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Nicolas JouvinView profile

Researcher

• Model based clustering
• Dimension reduction
• Variational inference
• +4 more

Nicolas Jouvin is an INRAE researcher at the MIA-Paris laboratory specializing in applied mathematics and statistics. He is based at AgroParisTech Saclay (Bureau E4.512, Bâtiment E, 22 place de l'agronomie, 91120 Palaiseau) and works within the MIA-Paris-Saclay research unit. His educational background includes a PhD in Applied Mathematics (2017-2020) from Université Paris 1 Panthéon-Sorbonne and an MVA degree (2016) from ENS Paris Saclay. His doctoral research focused on high-dimensional data and graph clustering with discrete latent variable models under the supervision of Pr. Pierre Latouche, Pr. Charles Bouveyron, and Dr. Alain Livartowski, with collaboration at Institut Curie. Jouvin's research centers on probabilistic models for unsupervised learning, particularly model-based clustering and dimension reduction techniques. His work incorporates variational inference, sparse regularisation, and high-dimensional statistics with applications to medical data analysis. He has developed methodologies for hierarchical clustering using discrete latent variable models and the integrated classification likelihood criterion. His publications demonstrate expertise in developing algorithms for clustering count data through multinomial PCA mixtures and discriminative Gaussian subspace clustering using Bayesian approaches. The Greed R package, which implements some of his methodological contributions, was developed in collaboration with Etienne Côme. Charged de cours for Master 2 Data Science (Evry) - Unsupervised learning Master 2 data science (Evry) - Introduction à Python Master 1 data science (Evry) - Analyse des données TD de M1 MAEF Python TD de L3 MIAGE Technique de calcul TD de L1 MIASHS

• High-dimensional inference
• Forecasting
• Semi-parametric Bayesian inference
• +4 more

Didier Nibbering is an Assistant Professor (Lecturer) in the Department of Econometrics and Business Statistics at Monash University, Faculty of Business and Economics, Melbourne, Australia. His research lies at the intersection of econometrics, statistics, and computational methods. Research Interests: His primary research areas include high-dimensional inference, forecasting, and semi-parametric Bayesian inference. He works on developing and applying advanced statistical models for complex economic and financial data, particularly in contexts involving large-dimensional datasets and latent variable structures. The recent publications indicate a strong focus on Bayesian computational techniques, state space models, variational inference, and forecasting applications in economics and environmental modeling. His work combines theoretical rigor with practical applications in policy-relevant domains such as carbon emissions forecasting. Scientific Contributions: Developed novel hybrid MCMC methods for high-dimensional latent variable models. Advanced variational approximations for state space models using importance sampling. Proposed asymmetric grouping methods for improved forecasting of carbon emissions. Contributed to a comprehensive modern review of Bayesian forecasting in economics and finance. Introduced high-dimensional extensions to multinomial logit models. Advising and Grants: While specific details about PhD students or grant funding are not provided in the text, his active publication record and collaborations suggest ongoing research supervision and external funding involvement typical for early-career faculty. He has collaborated with prominent researchers in econometrics such as Gael Martin, David Frazier, and Richard Paap. Labs and Teams: There is no explicit mention of lab affiliations or research teams, but his work appears to be part of the broader econometrics and statistics research group at Monash University, with international collaborations including Stanford University.

