جست‌وجوی استاد و پژوهشگر

Sam Baughمشاهده پروفایل

استادیار

• Spatial Statistics
• Climate Science
• Environmental Statistics
• +۳ مورد دیگر

Sam Baugh is an Assistant Professor of Statistics at Pennsylvania State University's Department of Statistics since Fall 2023. He holds a PhD from UCLA (2022) and MS/BS degrees from the University of Chicago. His research focuses on developing statistical methods for climate change impacts, spatio-temporal modeling, Bayesian hierarchical approaches, and psychometrics, with collaborations in medicine/public health. Education: Ph.D. in Statistics, UCLA (2022) MS in Statistics, University of Chicago (Advisor: Michael Stein) BS in Mathematics, University of Chicago Research interests emphasize climate science applications, including ocean/atmosphere changes and extreme event probabilities. Methodologically, he specializes in: Bayesian hierarchical models Spatio-temporal statistics Computational approximations Psychometric modeling Recent work spans climate attribution studies, medical AI applications, and latent variable modeling. His publications demonstrate cross-disciplinary impact across climate science, surgery outcomes, and educational measurement. Awards include the NSF Postdoctoral Fellowship (2022-2023) and UCLA's Outstanding Ph.D. Student Award (2022). Collaborates with institutions like Lawrence Berkeley National Lab and medical teams in plastic surgery research. Active in both environmental and health-related statistical method development.

Alistair Stewartمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Computer Science
• Algorithms
• Machine Learning
• +۳ مورد دیگر

Alistair Stewart is a Research Fellow in the Department of Computer Science at the University of Southern California (USC), working with Professor Ilias Diakonikolas. Previously, he completed his PhD at the University of Edinburgh under the supervision of Professor Kousha Etessami, focusing on algorithms for infinite-state stochastic models. His research interests span theoretical computer science, algorithms, machine learning, and probability theory. Notable areas include robust statistical estimation, distribution learning, algorithmic game theory, and probabilistic systems analysis. His work has been published in top conferences such as SODA, FOCS, COLT, and STOC. Key contributions include advancements in high-dimensional robust estimation, efficient learning algorithms for discrete distributions, and analysis of stochastic models like branching processes and context-free grammars. No scientific awards are explicitly mentioned in the provided texts. His academic trajectory includes collaborations with leading researchers in theoretical computer science, and his PhD thesis addressed foundational problems in stochastic algorithmics.

Ghislain Verdierمشاهده پروفایل

مدرس

• Statistical Process Control
• Reliability Modeling
• Change-Point Detection
• +۲ مورد دیگر

Ghislain Verdier is a Lecturer at the University of Pau and Pays de l'Adour, affiliated with the Data Science department at IUT des Pays de l'Adour and the Probability and Statistics research team at LMAP. His expertise covers statistical methods for reliability and process monitoring. Research focuses on: Change-point detection in dynamic systems Control charts for multivariate processes Stochastic degradation modeling Nonparametric sequential analysis Extended gamma process applications He developed goodness-of-fit procedures for gamma processes and dependence models for Lévy wear processes. He has supervised PhD students on reliability topics and serves on academic committees, including the CNRS expert panel (sections 25-26).

Bruno Ebnerمشاهده پروفایل

پژوهشگر ارشد

• Statistical Methodologies
• Goodness-of-Fit Tests
• Multivariate Statistics
• +۶ مورد دیگر

Bruno Ebner is a Senior Research Fellow at the Karlsruhe Institute of Technology (KIT), specializing in statistical methodologies and their applications in applied mathematics and mathematical statistics. He obtained his PhD in Mathematics from KIT in 2010 and has been a permanent faculty member since 2012. His research focuses on developing statistical methods for uni-, multi-, and infinite-dimensional data, including non-Euclidean and metric spaces. Key research interests include goodness-of-fit testing, multivariate statistics, directional statistics, functional data analysis, and Stein's method. Notable contributions include the development of Sobolev tests on hyperspheres and the analysis of eigenvalues in covariance operators. Ebner has secured grants such as the STENED project, supporting PhD students like Paul Axmann and Vinícius Litvinoff. He has authored over 50 peer-reviewed articles in journals like TEST and the Journal of Nonparametric Statistics, with a focus on multivariate normality testing, exponentiality tests, and distributional characterization. His work emphasizes affine-invariant tests and neighborhood model validation. Ebner’s research bridges theoretical statistics with practical applications in data analysis and computational methods.

