Find Academic Supervisors

• Bayesian Methods
• Stochastic Processes
• Time Series Analysis
• +3 more

Tevfik Aktekin is a Professor in the Department of Decision Sciences at the Paul College of Business and Economics, University of New Hampshire. His research focuses on Bayesian inference and stochastic modeling in service systems and business analytics. Education : Ph.D. in Decision Sciences (George Washington University), M.B.A. in Management Decision Making (George Washington University), B.S. in Mechanical Engineering (Yildiz Technical University). His work applies Bayesian state-space models to multivariate count data, with applications in call center staffing, product modification cycles, and public health analysis. He has published in journals like Annals of Applied Statistics , Bayesian Analysis , and European Journal of Operational Research . The 15 most recent articles highlight trends in sequential Bayesian learning for dynamic systems (e.g., bike-sharing, web traffic), time series of non-Gaussian data, and stochastic optimization under uncertainty. These works bridge statistical theory with practical applications in business and health.

• Optimal Transport
• Martingale Theory
• Mathematical Finance
• +3 more

Mathias Beiglböck is a full Professor at the Department of Mathematics within the Faculty of Mathematics at the University of Vienna. His research spans multiple areas of mathematical analysis with a strong focus on probability theory and its applications to finance and other fields. With over 60 publications spanning from 2009 to 2024, Beiglböck has established himself as a leading researcher in his field. Beiglböck's primary research interests center around optimal transport theory, martingale theory, and their applications to mathematical finance. His work explores the deep connections between probability theory and financial mathematics, particularly in areas such as option pricing, risk management, and stochastic processes. His research also extends to epidemiological modeling, as evidenced by his contributions to SARS-CoV-2 research during the pandemic. Analysis of his recent publications (2022-2024) reveals a strong focus on advancing the theoretical foundations of optimal transport and martingale theory while finding novel applications in finance and data science. His work often bridges pure mathematical theory with practical applications, particularly in financial modeling and risk assessment. The high citation counts across his publications (some exceeding 100 citations) indicate significant impact in his field. Beiglböck has collaborated extensively with researchers across Europe, particularly with scholars from France, Austria, and the UK. His work on the COVID-19 pandemic demonstrates his ability to apply mathematical expertise to pressing real-world problems, contributing to public health policy through rigorous quantitative analysis.

Petros DellaportasView profile

Professor

• Bayesian Statistics
• Machine Learning
• Financial Econometrics
• +7 more

Petros Dellaportas holds dual appointments as a Professor of Statistical Science at University College London (UCL) and a Professor of Statistics at the Athens University of Economics and Business (AUEB). His research focuses on Bayesian statistics, machine learning, financial econometrics, and dynamic pricing. He leads projects on topics such as Poisson processes for cybersecurity, reservoir computing for macroeconomic forecasting, and probabilistic fault detection in wind parks. His recent publications emphasize advancements in Bayesian methods, variational autoencoders, and spatio-temporal point processes. Dellaportas has supervised over 20 PhD students, contributing to areas like stochastic volatility models and inverse reinforcement learning. He co-founded Thales and Friends, an organization bridging mathematics and cultural activities, and organizes the Greek Stochastics workshop series on topics ranging from causal learning to computational statistics. Key projects include anomaly detection in VAT networks and scalable Gaussian process models. His work often integrates statistical theory with applications in finance, sports analytics, and environmental science. Dellaportas maintains active collaborations with institutions globally, advancing interdisciplinary research and methodological innovations in statistical science.