• Microeconometrics
• Probability Theory
• Mathematical Statistics
• +3 more

Elena Vladimirovna Kossova is an Associate Professor at the Department of Applied Economics within the Faculty of Economic Sciences at the National Research University Higher School of Economics (HSE). She has been with HSE since 1995, accumulating 30 years of scientific and teaching experience. Her academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University. Educational Background: 1995: Candidate of Physical and Mathematical Sciences, Lomonosov Moscow State University, specialty 01.01.05 "Probability Theory and Mathematical Statistics" 1995: Postgraduate study, Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics 1990: Specialty in Applied Mathematics, Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics, qualification "Mathematician" Elena Kossova's research focuses on Microeconometrics, Probability Theory, Mathematical Statistics, and Applied Econometrics. Her work particularly emphasizes microeconometrics of qualitative data, models with qualitative and limited dependent variables, and the application of advanced statistical methods to economic problems. She has developed expertise in handling complex data structures including non-random selection and endogeneity issues. Her teaching portfolio includes Probability Theory and Mathematical Statistics courses alongside specialized microeconometrics courses for both undergraduate and graduate students. Her publication record shows a consistent focus on applying econometric methods to analyze health economics, consumer behavior, and social policy issues in the Russian context. A significant portion of her recent work examines the relationship between alcohol consumption, health outcomes, and socioeconomic factors, as well as the impact of education on health. Her methodological contributions include developing and comparing various approaches to handling sample selection bias and endogeneity in microeconometric models. Scientific Awards and Recognitions: Diploma "Founder of the Higher School of Economics" (November 2022) Honorary title "Honorary Worker of Education of the Russian Federation" (November 2022) Medal "Recognition - 25 years of successful work" (August 2021) Honorary Badge of the 2nd Degree of the Higher School of Economics (December 2018) Medal "Recognition - 20 years of successful work" (January 2018) Certificate of Honor of the Ministry of Education and Science of the Russian Federation (November 2012) Multiple Academic Work Allowances (2012-2018) Publication Bonuses for international journal articles (2018-2022) Best Teacher awards (2012-2025) Professor Kossova serves as a scientific supervisor for the educational program specialization in "Economy and economic policy." She has supervised doctoral research, including Potanin B.S.'s 2020 dissertation on extending regression models with endogenous switching and non-random selection. She is actively involved in research projects, including as project leader for the Russian Science Foundation grant No. 21-18-00427 on "Development and application of econometric estimation methods for economic models considering endogeneity and non-random observation selection." Her work demonstrates strong collaboration with colleagues both within HSE and internationally. Professor Kossova maintains active participation in academic conferences, particularly the annual "Applied Econometrics" conference series at HSE and international econometrics events. Her consultation hours are held via Telegram chat for students taking her courses in TViS (Probability Theory and Mathematical Statistics) and Microeconometrics.

Jeffrey M. PerloffView profile

Professor

• Industrial Organization
• Agricultural Economics
• Labor Economics
• +3 more

Jeffrey M. Perloff is a Distinguished Professor in the Department of Agricultural and Resource Economics at the University of California, Berkeley. His research focuses on industrial organization, marketing, labor economics, trade, and econometrics. He holds a B.A. from the University of Chicago (1972) and a Ph.D. from MIT (1976). Previously, he was an assistant professor at the University of Pennsylvania. Education: B.A. in Economics, University of Chicago, 1972 Ph.D. in Economics, Massachusetts Institute of Technology, 1976 Research Interests: Industrial organization, agricultural economics, labor economics, trade policy, econometrics, and marketing strategies. His work spans theoretical and applied economics, with notable contributions to market power analysis, trade diversion, and agricultural labor dynamics. Publications: Over 30 years of peer-reviewed articles, including studies on double markups in markets, traffic violation deterrence, and WIC contract spillovers. His research often bridges theory and policy, with a focus on empirical methods like maximum entropy estimation. Awards: Fellow of the American Agricultural Economics Association (AAEA), recognizing his significant contributions to the field. Professional Roles: Editor of Industrial Relations and Journal of Industrial Organization Education ; associate editor of American Journal of Agricultural Economics . He has consulted for agencies like the FTC and USDA on antitrust and policy evaluation. Textbooks: Author of influential texts such as Modern Industrial Organization (with Dennis Carlton), Microeconomics: Theory and Applications with Calculus , and Managerial Economics and Strategy .

