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• Information Systems
• Econometrics
• Business Economics
• +4 more

Prof. Dr. Kosta Sotiroski is a Full Professor at the Faculty of Economics - Prilep, University 'St. Kliment Ohridski' - Bitola. He holds a PhD in Economics from the same institution (1998), with research focused on statistical modeling and production optimization. His academic roles include mentoring doctoral students in Marketing, Management, and Business Informatics, and serving as Secretary of the Doctoral Studies Committee. Education: Bachelor's in Mathematical-Statistical-Cybernetics (1986) Master's in Monte Carlo Simulation in Economic Management (1993) PhD in 'Modeling Optimal Organization of Production Processes' (1998) Research Interests: Business Informatics, ERP Systems, Big Data Technologies, Econometrics, E-Banking Services, and Statistical Process Control. His work emphasizes data-driven decision-making in economics and healthcare. Publications: Over 112 publications in areas like arterial stiffness in dialysis patients, e-learning adoption, and SME innovation. Recent trends focus on healthcare analytics and cloud computing in education. Awards & Grants: Involved in EU-funded projects such as TEMPUS and CARDS initiatives. Recognized as a top-ranked scientist in Macedonia for contributions to economics and management science. Labs/Teams: Leads research in Business Informatics and Data Analytics. Active in editorial roles for journals like Strategic Management and International Journal of Economics, Politics, Humanities & Social Sciences.

Frédéric BERTRANDView profile

Professor

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +5 more

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

Michael KutnerView profile

Professor

• Statistical Modeling
• Clinical Trials
• Epidemiology

Michael Kutner is the Rollins Professor of Biostatistics at the Emory School of Public Health, Department of Biostatistics and Bioinformatics. He holds an Endowed Chair and has served in leadership roles, including Chair of the Department of Biostatistics and Bioinformatics (2002-2009) and Chair of the Department of Biostatistics and Epidemiology at the Cleveland Clinic (1994-1999). He has also held roles such as Executive Associate Dean for Faculty Affairs at Emory's School of Public Health (1990-1993). Education: PhD from Texas A&M University, M.S. from Virginia Polytechnic Institute and State University (VPI & SU). His research focuses on Statistical Modeling , Clinical Trials , and Epidemiology . He has contributed extensively to statistical methodology, including works on linear models, regression analysis, and experimental design, with a strong emphasis on applied biostatistics. His publications span foundational textbooks like Applied Linear Statistical Models and Applied Linear Regression Models , which remain influential in statistical education. Recent work includes multi-omic cancer genetics studies and collaborative statistical practices. Awards: ASA Mentoring Award (2018) Lifetime Achievement Awards (ASA Georgia Chapter, 2018) Fellow of the American Statistical Association (1984) H.O. Hartley Award (1984) His career highlights include leadership in academic departments, prolific textbook authorship, and recognition for mentoring and service to the statistical community.

• Linear Statistical Models
• Covariance Analysis
• Biostatistics
• +3 more

Jarkko Isotalo is a University Lecturer in the Department of Computing Sciences at Tampere University, Finland. His affiliation is within the Faculty of Information Technology and Communication Sciences, focusing on theoretical and applied statistical research. His research interests span linear statistical models , covariance analysis , biostatistics , and computational statistics . Notably, he explores topics like the Best Linear Unbiased Estimator (BLUE), mixed models, and their applications in clinical trials and ecological studies. Recent work includes studies on permissible covariance structures in linear models, treatment outcomes for gambling disorder (using randomized controlled trials), and partitioning of forest CO2 emissions. His articles often emphasize theoretical foundations while addressing practical implications in health and environmental sciences. Isotalo has contributed to methodological advancements in statistical modeling without explicitly mentioned awards or grants. His research often involves interdisciplinary collaborations, bridging mathematics, computing, and applied sciences.

