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Frederick MatsenView profile

Professor

• Computational Biology
• Phylogenetics
• Evolutionary Immunology
• +5 more

Dr. Frederick "Erick" Matsen is a Professor in the Public Health Sciences Division at Fred Hutchinson Cancer Research Center, where he serves as Associate Program Head of the Herbold Computational Biology Program. He is also an Affiliate Associate Professor of Genome Sciences and Statistics at the University of Washington and a Howard Hughes Medical Institute Investigator. Dr. Matsen leads the Matsen Lab, which focuses on computational approaches to evolutionary biology and immunology. Dr. Matsen's research centers on computational biology, particularly developing algorithms to analyze biological data from DNA sequencing of viruses, immune cells, and complex environmental samples. His team works on phylogenetic methods, evolutionary tree construction, and software development for computational biology. Key research areas include Bayesian phylogenetics via optimization , phylodynamics with many sequences , antibody sequence analysis , and learning immune mechanisms from receptor sequences . His work bridges mathematics, computer science, and biological discovery, with applications to HIV, SARS-CoV-2, and other pathogens. The Matsen Lab has developed numerous influential resources including phyloseminar.org, B-T.CR, and phylobabble.org. Recent publications demonstrate a strong focus on machine learning applications in phylogenetics, antibody evolution analysis, and computational approaches to viral evolution. His team's work has significant implications for understanding immune responses to pathogens and developing better vaccines and treatments. Howard Hughes Medical Institute Investigator (2021-present) Howard Hughes Medical Institute-Simons Foundation Faculty Scholar Dr. Matsen maintains an active mentoring program, with an impressive track record of trainees securing tenure-track faculty positions. His lab fosters a collaborative environment where biologists learn analytical tools and computational scientists work directly with biological problems. Current projects include developing computational methods for outbreak control and analyzing antibody responses to SARS-CoV-2 using phage-immunoprecipitation sequencing. The Matsen Lab operates within Fred Hutch's collaborative ecosystem, including membership in the Immunotherapy Integrated Research Center, Pathogen-Associated Malignancies Integrated Research Center, and Translational Data Science Integrated Research Center. The lab has recently moved to the Lake Union Steam Plant facility, specifically designed to facilitate collaboration between clinical researchers, data scientists, and evolutionary biologists.

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
• +5 more

Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.

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.