جستجوی استاد راهنمای دانشگاهی

Christian Robertمشاهده پروفایل

استاد

• Bayesian Statistics
• Computational Statistics
• Latent Variable Models
• +۵ مورد دیگر

Christian Robert is a Professor at Université Paris-Dauphine (France) and holds a part-time Professorship at the University of Warwick's Department of Statistics. He is a Senior Member of the Institut Universitaire de France (IUF) and a member of the Statistics Laboratory at CREST (Paris-Saclay). His roles include editorial leadership as former Editor of the Journal of the Royal Statistical Society (Series B) and current Deputy-Editor for Biometrika. He previously served as President of the International Society for Bayesian Analysis (ISBA) in 2008. His research focuses on Bayesian statistics, computational methods, and latent variable models. Key areas include decision theory, model choice, Monte Carlo techniques (e.g., MCMC, ABC), and hidden Markov models. He explores foundational aspects of Bayesian methodology and approximate inference. Awards: Fellow of the Institute of Mathematical Statistics (IMS) Fellow of the International Society for Bayesian Analysis (ISBA) Fellow of the American Statistical Association (ASA) Senior Member of the Institut Universitaire de France (IUF) His professional contributions extend to academic service, including editorial roles and leadership in statistical societies. He collaborates with CREST researchers and actively engages in advancing Bayesian methodologies through publications and mentorship.

• Machine Learning
• Kernel Methods
• Gaussian Processes
• +۴ مورد دیگر

KANAGAWA Motonobu is an Assistant Professor in the Data Science department at EURECOM . His research focuses on kernel methods, Gaussian processes, and their applications in machine learning, numerical computation, and enhancing computer simulations. Research Interests: Kernel methods and Gaussian processes in machine learning Machine learning for computer simulation enhancement Applications to real-world simulation-based problems Publications highlight contributions to Bayesian quadrature, kernel-based integration, and intractable likelihood estimation. He serves on program committees for top conferences (NeurIPS, ICML, AISTATS) and reviews for journals like JMLR and TPAMI. No awards listed. Advising/grants sections are not detailed here. Active in computational statistics and simulation-driven machine learning.

Daniele Duranteمشاهده پروفایل

دانشیار

• Network Science
• Bayesian Methods and Computation
• Categorical Data
• +۵ مورد دیگر

Roles and Affiliations: Associate Professor of Statistics at Bocconi University's Department of Decision Sciences. Research Affiliate at Bocconi Institute for Data Science and Analytics (BIDSA), DONDENA Centre, and Laboratory for Coronavirus Crisis Research. Former Post-Doctoral Fellow at University of Padova and Visiting Scholar at Duke University. Education: Ph.D. (2016) and M.Sc. (2012) in Statistics from University of Padova. Research Interests: Bayesian methodology, network science, categorical data, latent variable models, criminology, and demography. Focus on statistical models for complex data, including criminal networks and cause-of-death dependencies. Grants and Awards: ERC Starting Grant (NEMESIS, 2024–2029), PRIN-MUR Grant (CARONTE), COPSS Emerging Leader Award, Leonardo da Vinci Medal, Mitchell Prize (twice), Laplace Prize. Editor for Biometrika , Journal of Computational and Graphical Statistics , and Journal of Multivariate Analysis . Teaching and Service: Courses on Machine Learning, Data Science, and Statistical Methods. Organized Stats under the Stars hackathons, Bocconi Data Science Challenge platform. Served on committees for ISBA, ASA, and others. Labs/Teams: Leads NEMESIS and CARONTE projects. Collaborates with interdisciplinary teams in quantitative criminology and demography.

Sanjay Chaudhuriمشاهده پروفایل

دانشیار

• Empirical Likelihood
• Analysis of Complex Survey Data
• Statistical Data Integration
• +۷ مورد دیگر

Sanjay Chaudhuri is an Associate Professor in the Department of Statistics at the University of Nebraska-Lincoln (UNL). He previously held positions at the National University of Singapore (2005–2023) and earned his Ph.D. from the University of Washington (2005), under Professors Michael D. Perlman and Thomas S. Richardson. His research focuses on empirical likelihood, Bayesian methods, high-dimensional data analysis, and applications in demography, network data, and machine learning. Education: B.Stat and M.Stat from the Indian Statistical Institute (1998–2000), Ph.D. in Statistics from the University of Washington (2005). Research Interests: Empirical likelihood, statistical data integration, Bayesian empirical likelihood, graphical Markov models, covariance estimation, causality, approximate Bayesian computation (ABC), and applications in natural, engineering, and marketing sciences. Teaching: Advanced Topics in Applied Statistics, Statistical Models, Applied Regression Analysis, Design of Experiments, and courses on graphical models, data science, and linear models. Software Contributions: Developer of R packages glmc (combining sample/population data), ES (edge selection), elhmc (Hamiltonian Monte Carlo sampling), and Anhysnuc (analyzing nucleic acid thermal curves). Grants & Collaborations: Active in interdisciplinary projects involving statistical methods for complex data, including pandemic dynamics, thermal curve analysis, and environmental statistics. Labs/Teams: Leads the development of Anhysnuc , a web-based tool for analyzing irreversible thermal curves from nucleic acid hybridization experiments.