Andee KaplanView profile

Associate Professor

• Bayesian Statistics
• Computational Statistics
• Record Linkage
• +6 more

Andee Kaplan is an Associate Professor in the Department of Statistics at Colorado State University. She completed her Ph.D. in Statistics at Iowa State University under advisors Dan Nordman and Steve Vardeman, a postdoc at Duke University with Beka Steorts, and holds advanced degrees in Statistics (M.S.) and Mathematics (M.A.) from Iowa State and The University of Texas at Austin respectively. Ph.D. in Statistics, Iowa State University (2017) Postdoctoral Researcher, Duke University M.S. in Statistics, Iowa State University M.A. in Mathematics, The University of Texas at Austin B.S. in Mathematics with Computing Certificate, The University of Texas at Austin Her research bridges Statistics and Computing, focusing on Bayesian Statistics, Computational Statistics, Record Linkage/Entity Resolution, Markov chain Monte Carlo methods, Spatial Resampling, Statistical Machine Learning, Interactive Statistical Graphics, and Reproducible Research. Her methodological work emphasizes scalable algorithms for complex data structures in ecology, sports science, and public health. Her recent publications highlight trends in Bayesian entity resolution for streaming data, spatial modeling of riverscapes, time-varying epidemic models, and computational methods for LiDAR data analysis. Over 2014-2025, her work spans theoretical advancements in deep learning degeneracy, practical software design for statistics, and interactive tools for network detection.

• Econometrics
• Applied Econometrics
• Statistical Modeling
• +3 more

Shakeeb Khan is a Professor of Economics at Boston College. His primary affiliation is with the Department of Economics. He holds a Ph.D. from Princeton University. His research focuses on econometrics, particularly in areas such as panel data models, endogeneity, censored regression, and binary response models. He has contributed extensively to methodological advancements in econometric theory and applications. Education: Ph.D. in Economics from Princeton University. Research interests include: Nonparametric and semiparametric estimation techniques Endogeneity and identification in econometric models Applications of econometric methods to labor economics, policy evaluation, and development economics His work often addresses challenges in censored data, dynamic models, and factor structures. Recent research trends emphasize high-dimensional models and robust inference in nonlinear frameworks. Courses taught include advanced econometrics and econometric theory.

Svante EriksenView profile

Associate Professor

• Statistics
• Bayesian Networks
• Graphical Models
• +7 more

Svante Eriksen is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His research spans statistics, forensic genetics, and computational modeling, with a focus on Bayesian networks, graphical models, and statistical methods for forensic DNA analysis. He is actively involved in interdisciplinary research and software development for probabilistic genotyping and large-scale inference. Research Interests: Bayesian Networks and Graphical Models Statistical Methods in Forensic Genetics SNP and Y-STR Genotyping Data Mining and Knowledge Discovery Model Selection and Context-Specific Independence Software Development for Statistical Inference Recent Publication Trends (2024–2025): His recent work focuses on improving SNP genotyping accuracy using logistic regression models, developing efficient software (jti and sparta) for Bayesian network inference, and advancing forensic DNA analysis through dynamic SNP selection and probabilistic modeling of Y-STR databases. These contributions reflect a strong integration of statistical theory, computational efficiency, and real-world forensic applications. Scientific Contributions: Principal contributor to software packages for Bayesian network prediction. Developer of statistical models for forensic DNA data interpretation. Collaborator on projects involving digital learning analytics and student retention. Advising and Grants: While specific student names are not listed, the profile indicates involvement in PhD supervision (4 cases). He has participated in multiple externally funded research projects, including those supported by Novo Nordisk and Danish research councils, focusing on forensic DNA analysis, graphical models, and educational data mining. Research Groups and Collaborations: He is part of a strong research network in forensic genetics and statistical modeling at Aalborg University, collaborating with leading researchers such as N. Morling, M. M. Andersen, and T. Tvedebrink. His work is closely tied to the development and application of statistical software in both forensic and educational domains.

• Statistical Ecology
• Multiple Systems Estimation
• Illegal Wildlife Trade
• +3 more

Professor Rachel McCrea holds a Chair in Statistics at Lancaster University's School of Mathematical Sciences and is a core member of the Data Science Institute. She actively supervises PhD students in statistical methodology development for real-world ecological and conservation challenges, with current research focusing on statistical ecology, multiple systems estimation, and illegal wildlife trade applications. Her research program centers on: Advanced capture-recapture and state-space modeling for population dynamics Multiple systems estimation for hidden populations (wildlife trade/human trafficking) Integrated population modeling for conservation decision-making Bias correction in translocation management and extinction inference Bayesian methods for ecological time-series and demographic analysis Recent publications (2021-2025) reveal strong methodological innovation in ecological statistics, with key contributions to integrated population models, multi-system estimation frameworks, and conservation applications. Her work bridges theoretical statistics with urgent biodiversity challenges, particularly in extinction risk assessment and wildlife trade monitoring. Current research projects include 'New stochastic models to quantify illegal trade' (2023-2024), 'A transdisciplinary approach to inferring the end of political violence' (2022-2023), and 'Modelling removal and re-introduction data for improved conservation' (2022-2023). She supervises PhD student Lucas Howell through the STOR-i Centre for Doctoral Training. Professor McCrea collaborates extensively through Lancaster's Centre of Excellence in Environmental Data Science, Environmental and Ecological Statistics group, Social and Economic Statistics group, and STOR-i Centre for Doctoral Training, driving interdisciplinary approaches to ecological data science.

