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• Statistical Network Analysis
• Non-parametric Statistics
• Causal Inference
• +7 more

Dr. Alexander Fuchs-Kreiß is a Junior Professor for Statistics at the Institute of Mathematics, Faculty of Mathematics and Computer Science at Leipzig University. He joined the university in April 2022 as a member of the Stochastics research group. He is also a scientific member of the International Max Planck Research School for Mathematics in the Sciences and affiliated with the HKMetrics Network. His research focuses on the statistical analysis of network data, particularly semi- and non-parametric methods for dynamic interaction networks. He has developed methodologies for relational event models using counting processes like Hawkes processes, and has contributed to causal inference, high-dimensional statistics, and quantile regression. His work often bridges multiple statistical areas, creating innovative approaches to complex data structures. His recent publications show a strong trend toward developing statistical methods for network data with applications in economics, epidemiology, and social sciences. His research combines theoretical developments with practical implementations, as evidenced by the R packages accompanying his publications. As an educator, he teaches Mathematical Statistics, Statistical Network Analysis, and Causal Inference at Leipzig University. He supervises PhD students working on network models and advises diploma theses in mathematics and business mathematics. Dr. Fuchs-Kreiß maintains active research collaborations with institutions including Heidelberg University, UC Davis, KU Leuven, and LSE. He co-organizes academic seminars and participates in international conferences on statistical learning and probability.

Yichi ZhangView profile

Assistant Professor

• Causal Mechanism and Causal Learning
• High-dimensional Models
• Large-scale Computations
• +2 more

Yichi Zhang is an Assistant Professor in the Department of Statistics at Indiana University Bloomington. Previously, he was a Postdoctoral Scholar at Duke University with dual appointments in the Fuqua School of Business and Department of Biostatistics & Bioinformatics. His academic credentials include a Ph.D. in Statistics from North Carolina State University (2023) and a B.S. in Mathematics and Applied Mathematics from Sichuan University (2018). Professor Zhang's research centers on methodological innovation in statistical learning, with primary focus areas: Causal Mechanism and Causal Learning High-dimensional Models Large-scale Computations Randomized Algorithms Uncertainty Quantification His publication trajectory (2023-2025) reveals rapid advancement in theoretical statistics and machine learning, with increasing contributions to causal inference frameworks and computational methodologies. Recent work demonstrates particular strength in adapting statistical theory to AI challenges, including large language model evaluation and network analysis. His scholarly recognition includes: ASA Nonparametric Statistics Student Paper Award Finalist (2023) IMS Hannan Graduate Student Travel Award (2021) ENAR Distinguished Student Paper Award (2021) Mu Sigma Rho National Statistics Honor Society (2018) As an early-career faculty member, Professor Zhang is establishing his research program while teaching core statistics courses. His active publication pipeline suggests robust research momentum, though specific grant details aren't provided. Prospective students interested in theoretical-computational statistics should contact him directly regarding advising opportunities and current projects.

Michael W. TrossetView profile

Professor

• Computational Statistics
• Statistical Learning
• Design and Analysis of Computer Experiments
• +6 more

Michael W. Trosset is a Professor of Statistics and Director of Graduate Studies at Indiana University Bloomington's Department of Statistics. With a PhD in Statistics from UC Berkeley (1983) and a BA in Mathematics from Rice University (1978), he brings over 40 years of academic experience to his role. His career includes faculty positions at the University of Arizona (14 years) and the College of William & Mary (8 years) before joining Indiana University. Professor Trosset's educational background includes: B.A. in Mathematics and Mathematical Sciences, Rice University, 1978 Ph.D. in Statistics, University of California Berkeley, 1983 His research focuses on computational statistics and statistical learning, with particular expertise in multidimensional scaling, manifold learning, and high-dimensional data analysis. He investigates how proximity data can be represented in Euclidean space and how dimension reduction techniques can inform subsequent statistical inference. His work spans theoretical foundations and practical applications in network analysis, particularly random dot product graphs. Professor Trosset's publication record shows consistent contributions to statistical methodology, with recent work emphasizing the theoretical underpinnings of manifold learning for statistical inference. His research bridges computational mathematics, statistical theory, and practical data analysis applications, particularly in high-dimensional settings where traditional multivariate methods face challenges. As an educator, Professor Trosset has developed a distinctive approach that emphasizes understanding statistical principles rather than treating methods as black boxes. He is the author of the textbook An Introduction to Statistical Inference and Its Applications with R (2009) and teaches courses ranging from introductory statistics to advanced topics in statistical computing and high-dimensional data analysis. His teaching philosophy centers on helping students understand why statistical methods work, with emphasis on mathematical notation and computational implementation using R. He encourages students to move beyond superficial coverage of many topics to develop deeper understanding of fewer methods. His courses typically include custom R functions to illustrate statistical concepts and algorithms. Professor Trosset maintains an active research collaboration with Carey Priebe at Johns Hopkins University, co-teaching the course 'Manifold Learning for Subsequent Inference' and publishing jointly on topics related to statistical inference on network data using dimension reduction techniques.

Huibin ZhouView profile

Professor

• Statistics
• Data Science
• High-dimensional Statistics
• +5 more

Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University, where he has held faculty positions since 2004. He served as Chair of the Department of Statistics and Data Science (2017-2021) and earlier as Chair of the Department of Statistics (2012-2017). Professor Zhou earned his Ph.D. from Cornell University in 2004, progressing through academic ranks from Assistant Professor to full Professor at Yale. His research spans high-dimensional statistics, nonparametric estimation, machine learning, and statistical decision theory. Key focus areas include minimax optimality in estimation, Bayesian methods, network analysis, and functional data. Zhou's work frequently bridges theoretical foundations with applications in social networks, signal processing, and computational biology. Analysis of his publications reveals consistent emphasis on theoretical guarantees for statistical methods, with recent work exploring spectral clustering, Gaussian mixtures, variational inference, and privacy-preserving estimation. His research demonstrates deep engagement with minimax optimality, high-dimensional challenges, and computational-statistical tradeoffs. Honors & Awards: Henry Ford II Professorship at Yale University Professor Zhou has taught graduate and undergraduate courses including Probability Theory, Statistical Decision Theory, Nonparametric Estimation, Data Mining, and Functional Data Analysis. He organized several NSF workshops including the 2012 Workshop on High-Dimensional Data and the 2015 Workshop on Empirical Processes.