Thijs Janzenمشاهده پروفایل

پژوهشگر

• Phylogenetics
• Population Genetics
• Computational Biology
• +۳ مورد دیگر

Dr. Thijs Janzen is a Researcher and Scientific Programmer at the University of Groningen's Faculty of Science and Engineering, affiliated with the Theoretical Research in Evolutionary Life Sciences department. He holds a PhD in Biology from the University of Groningen (2015) and has extensive postdoctoral experience at institutions including the Max Planck Institute for Evolutionary Biology and Carl von Ossietzky University Oldenburg. Education: PhD in Biology, University of Groningen (2010–2014) MSc in Biology, University of Groningen (2006–2009) BSc in Biology, University of Groningen (2003–2006) Research Interests: Phylogenetics and phylogenetic reconstruction Population genetics and genomic admixture Individual-based computational modeling Software development in R, C++, and Python Support for MSc/PhD students in programming and modeling Research Trends: His work spans theoretical evolutionary biology, computational genomics, and phylogenetic analysis. Recent studies focus on hybridization dynamics, viral oncolytic therapy modeling, and tools for phylogenetic statistics (e.g., the 'treestats' R package). His contributions bridge ecological theory with genomic data, emphasizing quantitative methods in evolutionary processes. Advising & Grants: Collaborated on the MARM project (2018–2023) on adaptive responses in evolution, contributing technical expertise to PhD student projects. Maintains active roles in computational support and course development for biologists. Labs/Teams: Core member of the Theoretical Research in Evolutionary Life Sciences group, developing open-source tools (e.g., GenomeAdmixR, nLTT) and collaborating globally on biodiversity and evolutionary questions.

Shu Xieمشاهده پروفایل

پژوهشگر

• Island Biogeography
• Evolutionary Ecology
• Phylogenetics
• +۵ مورد دیگر

Shu Xie is a Guest Researcher at the Faculty of Science and Engineering , University of Groningen, affiliated with the Etienne group specializing in Theoretical and Evolutionary Community Ecology. Their ongoing PhD project (2018–present) at Naturalis Biodiversity Center (Netherlands) focuses on "The effect of traits on insular diversification" , co-supervised by L. Valente. Shu Xie's research lies at the intersection of Island Biogeography Phylogenetics Speciation Modeling Approximate Bayesian Computation (ABC) DAISIE Framework Development contributing to UN Sustainable Development Goals related to biodiversity conservation. Recent work emphasizes Computational methods for trait-dependent diversification ABC parameter inference in island phylogenetic models DAISIE software for R Evaluating colonization-extinction dynamics Identifying summary statistics for Bayesian model validation with publications in journals like Evolution and Geoderma .

Juliette de Meauxمشاهده پروفایل

استاد

• Plant Evolutionary Biology
• Molecular Ecology
• Population Genetics
• +۷ مورد دیگر

Prof. Dr. Juliette de Meaux is a Principal Investigator at the Institute for Plant Sciences within the Cologne Biocenter at the University of Cologne. She has been affiliated with CEPLAS (Cluster of Excellence on Plant Sciences) since 2014 and is also a member of the International Max Planck Research School (IMPRS). Her research group investigates molecular mechanisms of plant adaptation and evolution. Institution: University of Cologne Department: Institute for Plant Sciences Affiliations: CEPLAS since 2014, International Max Planck Research School Professor de Meaux's research focuses on reconstructing the recent history of adaptive molecular variation in plant systems, particularly using Arabidopsis thaliana and its close relatives (A. lyrata and A. halleri) as well as Arabis nemorensis. Her work spans several key research areas including plant cis-regulatory variation and polygenic adaptation, multivariate evolution of plant traits, Arabis nemorensis genomics, and ecology of urban Arabidopsis thaliana populations. She employs approaches that combine experimental measures of functional variation with molecular and population-level analysis of selection signatures. Analysis of her recent publications reveals a strong focus on evolutionary genomics, with particular attention to polygenic adaptation mechanisms, drought tolerance strategies, urban ecology of plant populations, and conservation genetics of endangered species. Her work often integrates computational approaches like Approximate Bayesian Computation with experimental molecular biology techniques. Long-term research on plant adaptation mechanisms Investigation of polygenic selection in natural populations Integration of ecological and molecular approaches Focus on urban environments and climate change impacts Professor de Meaux actively supervises a diverse research group including postdoctoral fellows, PhD students, and technical staff. She has mentored numerous students who have gone on to postdoctoral positions and research careers. She has secured significant research funding that has supported her group's work since at least 2013-2018 (as evidenced by archive references) and continuing through the present. Her laboratory maintains several research platforms including a Plant Metabolism and Metabolomics Facility and an Imaging Platform, which support the group's work on plant performance optimization, plant microbiota networks, and theoretical plant biology.