Julian Quevedo PütterView profile

Researcher

• Cognitive Psychology
• Memory Consolidation
• Retroactive Interference
• +3 more

Dr. Julian Quevedo Pütter is a researcher at the University of Mannheim , affiliated with the School of Social Sciences and the Department of Psychology . His work spans cognitive psychology, memory processes, statistical modeling, and interdisciplinary applications. University: University of Mannheim School: School of Social Sciences Department: Department of Psychology Email: julian.quevedo@psychology.uni-mannheim.de Research Interests include memory consolidation, retroactive interference, belief updating, and statistical modeling of cognitive processes. He also explores the intersection of religiosity and well-being, as well as the behavioral impacts of alcohol consumption on memory retention. Publication Trends reveal a focus on experimental validation of cognitive theories, methodological rigor in statistical analysis, and interdisciplinary collaborations. His recent work addresses memory dynamics, pharmacological effects on memory, and data reliability in social science research.

• Probability Theory
• Discrete Optimization
• Mathematical Statistics
• +2 more

Odysseas Kanavetas is an Assistant Professor in the Mathematical Institute at Leiden University's Faculty of Science, specializing in Probability Theory and discrete optimization. His research interests span Probability Theory, Discrete Optimization, Mathematical Statistics, Algorithm Design, and Decision Sciences. His work bridges theoretical mathematics with practical applications in social sciences and operations research. Dr. Kanavetas has published significant research on Ameso optimization, which provides a relaxation framework for discrete midpoint convexity with applications to problems like the knapsack problem. He has also conducted research on recidivism factors in Boston using statistical methods including multinomial logistic regression and chi-square analysis. His publications demonstrate expertise in both theoretical mathematical frameworks and their application to real-world decision problems. Dr. Kanavetas maintains an active research profile with publications in reputable journals including Discrete Applied Mathematics and the International Journal of Applied Decision Sciences.

Alexandra Harmon-ThreattView profile

Associate Professor

• Pollination Ecology
• Conservation Biology
• Biodiversity Conservation
• +3 more

Alexandra Harmon-Threatt is an Associate Professor in the Department of Entomology at the University of Illinois Urbana-Champaign . Her research focuses on pollination ecology, conservation, and restoration , particularly examining how plant-pollinator interactions and landscape factors influence pollinator diversity. She holds a PhD from the University of California, Berkeley (2011), and her work emphasizes bees as key pollinators. Her research interests include understanding the effects of neonicotinoids , forest management practices , and habitat fragmentation on bee communities. She has published extensively on topics such as pollinator conservation, pesticide impacts, and biodiversity in agricultural and natural ecosystems. Harmon-Threatt is affiliated with the College of Agricultural, Consumer and Environmental Sciences . Key awards include the Fulbright Scholar Award (2022) and ESA Early Career Fellow (2020) . Her work often integrates field studies, stable isotope analyses, and ecological modeling. She collaborates on projects exploring agroecology, prairie restoration, and the role of predators in pest control. Her office is located at 320 Morrill Hall, 505 S. Goodwin Ave, Urbana, IL 61801 . Recent research highlights include studies on the ecological impacts of neonicotinoids , forest management strategies , and hover fly migration .