Nicole Yangمشاهده پروفایل

استادیار مهمان

• Machine Learning
• Financial Mathematics
• Stochastic Processes
• +۳ مورد دیگر

Nicole Yang is a Visiting Assistant Professor at Emory University. She holds a PhD from 2021, though the institution is not explicitly stated in the provided text. Her research focuses on interdisciplinary areas including machine learning, financial mathematics, stochastic processes, computer vision, and optimal transport. She explores topics such as fairness in AI, dynamical systems, volatility modeling, and generative modeling with applications in finance and data science. Her work bridges theoretical foundations (e.g., Schrödinger bridges, PDEs) with practical challenges like time series imputation and facial recognition bias mitigation. Recent publications emphasize statistical accuracy in dynamical systems and relative arbitrage strategies in complex markets. She maintains an active research agenda in high-dimensional problems, such as image generation via score-based diffusion models and constrained generative modeling. No scientific awards or grants are explicitly listed in the text. Her advising record is not provided, and no specific lab affiliations are mentioned. Her email is listed as yang@pstat.ucsb.edu, suggesting possible academic ties to the University of California, Santa Barbara, though her current faculty role is at Emory University.

Tomasz Tkoczمشاهده پروفایل

دانشیار

• Analysis
• Probability
• Convex Geometry
• +۵ مورد دیگر

Tomasz Tkocz is an Associate Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. He holds a Ph.D. from the University of Warwick and has held postdoctoral positions at Princeton University and the Mathematical Sciences Research Institute (MSRI). His research focuses on analysis, probability, and convex geometry, with emphasis on high-dimensional phenomena, geometric inequalities, and information theory. Tkocz has taught courses ranging from undergraduate calculus to graduate-level topics in complex analysis, functional analysis, and probability. Notable contributions include work on Gaussian inequalities, convex body sections, and entropy-related problems. Education: Ph.D. in Mathematical Sciences, University of Warwick Postdoctoral Fellowships: Princeton University, MSRI Research Interests: Analysis of high-dimensional geometric structures Probabilistic methods in convex geometry Entropy and concentration inequalities Random matrices and log-concave distributions Recent Publications Trends: Tkocz’s recent work explores geometric stability phenomena (e.g., simplex and polydisc slicing), entropy interpretations of anti-concentration, and sharp inequalities for random variables. His 2025 papers extend tail bounds for uniform sums and reverse Hölder inequalities, reflecting ongoing contributions to probabilistic and geometric analysis. Teaching & Outreach: Tkocz has instructed over a decade of courses at CMU, including Complex Analysis, Functional Analysis, and Real Analysis. His materials emphasize rigorous problem-solving and applications of abstract mathematical concepts.

Jun Xieمشاهده پروفایل

استاد

• Causal Machine Learning
• Causal AI
• Bayesian Statistics
• +۷ مورد دیگر

Jun Xie is a Professor of Statistics and Graduate Chair in the Department of Statistics at Purdue University's College of Science. His research bridges causal machine learning, bioinformatics, and computational statistics with applications in genomics, proteomics, and precision medicine. Education: B.S. in Probability and Statistics, Peking University, 1994 M.S. in Probability and Statistics, Peking University, 1997 Ph.D. in Statistics, University of California, Los Angeles, 2000 Xie's research centers on causal machine learning and causal AI, specifically addressing causal inference from observational data when perfect interventions are absent. His group develops methods to integrate causal inference with machine learning for treatment effectiveness evaluation and precision medicine. He maintains significant contributions in bioinformatics, including Bayesian methods for genomics, proteomics, and statistical genetics, with applications in cotton fiber development and biomedical research. His recent publications (2022-2025) reveal a dominant focus on causal machine learning, featuring breakthroughs in causal representation learning and heterogeneous treatment effect estimation. Simultaneously, his bioinformatics work demonstrates strong continuity in multi-omics integration for plant science and biomedical applications, particularly in cotton development and vocal fold physiology. Scientific Awards: College of Science Outstanding Service Award, 2016 College of Science Team Award, 2014 Excellence in Research / Seed for Success, 2014 Excellence in Research / Seed for Success, 2012 College of Science Graduate Student Mentoring Award Xie has mentored 11 PhD graduates including Donglai Chen (2019) and Pengcheng Yang (2025), with 4 current PhD students. His NIH and NIFA-funded research supports work on causal inference methods and bioinformatics applications, particularly in agricultural and biomedical contexts. Xie leads an active research group focused on causal machine learning, developing methods for real-world data analysis where perfect interventions are absent. The group explores synergies between causal inference, machine learning, and AI, with recent emphasis on causal generative AI for precision medicine and treatment evaluation.

David Källbergمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Bioinformatics
• +۷ مورد دیگر

David Källberg is an Associate Professor at the School of Business, Economics and Statistics (USBE), Umeå University , specializing in Statistics . His research focuses on statistical methods for high-dimensional data analysis, entropy estimation, and mixture modeling. Research Interests: David's work bridges statistical theory and practical applications in genomics and machine learning. Key areas include: Entropy balancing for causal effect estimation Feature selection in high-dimensional clustering (e.g., RNA-seq data) Mixture models for biomedical applications Nonparametric methods for entropy-type functionals Image matching algorithms in large databases Integration of epigenetic and clinicopathological data Collaborations: He collaborates with researchers like Patrik Rydén, Linda Vidman, and Ingeborg Waernbaum, contributing to journals such as Frontiers in Genetics , PLOS ONE , and Lecture Notes in Computer Science . He is also a member of the Stat4Reg research group.

Marius Krollمشاهده پروفایل

پژوهشگر

• Nonparametric Statistics
• U and V Statistics
• Bootstrap Methods
• +۳ مورد دیگر

Marius Kroll is a Researcher at Ruhr University Bochum's Faculty of Mathematics within the Institute of Statistics, working as part of Professor Holger Dette's research group in stochastics. His position is based in Building IB 2/89 at Universitätsstraße 150 in Bochum. His educational background includes: Doctoral Thesis in Mathematics, Ruhr-Universität Bochum (2023) Master Thesis in Mathematics, Ruhr-Universität Bochum (2019) Bachelor Thesis in Mathematics, Ruhr-Universität Bochum (2017) Kroll specializes in nonparametric statistics with expertise in U and V statistics , bootstrap methodology , and analysis of short-range dependent processes . His research develops theoretical foundations for measuring statistical dependence, particularly through distance covariance and Chatterjee's rank correlation. He investigates asymptotic properties of these measures under various dependence structures and develops practical inference procedures. His publication record shows a focused trajectory examining the theoretical properties of dependence measures, with recent work extending to infinite dimensional data and establishing connections between different forms of asymptotic behavior. Kroll has published in top statistics journals including Biometrika and the Journal of Theoretical Probability, with several additional papers currently in preparation. His major recognition includes: Ruth Massenberg award for the best mathematical dissertation 2023 Kroll actively participates in the academic community through invited talks at major conferences including the Bernoulli-IMS World Congress in Probability and Statistics and German Probability and Statistics Days. He serves on the Executive Board of the RUB Research School (since October 2023) and the Early Career Researchers Board of the Research Academy Ruhr (since June 2025), demonstrating leadership in supporting early career researchers at his institution.

Melanie Birkeمشاهده پروفایل

استاد

• Nonparametric Statistics
• Functional Data Analysis
• Inverse Problems
• +۳ مورد دیگر

Prof. Dr. Melanie Birke is a faculty member at the University of Bayreuth , holding the Professorship for Mathematical Statistics within the Faculty of Mathematics, Physics and Computer Science . Her research spans multiple areas of statistics, including nonparametric methods, functional data analysis, inverse problems, goodness-of-fit tests, and random matrices. Women's Representative of the Mathematical Institute DAV Correspondent for actuarial training exemptions Research Interests are focused on nonparametric statistics, functional data, and inverse problems. She develops asymptotic theory for estimation and testing procedures, particularly in high-dimensional or infinite-dimensional spaces, with applications to real-world issues like image distortion and data collection errors. Her work also includes constructing goodness-of-fit tests for regression models and functional data, ensuring robust statistical analysis. Publications highlight her contributions to quantile regression, symmetry testing in inverse problems, and shape-constrained density estimation. A recurring theme involves improving statistical consistency by addressing model misspecification through advanced nonparametric techniques. Statistical Consulting is offered to both internal and external stakeholders, emphasizing methodological guidance during experimental design to prevent data inconsistencies. She also supervises Bachelor's and Master's theses in nonparametric statistics and financial mathematics.

André Schlichtingمشاهده پروفایل

استاد

• Stochastic Processes
• Partial Differential Equations
• Optimal Transport
• +۴ مورد دیگر

André Schlichting is a full professor at the University of Ulm, leading the Institute of Applied Analysis. His research focuses on stochastic processes, partial differential equations (PDEs), and optimal transport, with a particular emphasis on gradient flows, nonlocal dynamics, and mathematical physics. He investigates systems such as stochastic particle models, McKean-Vlasov equations, and metric graphs, often exploring their long-time behavior and discretization techniques. Collaborations include work on diffusive transport distances, porous medium equations, and manifold analysis. His recent contributions address topics like convergence of particle systems to continuous models, gradient flows on graphs with reservoirs, and stationary solutions on high-dimensional spheres. His work bridges theoretical analysis with applications in statistical mechanics and numerical methods. Education and affiliations are not explicitly detailed in the provided texts, but his current role highlights academic leadership in applied analysis. Key collaborations involve researchers such as Daniel Matthes, Eva-Maria Rott, and Jan-Frederik Pietschmann. His research group at the University of Ulm is expanding, focusing on advanced analytical techniques and their implications in interdisciplinary fields.