Simon Tavaréمشاهده پروفایل

استاد

• Cancer Genomics
• Evolutionary Cancer Biology
• Statistical Bioinformatics
• +۸ مورد دیگر

Simon Tavaré is a Professor of Cancer Research (Bioinformatics) at the University of Cambridge’s Department of Oncology and a Professor in the Department of Applied Mathematics and Theoretical Physics (DAMTP). He is also the former Director of the Cancer Research UK Cambridge Institute (2013–2018), a Senior Group Leader there, and a Professorial Fellow at Christ’s College, Cambridge. His research focuses on cancer genomics, evolutionary approaches to cancer, and statistical bioinformatics for sequencing data. He integrates methods from computational biology, population genetics, and stochastic computation to address molecular biology questions. His career includes roles at the University of Southern California (1999–2006) and the University of Utah (1984–1989). He has been recognized with prestigious awards, including Fellowship of the Royal Society (2011) and election to the U.S. National Academy of Sciences as a Foreign Associate (2018). His work spans diverse areas such as evolutionary biology, palaeontology, and probabilistic combinatorics. Education: Early career details not explicitly listed, but academic roles began in 1978 as an instructor at the University of Utah. Research interests include applying statistical methods to cancer heterogeneity, mutational signatures analysis, and evolutionary dynamics in tumors. His publications highlight contributions to genomic data analysis tools like BayesPeak and foundational work in combinatorial probability. Awards: FMedSci (2009), FRS (2011), EMBO Membership (2015), NAS Foreign Associate (2018). He has led initiatives at the Cancer Research UK Cambridge Institute and contributed to international collaborations, including the OCCAMS Consortium for esophageal cancer research. His advisory roles include serving as President of the London Mathematical Society (2015–2017).

• Bayesian Computational Statistics
• Markov Chain Monte Carlo (MCMC)
• Approximate Bayesian Computation (ABC)
• +۴ مورد دیگر

Robin Ryder is a Senior Lecturer in Statistics at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. He previously held positions as Associate Professor (maître de conférences HDR) at Université Paris-Dauphine and an associate member at École Normale Supérieure, Paris. His research focuses on Bayesian computational statistics (MCMC and ABC methods) and stochastic modeling in the Humanities, particularly Linguistics and Social Sciences. Education: DPhil in Statistics , University of Oxford Habilitation à Diriger les Recherches , Paris Dauphine University Research Interests: Dr. Ryder specializes in advancing computational methods for statistical inference, including Markov Chain Monte Carlo (MCMC) and Approximate Bayesian Computation (ABC). His applied work bridges statistics with the Humanities, addressing questions in linguistic phylogenetics, cultural evolution, and social sciences. Notable areas include modeling language evolution, analyzing sign language histories, and investigating functional load dynamics in linguistic systems. Recent Contributions: His work spans theoretical advancements (e.g., saddlepoint Monte Carlo for ecological inference) and applied studies (e.g., phylogenetic models for Kra-Dai languages and weaving technologies). He frequently collaborates on interdisciplinary projects, such as computational analyses of sign language histories and formal assessments of primate communication systems. Awards & Recognition: While no explicit awards are listed, his contributions have been featured in prominent outlets like Science and French popular science magazine Pour la Science . Grants & Advising: Details on grants or advisees are not explicitly mentioned, though his involvement in editorial boards (e.g., Bayesian Analysis ) underscores his leadership in statistical methodology. He actively participates in conferences and workshops, emphasizing sustainability by advocating for reduced academic travel.

Iva Popovicمشاهده پروفایل

پژوهشگر ارشد

• Marine Biology
• Evolutionary Genetics
• Conservation Biology
• +۳ مورد دیگر

Dr Iva Popovic is an Adjunct Research Fellow in the School of the Environment at The University of Queensland. Her research focuses on evolutionary biology, marine conservation, and genomics, particularly addressing challenges in coral reef ecosystems, invasive species dynamics, and climate adaptation. She leads projects on genetic diversity in marine organisms and the application of genomics to conservation strategies. PhD in Biological Sciences from The University of Queensland (2019) Her work spans coral speciation, hybridization, and the genetic basis of thermal tolerance in marine species. She collaborates on tools like Reef Adapt to inform climate-smart marine management. Over 24 peer-reviewed articles and 4 datasets highlight her contributions to marine genomics and biodiversity research. Key research interests include: coral predator genetics, cryptic species discovery, and genomic tools for invasive species control. She emphasizes practical applications of evolutionary insights for conservation.