Joseph GlazView profile

Professor

• Applied Probability
• Geometrical Probability
• Probability Approximations
• +7 more

Joseph Glaz is a Professor in the Department of Statistics at the University of Connecticut. His work focuses on applied probability and statistical methodology, particularly in scan statistics and related inference techniques. Contact: joseph.glaz@uconn.edu Phone: (860) 486-4193 (Storrs Campus) Research highlights: Develops scan statistics for discrete , continuous , and conditional data frameworks Specializes in change-point detection for normal data mean/variance Innovates robust methods for outlier-prone datasets Extends scan statistics to network and graph structures Contributed to foundational texts like the Handbook of Scan Statistics Publication trends include: Scan statistics for genomic data (Hi-C translocation detection) Nonparametric and Bayesian extensions of scan methods Approximations and inequalities for sequential testing Applications in quality control , medical imaging , and sensor networks

• Machine Learning
• Statistics
• Model-Based Clustering
• +5 more

Brendan Murphy is a Full Professor in the School of Mathematical Sciences at University College Dublin since 2015, previously holding a Professor role at the same institution (2007-2015) and a Lecturer position at Trinity College Dublin (1999-2007). He is a Principal Investigator at the Insight Centre for Data Analytics and actively works in Machine Learning & Statistics. Research Focus: His work centers on Model-based clustering Mixture models Applications in sports analytics, food science, microbiome studies, and public health Bayesian statistical methods High-dimensional data analysis Article Trends: Murphy's recent publications (2024-2025) emphasize soft clustering techniques, Bayesian mixture models, and their applications across diverse domains including metabolomics, political science, and oceanography. His work addresses challenges in variable selection, robust classification, and multi-omics integration.

Jenni NikuView profile

Senior Lecturer

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +3 more

Jenni Niku is a Senior Lecturer at the Department of Mathematics and Statistics, University of Jyväskylä, specializing in multivariate statistical modeling and computational methods for ecological data. She is actively involved in the Predictive Community Ecology Group and leads research projects on latent variable models for complex ecological structures. Research Focus: Latent variable models for community ecology Spatial-temporal pattern analysis Statistical methodology for multivariate data Joint species distribution modeling Computational tools for ecological datasets Recent Publications: Jenni has contributed to advancements in analyzing compositional count data, fungal dispersal dynamics, chronic disease modeling, and peatland restoration. Her work bridges statistical innovation with ecological applications, particularly in handling high-dimensional data. Collaborations: She works extensively with researchers from Biological and Environmental Science departments, focusing on interdisciplinary applications of her statistical models.

Justin SilvermanView profile

Assistant Professor

• Bayesian Statistics
• Machine Learning
• Genomics
• +7 more

Dr. Justin Silverman is an Assistant Professor of Information Science & Technology, Statistics, and Medicine at Pennsylvania State University, with affiliations in the Department of Statistics and the Institute for Computational and Data Science. He holds an MD from Duke University School of Medicine (2020), a PhD in Computational Biology and Bioinformatics from Duke (2019), and a BS in Physics and Biophysics from Johns Hopkins University (2011). His research integrates Bayesian statistics, machine learning, and genomics to address impactful biomedical questions. Key focuses include microbiome analysis, gene expression studies, and scale-reliant inference methodologies. His work emphasizes methodological rigor and clinical relevance, with applications in personalized medicine, agriculture, and epidemiology. The Silverman Lab develops cutting-edge tools like the fido and philr packages for compositional data analysis. Collaborations span academia and industry, addressing challenges in livestock health, viral surveillance, and precision medicine. He also leads Anarres Analytics LLC, translating statistical methods into practical solutions for clients. His advising includes PhD and MS students working on topics like scalable Bayesian models, microbial co-occurrence networks, and topological data analysis in oncology. Notable projects include studies on ancient microbiomes, dairy cow treatments, and pandemic modeling. Labs and teams: Silverman Lab (PSU), Anarres Analytics. Current positions: PI, startup founder, and collaborator in multi-institutional projects.