Claartje VinkenburgView profile

Research Fellow

• Gender Diversity
• Ethnic Diversity
• Career Development Systems
• +4 more

Claartje Vinkenburg is a Visiting Fellow at Vrije Universiteit Amsterdam's School of Business and Economics (SBE) and the Amsterdam Business Research Institute (ABRI). Previously, she held roles as Assistant and Associate Professor at VU Amsterdam (2001-2018). She earned her PhD in Organizational Behavior from VU Amsterdam (1997) and studied Social Psychology at the University of Groningen. Her academic career includes visiting roles at Northwestern University (USA) as Adjunct Lecturer (2000-2001) and a sabbatical as Visiting Professor at ESADE Business School, Barcelona (2011). Her research focuses on gender and ethnic diversity in professional and academic careers, exploring implicit bias, normative beliefs, and discursive practices impacting career systems. She has led projects funded by the European Research Council (ERC) and contributed to initiatives like the GendERC project. Her work addresses career sustainability, work-life balance, and systemic diversity interventions. She has authored over 70 publications in journals like Academy of Management Review and Journal of Social Issues , and edited volumes such as Bruggen tussen Wetenschap en Praktijk . She has served on editorial boards for journals including Equality, Diversity and Inclusion and Journal of Social Issues . She has supervised six PhD theses and contributed to policy reforms in academia and professional services. Key contributions include analyzing gender bias in ERC grant selection processes, designing equitable career systems, and challenging organizational norms around parenting and careers. Her grants include the FP7 Marie Curie Fellowship (2011) and ERC CSA support (2012).

Lahiru WickramasingheView profile

Assistant Professor

• Bayesian methods
• Spatial and temporal data analysis
• Sparse multinomial data
• +5 more

Dr. Lahiru Wickramasinghe is an Assistant Professor of Statistics at the University of Winnipeg's Mathematics and Statistics department. He holds a PhD from the University of Manitoba (2021), following MSc (2015) and BSc degrees from the University of Peradeniya, Sri Lanka. Research Focus: Specializes in Bayesian methods, spatial/temporal data analysis, and applications in biostatistics/sports analytics. Key areas include sparse multinomial data modeling, smoothing techniques, and mark-recapture methods. Publications Overview: Recent work focuses on statistical applications in sports performance (cricket/baseball) and public health (COVID-19 analysis, cancer epidemiology). Methodological strengths in Bayesian inference and spatial modeling across diverse datasets. Affiliations: Active in academic statistics community with publications in journals like Annals of Biostatistics, Journal of Applied Statistics, and Inflammatory Bowel Diseases.

• Probability Theory
• Multivariate Statistics
• Gamma Distributions
• +2 more

Philippe Bernardoff is an Associate Professor at the University of Pau and the Pays de l'Adour. His research focuses on advanced probabilistic models, including multivariate gamma distributions, negative multinomial laws, and Laplace transform applications in statistical dependence structures. His work bridges theoretical probability and practical applications in areas like polarimetric image processing. Bernardoff's research interests center on developing statistical methodologies for complex multivariate systems, with emphases on distribution theory, simulation algorithms, and copula-based dependence modeling. His publications consistently explore the mathematical frontiers of infinitely divisible distributions and their computational implementations. His recent articles demonstrate a strong focus on advancing simulation techniques for gamma distributions and expanding the theoretical understanding of Laplace transforms in multivariate contexts. This work has implications for high-dimensional data analysis and stochastic modeling.

Alwell OyetView profile

Professor

• Longitudinal Data Analysis
• Analysis of Multinomial Data
• Spatial Data Analysis
• +3 more

Alwell Oyet is a Professor of Statistics at the Department of Mathematics and Statistics within Memorial University of Newfoundland. His research focuses on advanced statistical methodologies including longitudinal data analysis, spatial data analysis, and robust methods. He has held academic positions since at least 1997, following his PhD from the University of Alberta. Education: B.Sc. Industrial Mathematics, Federal University of Technology, Owerri, Nigeria (1986) M.Sc. Statistics, Federal University of Technology, Owerri, Nigeria (1992) Ph.D. Statistics, University of Alberta, Canada (1997) Research Interests: Oyet’s work spans statistical computing, time series analysis, and multivariate analysis. His expertise includes developing computational tools for statistical modeling and analyzing complex datasets. Teaching: He has taught numerous advanced statistics courses, including Time Series Analysis, Multivariate Analysis, and Design of Experiments at both undergraduate and graduate levels. Recent courses include STAT 3540 (Time Series Analysis) and STAT 6560 (Multivariate Analysis). Affiliations: Active in academic roles since joining Memorial University, Oyet contributes to graduate programs in statistics and collaborates on statistical methodologies with international peers.