• Epidemiology
• Biostatistics
• Infectious Diseases
• +4 more

Forrest W. Crawford is an Associate Professor Adjunct in Biostatistics at the Yale School of Public Health (YSPH). He holds affiliations with the Computational Biology and Bioinformatics Program, the Center for Biomedical Data Science, and the Public Health Modeling initiative. His research focuses on mathematical and statistical methods for discrete structures and stochastic processes, with applications in epidemiology, public health, biomedicine, and evolutionary science. Dr. Crawford earned his PhD and MS in Biostatistics from the University of California, Los Angeles (2012 and 2009). His work bridges statistical theory and applied public health challenges, emphasizing network analysis, infectious disease modeling, and computational methods. He leads the Crawford Lab, which develops tools for understanding complex systems like opioid epidemics, HIV transmission dynamics, and pandemic response strategies. Key research interests include: - Network-based epidemiological modeling - Causal inference under contagion - Public health data science - Statistical methods for hidden populations - Stochastic processes in biological systems Recent work includes studies on fentanyl-related mortality trends in Connecticut, SARS-CoV-2 transmission dynamics, and vaccine efficacy analyses. His interdisciplinary collaborations span public health, computer science, and mathematics. Dr. Crawford has advised numerous research initiatives and contributed to pandemic response efforts through real-time modeling. His lab's projects include software development for public health surveillance (e.g., the covidestim tool) and methodological advancements in respondent-driven sampling for hidden populations. Professional affiliations include the American Statistical Association, Society for Mathematical Biology, and editorial roles in statistical and epidemiological journals.

Paul GustafsonView profile

Professor

• Bayesian Analysis
• Partial Identification
• Causal Inference
• +5 more

Paul Gustafson is a Professor and Department Head in the Department of Statistics at the University of British Columbia (UBC). His research focuses on Bayesian analysis, partial identification, causal inference, measurement error, and evidence synthesis, with applications in epidemiology, public health, and biostatistics. He has authored two books: Bayesian Inference in Partially Identified Models (2015) and Measurement Error and Misclassification in Statistics and Epidemiology (2003). As an inaugural Statistics Editor of Epidemiology , he also serves as an Associate Editor of Biometrics . Gustafson has led the STRATOS initiative to strengthen analytic thinking in observational studies, particularly addressing measurement error and misclassification. His work emphasizes methodological advancements in handling complex data challenges, with contributions to understanding bias in epidemic curves and improving statistical practices in observational research.

Charmaine DeanView profile

Professor

• Biostatistics
• Environmental Science
• Epidemiology
• +7 more

Charmaine Dean is the Vice-President, Research and Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. She holds leadership roles in research administration and academic governance, including previous service as Dean of Science at Western University and Associate Dean at Simon Fraser University. Dr. Dean earned her Ph.D. in Statistics from the University of Waterloo in 1988. Her research focuses on biostatistics, environmental science, and spatio-temporal analysis, with applications to public health, wildfire management, and ecological modeling. Notable contributions include disease mapping, clinical trial design, and statistical methods for analyzing wildfire risk and forest ecology. She has received prestigious awards such as the CRM-SSC Prize, Fellowships from leading statistical societies, and the L’Ordre des Palmes Académiques from France. Her service includes roles on advisory boards for institutions like the Pacific Institute for the Mathematical Sciences and the Banff International Research Station. Dr. Dean’s work bridges academic research and policy, with impactful contributions to environmental and public health policy through interdisciplinary collaborations. Her research group’s activities include developing statistical tools for environmental risk assessment and pandemic response.

• Supply Chain Management
• Time-Series Analysis
• Data-Mining
• +2 more

Vladimir Kovtun is a Visiting Assistant Professor at the Sy Syms School of Business, Yeshiva University. He holds a PhD from NYU Stern School of Business and joined Yeshiva University as a Research Fellow in 2013. His research focuses on the intersection of time-series analysis and supply chain management, particularly the value of information-sharing in supply chains using modern forecasting models. Previously, he studied data-mining and high-dimensional data. He teaches courses in Business Statistics, Quantitative Methods, Business Intelligence, and Consumer Insights. His work has been published in journals such as Biostatistics and Naval Research Logistics . Dr. Kovtun works across two campuses: Belfer Hall (Wilf Campus) and 215 Lexington Ave (Beren Campus).