Alwell Julius OyetView profile

Professor

• Longitudinal Data Analysis
• Spatial Data Analysis
• Time Series Analysis
• +3 more

Alwell Julius Oyet is a Professor of Statistics in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He earned his B.Sc. (1986) and M.Sc. (1992) from the Federal University of Technology, Owerri, Nigeria, and his Ph.D. (1997) from the University of Alberta, Canada. His research focuses on longitudinal data analysis, spatial statistics, time series, statistical computing, multinomial data, and robust methods. He maintains active research collaborations in these areas. Oyet teaches undergraduate and graduate courses including Design of Experiments, Time Series Analysis, Multivariate Analysis, and Computational Statistics. He has extensive experience developing course materials and statistical computing resources.

Anas AbdallahView profile

Associate Professor

• Actuarial Science
• Insurance Mathematics
• Loss Reserving Models
• +5 more

Anas Abdallah is an Associate Professor in the Department of Mathematics and Statistics at McMaster University. His research focuses on actuarial science, particularly in loss reserving, stochastic modeling, and the application of machine learning techniques such as recurrent neural networks (RNNs) to insurance risk analysis. He specializes in dependence modeling using copulas and Sarmanov distributions to address multivariate insurance claims and risk capital analysis. Education: PhD in Actuarial Science, Université Laval (2016) MSc in Mathematical Engineering and Modeling, Institut National des Sciences Appliquées (2009) Research Interests: Develops methods for multivariate loss reserving and risk capital estimation Applies hierarchical Archimedean copulas and RNNs to model dependencies between loss triangles Explores stochastic reserving techniques and alternative actuarial provisioning strategies Teaching: Instructor for courses such as Actuarial Models in Non-Life Insurance, Financial Markets and Derivatives, and Statistics of Financial Data Has taught across multiple levels including undergraduate (MATH 2FM3), graduate (STATS 4G03), and professional programs (MFM 704) Grants and Labs: No specific grants or lab affiliations explicitly listed, though his research indicates involvement in collaborative projects in actuarial science and financial mathematics.

• Statistical Inference
• Machine Learning
• Bioinformatics
• +3 more

Lucy Gao is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC). Her research focuses on statistical inference, machine learning, and bioinformatics, with particular emphasis on clustering methods, high-dimensional data analysis, and optimal design theory. She holds a faculty position within UBC’s Faculty of Science and is affiliated with the Vancouver Campus. Her work spans theoretical developments in statistical methodologies and applications to biological data, such as single-cell RNA sequencing analysis. She has contributed to areas like selective inference for hierarchical clustering and algorithmic optimization for complex systems. Her email is lucy.gao@stat.ubc.ca, and she maintains a website at https://www.lucylgao.com/ . Recent publications highlight her expertise in data thinning techniques, latent variable modeling, and multiview network analysis. She has explored topics ranging from negative binomial count splitting in genomics to bilevel optimization algorithms. Her research bridges statistical theory and practical computational challenges in modern data science. No scientific awards or advising records are explicitly listed in the provided materials. Her academic contributions are primarily through peer-reviewed articles and methodological innovations.

Daniel KowalView profile

Associate Professor

• Bayesian Statistics
• Environmental Health and Policy
• Public Health
• +9 more

Daniel Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, effective July 2024. Previously, he served as the Dobelman Chair Assistant Professor at Rice University. His research focuses on Bayesian methodology for complex dependent data, including functional, time series, and spatial datasets, with applications in environmental health, epidemiology, finance, and astronomy. He develops scalable algorithms for high-dimensional data and interpretable uncertainty quantification. His work has been recognized with the Blackwell-Rosenbluth Award (2021) and ARO Young Investigator Award (2020). Education: Ph.D. in Statistics (Cornell University), M.S. in Statistics (Cornell University), B.A. in Mathematics (Washington University in St. Louis). Research interests include Bayesian models for prediction/inference, decision theory, discrete data analysis, and scalable approximations. Key areas of application: environmental health policy, wearable devices, economics, biomedical engineering, and astronomy. Recent grants include NSF-funded projects on adaptive dependent data models and Army Research Office initiatives on Bayesian approximations. He has supervised multiple Ph.D. students, including Yunan Gao, Thomas Sun, and Brian King. His software contributions include R packages like countSTAR, SeBR, and lmabc for Bayesian regression and data synthesis. Awards include Lindley Prize Honorable Mention (2024), Arnold Zellner Thesis Award (2018), and numerous student paper awards from ASA sections.