Dongbin Xiuمشاهده پروفایل

استاد

• Stochastic Computations
• Uncertainty Quantification
• Numerical Methods
• +۳ مورد دیگر

Professor Dongbin Xiu is a faculty member at The Ohio State University's Department of Mathematics, holding the position of Ohio Eminent Scholar. Previously, he has served at institutions such as the University of Utah and Purdue University. His research focuses on stochastic computations, uncertainty quantification, high-order numerical methods, and data assimilation. Dr. Xiu earned his Ph.D. in Applied Mathematics from Brown University, followed by M.S. and B.S. degrees in Mechanical Engineering and Mechanics. His work is supported by grants from NSF, AFOSR, DOE, and DARPA. He has authored a notable book on numerical methods for stochastic computations and holds several awards, including the NSF CAREER Award and Purdue University's Teaching for Tomorrow Award. His publications emphasize innovative approaches in uncertainty quantification, including stochastic collocation methods, polynomial chaos, and machine learning integration. He serves on editorial boards of journals such as the SIAM Journal on Scientific Computing and the International Journal for Uncertainty Quantification.

• Statistical Modeling
• Robust Statistics
• Graphical Models
• +۴ مورد دیگر

Simona Pacillo is a Researcher in Statistics at the University of Sannio, affiliated with the Department of Law, Economics, Management and Quantitative Methods. Her office is located in the De Simone building, and she can be contacted via phone or institutional email. Research Focus: Dr. Pacillo specializes in developing robust statistical methodologies with particular expertise in: Flexible probability models including Skew Normal and Skew t distributions Graphical model theory and conditional independence testing Robust statistical procedures resistant to data contamination Algorithms for Generalized Linear Models with convex penalties Model selection techniques in high-dimensional settings Publication Trends: Her recent scholarly work demonstrates consistent focus on robust statistical methodologies, with publications spanning robust circle fitting algorithms, graphical model selection under non-normal distributions, and contamination-resistant statistical procedures. Her research frequently addresses challenges in model robustness and distributional flexibility.

Alexander Van Werdeمشاهده پروفایل

پژوهشگر

• Random Matrix Theory
• Spectral Algorithms
• Stochastic Processes
• +۵ مورد دیگر

Alexander Van Werde is a doctoral researcher at Eindhoven University of Technology, affiliated with the Department of Mathematics and Computer Science. He is supervised by Jaron Sanders and recently defended his PhD thesis titled Latent structures in Markovian processes and associated random matrices . He will transition to a postdoctoral researcher role at the Cluster of Excellence Mathematics Münster. Education: Current PhD candidate at TU/e Research interests span random matrix theory , Markovian processes , algebraic geometry , and applications of spectral algorithms . His work explores spectral characterization of networks, latent structures in stochastic processes, and intersections with algebraic combinatorics. Additionally, he contributes to theoretical computer science through Lean4-based proof formalization . Recent articles focus on dependent random matrices , semipermeable barriers in Brownian motion , and algebraic properties of Bernstein-Sato ideals . These works emphasize asymptotic behavior , leading-order term analysis , and spectral concentration phenomena . Scientific awards include: 2023 Best Poster Award for universality-based concentration in dependent random matrices Projects include OCENW.KLEIN.324 on Markov chain clustering (2021–2025). He also engages in teaching (Probability and Stochastics 1) and public engagement through blogs and art creation.

Andrea Cerioliمشاهده پروفایل

استاد

• Robust Statistical Methodologies
• Multivariate Data Analysis
• Outlier Detection
• +۳ مورد دیگر

Andrea Cerioli is a full Professor of Statistics at the Department of Economics and Management, University of Parma, Italy, since 2002. He specializes in robust statistical methodologies, including the Forward Search approach, multivariate outlier detection, Benford’s Law, and tempered stable distributions, with applications to economic data and international trade fraud detection. Ph.D. in Statistics, University of Padua (1992) Editor-in-Chief of Statistical Methods and Applications (2011-2015) Current Editor of Advances in Data Analysis and Classification President of CLADAG (2009-2011) His research spans robust classification, clustering, and spatial data analysis. He has co-authored over 100 peer-reviewed works, including two Springer books, and has served on numerous conference committees. Recent work focuses on Benford’s Law for fraud detection and tempered stable distributions for modeling heavy-tailed data. He teaches courses like Data Science for Marketing and Statistics for Management at the University of Parma, contributing to graduate programs in trade and consumer marketing. His editorial and academic leadership roles highlight his influence in statistical methodology development.