Vincent JaddoeView profile

Professor

• Pediatrics
• Epidemiology
• Developmental Biology
• +5 more

Vincent Jaddoe is a Professor of Pediatric Epidemiology at Erasmus University Rotterdam and an Adjunct Professor at Harvard T.H. Chan School of Public Health . He is Chair of the Division of General Pediatrics at Erasmus MC, Sophia Children’s Hospital, and leads the Generation R Study , a major population-based cohort involving 10,000 pregnant women and their children. His work bridges epidemiology, pediatrics, and epigenomics, focusing on developmental origins of health and disease. Current Positions Chair, Division of General Pediatrics, Erasmus MC, Netherlands Principal Investigator, Generation R Research Core Facility Project Coordinator, EU LifeCycle Project Jaddoe’s research explores how genetic, epigenetic, and environmental factors during pregnancy influence lifelong health. His studies span fetal development , childhood obesity , and preconception health . He utilizes advanced epidemiological methods, imaging, and omics technologies, collaborating with over 20 departments and 150 PhD students. Key publications address fetal growth restriction , maternal smoking , and genetic heritability in child health. Articles highlight cross-disciplinary approaches integrating epidemiology , neuroscience , and genomics . Scientific Awards Lifetime member of Royal Dutch Academy of Sciences (KNAW) European Research Council (ERC) Consolidator Grant NWO VIDI and VENI grants Jaddoe has received over 900 publications with an H-index of 92 and significant citations. He coordinates the Academic Rotterdam Center for Pregnancy & Childhood , fostering collaboration between researchers, healthcare providers, and policymakers.

Jun LiuView profile

Professor

• Bioinformatics
• Statistical Genetics
• Bayesian Methods
• +3 more

Jun Liu is a Professor in the Department of Statistics at Harvard University, renowned for his contributions to computational statistics, bioinformatics, and Bayesian methods. He leads research in statistical genetics, genomic data analysis, and algorithm development for biological systems. His work integrates advanced statistical theory with computational tools, such as the Gibbs Motif Sampler and Bayesian Aligner, widely used in bioinformatics. Research interests include Monte Carlo methods, statistical genetics, and machine learning applications in biology. He has developed influential software tools like BPPS, MDScan, and CLIC, addressing problems in motif discovery, genomic sequence analysis, and pathway expansion. Liu’s interdisciplinary approach bridges statistics and computational biology, with applications in cancer genomics, immune repertoire analysis, and evolutionary biology. Notable recognition includes fellowships from the American Statistical Association, Institute of Mathematical Statistics, and International Society for Bayesian Analysis. He advises numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry positions globally. His lab collaborates internationally, organizing workshops on Monte Carlo methods and statistical forums in China. Liu’s publications span statistical methodology, computational biology, and genetics, with recent work on genomic element evolution, immune cell profiling, and algorithmic advancements in high-dimensional data analysis. He emphasizes inverse modeling and Bayesian approaches to tackle complex biological questions.

• Statistics
• Applied Mathematics
• Data Science
• +4 more

Dr. Jessica Hargreaves is a Lecturer in Data Science at the University of York's Department of Mathematics. Previously, she held roles as a Lecturer in Statistics at Sheffield Hallam University and taught mathematics in secondary schools (2011-2014). She earned an MSci in Mathematics from Imperial College London and a PhD in Statistics from the University of York. Research Focus: Her work spans applied statistics in biology, chemistry, and sports analytics. Key areas include wavelet techniques for nonstationary time series analysis (e.g., circadian rhythms, EEG data), statistical methods in sports (attendance patterns, athlete career trajectories), and collaborations with professional sports organizations. She co-founded Quantitative Sports Analysis: York (QSAY) with Dr. Ben Powell and Dr. Rich Bingham. Roles & Affiliations: Impact Officer, EGPC Committee Member, and Vice-Chair of the Royal Statistical Society's Statistics in Sport Section. Research datasets are available via the University of York repository. Teaching & Scholarship: Specializes in integrating practical learning experiences, evidenced by publications like 'Work-related learning: benefits and challenges' (2019) and pandemic-era pedagogy studies.

• Bayesian Modeling
• Longitudinal Data Analysis
• Experimental Designs
• +5 more

Elizabeth Slate is a Professor in the Department of Statistics at Florida State University. Her expertise includes longitudinal data analysis, experimental designs, and Bayesian modeling. She focuses on interdisciplinary applications in biomedical research, particularly in musculoskeletal biomechanics, clinical trial design, and public health disparities. Her work bridges statistical methodology with real-world clinical and epidemiological challenges. Research Interests: Her research spans Bayesian statistical methods, causal inference, and the analysis of complex biomedical datasets. Key areas include biomechanical modeling of joint disorders, risk factor analysis for chronic diseases, and the development of innovative statistical frameworks for clinical trials and observational studies. She has contributed to understanding the structural-functional relationships in musculoskeletal tissues and the sociocultural determinants of health behaviors in underserved populations. Publications Trends: Recent work emphasizes Bayesian borrowing of historical data in basket trials, explainable AI in medical imaging, and the biomechanical correlates of TMJ disorders. She also investigates longitudinal health outcomes in diabetes and HPV vaccination uptake among marginalized groups. Methodologically, her articles address causal mediation, sensitivity analysis, and mixed-scale data integration. Grants and Advising: While specific grant details are not listed, her publication record indicates sustained funding in interdisciplinary health research. No advising information is explicitly provided in the source text. Labs/Teams: Collaboration networks likely span biomedical engineering, public health, and clinical departments, though formal affiliations are not detailed here.