Noemi CorsiniView profile

Research Fellow

• Statistical Modeling
• Clustering
• Latent Variable Models
• +4 more

Noemi Corsini is a Research Associate in Statistics at the Institute of Criminology, University of Cambridge. She holds a PhD in Statistical Sciences (2021-2024) from the University of Padova, Italy, and completed her bachelor's and master's degrees in Statistical Sciences at the University of Bologna, Italy. Her research focuses on statistical modeling, particularly in clustering complex data structures , density-based clustering , latent variable models , and Bayesian inference . She develops methodologies for identifying overlapping clusters in high-dimensional network data and has contributed to advancements in computational statistics for multivariate count data. Key trends in her publications include: Extending Bayesian stochastic block models for overlapping cluster detection in biographical networks (2024) Addressing overdispersion in multivariate count data (2022) Investigating tourism participation patterns through statistical analysis (2023) Creating meta-frontier efficiency models for the insurance industry (2024) Advancing modal clustering techniques for categorical/mixed data (2023) Her technical expertise spans R, Python, and STATA, with fluent Italian and English communication skills.

Anne Ryan DriscollView profile

Associate Professor

• Statistical Process Control
• Healthcare Surveillance
• Industrial Statistics
• +3 more

Anne Ryan Driscoll is a Collegiate Associate Professor in the Department of Statistics at Virginia Tech. She holds affiliations with the College of Science and has research interests in Statistical Process Control, Healthcare Surveillance, and Industrial Statistics. Her work emphasizes practical applications of statistical methods in quality control and biomedical analysis. Education includes a B.S. in Mathematics and Physics from Emory & Henry College (2006), followed by M.S. (2007) and Ph.D. (2011) in Statistics from Virginia Tech. She has received numerous awards for teaching excellence and departmental contributions, including the Jesse C. Arnold Award and Ellis R. Ott National Scholarship. Her professional memberships include the American Statistical Association and American Society for Quality. Dr. Driscoll serves as a referee for journals like the Journal of Quality Technology and contributed to the 2011 Quality and Productivity Research Conference. Recent publications focus on control chart methodologies, biomedical applications of statistics, and experimental design. Her research bridges theoretical statistical frameworks with real-world quality assurance and healthcare applications.

Emily BergView profile

Associate Professor

• Statistics
• Small Area Estimation
• Agricultural Statistics
• +3 more

Emily Berg is an Associate Professor in the Department of Statistics at Iowa State University. Her research focuses on small area estimation, Bayesian hierarchical models, and statistical applications in agriculture and environmental science. She holds a Ph.D. in Statistics from Iowa State University (2010), an M.S. in Statistics (2008), and a B.A. in Mathematics from Middlebury College (2005). Education: Ph.D. Statistics, Iowa State University, 2010 M.S. Statistics, Iowa State University, 2008 B.A. Mathematics, Middlebury College, 2005 Her research interests include developing statistical methodologies for small area estimation, analyzing agricultural and environmental data, and addressing challenges in informative sampling and missing data. She has contributed to applications in transportation safety, veterinary practices, and environmental monitoring. Dr. Berg’s work emphasizes practical solutions for data integration and estimation under complex scenarios, including the use of Bayesian models and quantile regression techniques. She collaborates on projects such as the Conservation Effects Assessment Project and has developed web applications for data visualization and quality assessment. No scientific awards have been explicitly mentioned in the provided materials. She has advised or collaborated on numerous statistical methodologies but no specific student names are listed. Her research extends to agricultural economics, environmental modeling, and public health surveillance.

Richard A. DavisView profile

Professor

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +4 more

Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.