• Data Science
• Computational Statistics
• Bayesian Models
• +۵ مورد دیگر

Antonietta Mira is a Full Professor of Statistics at the University of Italian Switzerland (USI), where she also serves as the founder and director of the Data Science Lab. She holds a part-time professorship at Università dell'Insubria and has held visiting roles at institutions such as the University of Cambridge and Université Paris-Dauphine. Her academic leadership includes a Vice-Dean role in the Faculty of Economics (2013-2015). She earned a PhD in Computational Statistics from the University of Minnesota (1998), a Master's in Statistics (1996), and a Doctorate in Methodological Statistics from the University of Trento (1995). Her research focuses on Bayesian statistical models, computational statistics, and interdisciplinary applications in healthcare, finance, and social sciences. Mira is a Fellow of the International Society for Bayesian Analysis (ISBA) and the Istituto Lombardo Accademia di Scienze e Lettere. Her work has been recognized with awards for both research and teaching excellence. She leads Swiss National Science Foundation-funded projects and serves on editorial boards of journals like Bayesian Analysis and Statistica Sinica . Her publications span topics such as approximate Bayesian computation (ABC), spatio-temporal modeling of health events, and data science applications in public policy. Mira also engages in public outreach, including scientific exhibitions like Numbed by Numbers! and lectures on data-driven societal issues.

Ole Maneesoonthornمشاهده پروفایل

دانشیار

• Bayesian Inference
• Time Series Forecasting
• Volatility Modelling
• +۱ مورد دیگر

Ole Maneesoonthorn is an Associate Professor in the Department of Econometrics & Business Statistics at Monash University. He specializes in Bayesian inference, time series forecasting, volatility modeling, and financial econometrics. Currently accepting PhD students since 2012, his research contributes to UN Sustainable Development Goals through methodological advancements in statistical and financial modeling. He leads two major research projects: Property Price Index Development (University of Melbourne collaboration, 2022-2023) Loss-based Bayesian Prediction (2020-2025, collaborating with G. Martin, D. Frazier, and R. Hyndman) His work emphasizes practical Bayesian methods for economic and financial applications, with recent focus on copula models, hybrid variational inference, and optimal forecasting frameworks. Outputs include peer-reviewed articles in top journals like Journal of Business and Economic Statistics and International Journal of Forecasting .

Tetsuya Kajiمشاهده پروفایل

دانشیار

• Econometrics
• Statistics
• Machine Learning
• +۴ مورد دیگر

Tetsuya Kaji is an Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research spans econometrics, statistics, and machine learning , focusing on nonstandard econometric problems, empirical processes, and AI adaptation in economic applications. Education : Interdisciplinary PhD in Economics and Statistics from MIT; MA in Economics from the University of Tokyo. Recent work highlights adversarial inference for structural estimation, classification-based sampling (Metropolis-Hastings, ABC methods), and weak identification in semiparametric models . He also explores tail risk control and treatment effect heterogeneity .

Veronika Rockovaمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• High-dimensional Data Analysis
• +۳ مورد دیگر

Veronika Rockova is the Bruce Lindsay Professor of Econometrics and Statistics in the Wallman Society of Fellows at the University of Chicago Booth School of Business. She joined Booth after postdoctoral training at the Wharton School and has been internationally recognized for her work at the intersection of statistics and machine learning. Her research focuses on developing decision-centric statistical tools for large datasets, specializing in Bayesian computation Variable selection High-dimensional decision theory Hierarchical modeling Uncertainty quantification for generative AI Recent publications highlight trends in Bayesian CART mixing rates Generative posterior sampling Deep learning integration with Bayesian frameworks Tree-based bandit approaches for ABC Quantile methods for credible sets Scientific recognition includes COPSS President's Award (2024) COPSS Emerging Leader Award (2023) NSF CAREER Award (2020) She currently serves on editorial boards for Annals of Statistics Journal of the American Statistical Association Journal of the Royal Statistical Society (Series B) and mentors PhD students in econometrics and statistics.

Fabrizio Leisenمشاهده پروفایل

استاد

• Bayesian Inference
• Bayesian Nonparametric Methods
• Objective Bayesian Analysis
• +۳ مورد دیگر

Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.