J. Richard LandisView profile

Professor

• Biostatistics
• Epidemiology
• Clinical Research Informatics
• +6 more

Dr. J. Richard Landis is a Professor of Biostatistics and Statistics at the University of Pennsylvania's Perelman School of Medicine and Wharton School, respectively. As Senior Vice Chair (SVC) of the DBEI Department, he oversees strategic initiatives and serves as a liaison between academic and clinical units. His roles include Director of the Biostatistics Unit at the Center for Clinical Epidemiology and Biostatistics (CCEB), and Director of the Clinical Research Computing Unit (CRCU), supporting translational research infrastructure. He holds advanced degrees from Millersville University (B.S.Ed., 1969), and the University of North Carolina at Chapel Hill (M.S. 1973, Ph.D. 1975). Prior to Penn, he was Professor of Biostatistics at the University of Michigan (1975–1988) and founded Penn State's Center for Biostatistics and Epidemiology (1988–1997). Dr. Landis' research focuses on statistical methodologies for longitudinal and categorical data, with applications to urological, renal, and chronic pain syndromes. He leads NIH-funded Data Coordinating Centers for networks like the MAPP Research Network (2008–2022) and the Chronic Renal Insufficiency Cohort (CRIC) study (2001–2023). His work emphasizes precision medicine, clinical trial design, and translational epidemiology. He has authored over 180 peer-reviewed articles and received prestigious awards, including the Mortimer Spiegelman Gold Medal and the Marvin Zelen Leadership Award. His current projects include the RURAL study (NHLBI) and the HOPE Consortium (NIDDK). Grants & Leadership : NIH-funded DCCs for MAPP, CRIC, and RURAL studies Collaborations : Co-investigator roles in CTSA-Hub cores and multidisciplinary teams Labs/Teams : CRCU, CCEB, and SDCC/SDRC centers

Therese DolecekView profile

Research Professor

• Epidemiology
• Biostatistics
• Public Health
• +3 more

Therese Dolecek is a Research Professor Emerita at the School of Public Health, University of Illinois at Chicago (UIC), affiliated with the Department of Epidemiology and Biostatistics. She holds the title of Research Professor Emerita, indicating a distinguished retired academic role. Her contact details include an office at 910 SPHPI, 1603 W. Taylor St., Chicago, and a phone number: (312) 996-9516. Her email is tdolecek@uic.edu. While specific research interests or projects are not explicitly detailed in the provided text, her department affiliation suggests expertise in epidemiological and biostatistical methods, public health policy, and data-driven health outcomes research. No awards, grants, or recent publications are mentioned in the available information.

Khurram NadeemView profile

Associate Professor

• Predictive Modeling
• Wildland Fire Occurrence Prediction
• Spatio-Temporal Processes
• +4 more

Khurram Nadeem is an Associate Professor of Statistics at the University of Guelph, specializing in predictive modeling of ecological and environmental systems. His research focuses on leveraging big data analytics to address challenges in wildland fire prediction, agricultural sustainability, and statistical bioinformatics. He leads a lab offering graduate research opportunities in these areas and collaborates with organizations like the Canadian Interagency Forest Fire Centre (CIFFC). Education: PhD in Statistics, University of Alberta (2013) MSc in Statistics, University of Karachi (2005) BSc (Honours) in Statistics, University of Karachi (2003) Research Themes: Wildland Fire Prediction: Developing spatiotemporal models to forecast fire occurrences in British Columbia, aiding fire management strategies. Agro-Environmental Science: Integrating big data from precision agriculture and genomics to optimize sustainable farming practices under climate change. Statistical Bioinformatics: Analyzing microbiome data in livestock and crops to improve disease prevention and crop resilience. Media & Partnerships: Featured in University of Guelph news for wildfire research and Food from Thought initiatives. Seeks industry partnerships in forest fire analytics, environmetrics, and quantitative ecology. Advising & Grants: Supervises graduate students in Statistics and Bioinformatics programs. Offers postdoctoral positions in applied statistics for climate-agriculture linkages and microbiome modeling. Labs & Teams: Hosts a multidisciplinary lab focusing on environmental and agricultural data science. Collaborates with the Canadian Forest Service and agricultural stakeholders.