERC Starting Grant (2018-2023) . He has advised numerous PhD students and led institutional roles at Bocconi, including hiring committees and program leadership. His work bridges rigorous econometric methods with real-world policy evaluation for France Travail, public employment service reforms, and education initiatives.

Gery Geenensمشاهده پروفایل

مدرس ارشد

• Nonparametric Statistics
• Semiparametric Regression
• Copula Modeling
• +۵ مورد دیگر

Dr. Gery Geenens is a Senior Lecturer in the School of Mathematics & Statistics at UNSW Sydney, specializing in nonparametric and semiparametric statistical methods. He holds a PhD in Sciences (Statistics) from Université catholique de Louvain (Belgium, 2008) and joined UNSW in 2009 after postdoctoral work at the University of Melbourne under Professor Peter Hall. His research focuses on developing flexible nonparametric models for statistical analysis, with particular expertise in copula modeling, functional data analysis, and high-dimensional density estimation. Key contributions include: Nonparametric copula-based conditional density estimation Wavelet-based multivariate density estimation with shape preservation Applications in sports analytics (e.g., football/soccer outcome modeling) Solutions to the "Curse of Dimensionality" in functional regression Recent publications demonstrate strong activity in dependence modeling and theoretical foundations of nonparametric statistics, with significant work on Sklar's theorem and Hellinger correlation. His research bridges theoretical advances with practical applications in finance, hydrology, and sports analytics. As Director of Postgraduate Studies (Coursework) and Associate Editor of "Statistics and Probability Letters", he actively contributes to academic service. His teaching portfolio includes core statistics courses for engineering and advanced probability. Current research is supported by UNSW Faculty Research Grants (2014-2016) and prior Early Career Research Grants (2010-2013). He co-supervises PhD candidates including Carlos Aya Moreno on wavelet-based density estimation.

Igor Borisovمشاهده پروفایل

استاد

• Probability Theory
• Mathematical Statistics
• Stochastic Processes
• +۴ مورد دیگر

Prof. Igor Borisov is a distinguished academic affiliated with Novosibirsk State University (NSU) as a Professor in the Chair of Probability Theory and Mathematical Statistics. He also holds the position of Top-level Research Fellow at the Sobolev Institute of Mathematics, Russia. His career spans over five decades, with a Doctor of Science degree (1988) and a Ph.D. in Probability and Statistics (1978). Educational Background: Novosibirsk State University (1969-1974), followed by a Post Graduate Course at Sobolev Institute of Mathematics (1974-1978). His research focuses on Probability Theory, Mathematical Statistics, and Stochastic Processes, with significant contributions to Poisson approximation, limit theorems, and asymptotic analysis of random walks. His recent work explores kernel-type estimators, exponential inequalities for dependent data, and canonical statistics in nonlinear regression models. The 15 most recent articles highlight his expertise in Poisson approximation, canonical processes, and random walk dynamics. Topics include asymptotic behavior, exponential inequalities, and statistical inference for dependent observations. He has supervised 11 Ph.D. students, including Evgenii Shefer (2021) and Vasilii Zhechev (2018), and contributes to teaching through materials like Probability and Statistics lectures at NSU.

• Machine Learning
• Graph Neural Networks
• Bayesian Inference
• +۳ مورد دیگر

Diego Parente Paiva Mesquita serves as a Visitor (Faculty) in the Department of Computer Science under Professor Samuel Kaski's research group. His work bridges theoretical and applied machine learning with emphasis on scalable probabilistic systems. His academic credentials include: Doctor of Technology in Computer Science (awarded December 16, 2021) Bachelor of Computer Science from the Brazilian Ministry of Education (awarded August 23, 2016) Mesquita's research focuses on advancing graph neural networks, Bayesian inference, and incomplete data handling. His fingerprint reveals strong contributions to probabilistic modeling (60% Approximates), random variable analysis (53%), and machine learning systems (50%), with particular innovation in neural network architectures (44%) for complex data structures. Recent publications (2022-2024) demonstrate a cohesive trajectory toward parallelizable machine learning frameworks. Key themes include temporal graph representation learning, embarrassingly parallel Monte Carlo methods, and thin/deep Gaussian process hybrids—addressing critical scalability bottlenecks in probabilistic AI while maintaining theoretical rigor.