جست‌وجوی استاد و پژوهشگر

Eirik Myrvoll-Nilsenمشاهده پروفایل

پژوهشگر ارشد

• Climate Statistics
• Statistical Climatology
• Bayesian Inference
• +۳ مورد دیگر

Eirik Myrvoll-Nilsen serves as a Postdoctoral Fellow at the Department of Mathematics and Statistics, UiT The Arctic University of Norway, where he bridges statistical methodology with climate science and environmental monitoring. Based in Tromsø, he actively contributes to interdisciplinary research through collaborations within UiT's Complex Systems Modeling, Statistics and Data Analysis, and Machine Learning research groups, focusing on computationally intensive approaches to earth system challenges. His research expertise spans Climate Statistics, Statistical Climatology, Bayesian Inference, Long-range Dependence, Machine Learning for Environmental Science, and Paleoclimatology. He develops advanced statistical models for climate data analysis—including ice core chronology and global temperature response systems—while pioneering deep learning applications for automated classification of microfossils and marine fauna detection in underwater imagery, emphasizing methodological rigor and practical environmental solutions. Analysis of his recent publications reveals a cohesive research trajectory centered on uncertainty quantification in paleoclimate reconstructions and machine learning for environmental monitoring. Key methodological themes include Bayesian modeling of long-memory climate processes, statistical synchronization of ice core records, and deep learning frameworks for foraminifera classification—all demonstrating innovative integration of computational statistics with domain-specific climate and marine science challenges. Scientific awards: No scientific awards or honors were documented in the provided information. Advising and grants: The available text contains no details regarding doctoral/master's students supervised by Dr. Myrvoll-Nilsen or specific research grants secured. Labs and teams: He is an active member of UiT's Complex Systems Modeling (CoSMo), Statistics and Data Analysis, and Machine Learning research groups. Additionally, he contributes to the project 'Transforming ocean surveying by the power of DL and statistical methods,' which develops deep learning pipelines for automated analysis of marine environmental video data from Norwegian fjords and coastal waters.

Andrew Woodمشاهده پروفایل

استاد

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +۵ مورد دیگر

Professor Andrew Wood is a faculty member at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans non-Euclidean statistics, theoretical statistics, computational methods, and applied statistics in sciences and medicine. Research Interests : Non-Euclidean statistics, directional statistics, statistical shape analysis, asymptotic theory, computational statistics, stochastic differential equations, and applications in science/medicine. Grants : Funded by Engineering and Physical Sciences Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), and Australian Research Council Discovery Projects. Editorial Roles : Former Joint Editor of Journal of the Royal Statistical Society, Series B and current Associate Editor of Biometrika . Research Trends : Recent publications focus on robust statistical methods for non-Euclidean data, computational approaches for complex distributions, principal component analysis for high-dimensional datasets, and geometric inference on manifolds. Applications include microbiome analysis, surface fractal dimension estimation, and spherical regression models. Supervision & Collaboration : Registered as a supervisor at ANU, with collaborations across disciplines including molecular biology, environmental science, and computational mathematics.

• Statistics
• Nonparametric Statistics
• Survival Analysis
• +۵ مورد دیگر

Jacobo de Uña Álvarez is a full-time University Professor at the University of Vigo, Spain. He is affiliated with the School of Industrial Engineering in the Department of Statistics and Operational Research. His research focuses on nonparametric statistics, survival analysis, and statistical methods for censored and truncated data. He leads the SiDOR (Statistical Inference, Decision and Operational Research) research group and is associated with the Biomedical Research Center and CITMAga interuniversity research center. Dr. de Uña Álvarez earned his doctorate from the University of Santiago de Compostela in 1998 with a thesis titled "Statistical Inference under Proportional Censorship Models," supervised by Dr. Wenceslao González Manteiga. His academic journey has established him as a leading researcher in statistical methodology for complex data structures. His research interests center on nonparametric and semiparametric statistical methods, particularly for survival analysis with censored and truncated data. He has made significant contributions to the development of statistical techniques for doubly truncated data, length-biased sampling, and illness-death models. His work bridges theoretical statistics with practical applications in biomedicine and engineering. Analysis of his recent publications reveals a strong focus on methodological advances in handling complex data structures, particularly doubly truncated data. His research shows consistent progression from foundational work on nonparametric estimation to sophisticated methods for high-dimensional data and complex sampling schemes. The interdisciplinary nature of his work is evident in applications to biomedical research and genomics. Dr. de Uña Álvarez has mentored numerous researchers through collaborations, with extensive co-authorship networks. His work has been supported by various research grants focused on statistical methodology development, though specific grant details are not provided in the available information. He leads the SiDOR research group at the University of Vigo, which focuses on Statistical Inference, Decision Theory, and Operational Research. The group maintains strong collaborations with international researchers and institutions, contributing significantly to the advancement of nonparametric statistical methods.

Florian Huberمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Visual Analytics
• +۴ مورد دیگر

Prof. Dr. Florian Huber serves as Professor of Data Science and Visual Analytics at the Department of Media, Faculty of Media, Hochschule Düsseldorf University of Applied Sciences (HSD), a position held since September 2021. He is additionally affiliated with the Center for Digitalization and Digitality (ZDD), where he bridges data science methodologies with real-world applications in scientific and cultural domains through machine learning and visualization techniques. His academic foundation includes: Diploma in Physics, University of Leipzig PhD in Biological Physics, University of Leipzig (2012), thesis: "Emergent structure formation of the actin cytoskeleton" Prior to HSD, he worked as a postdoctoral researcher at AMOLF (Amsterdam) and TU Delft, founded the sustainable sweets company KÄNDI, and served as Data Scientist at the Netherlands eScience Center (2018-2021). Huber's research focuses on developing data science algorithms for comparative analysis of complex datasets, combining machine learning with domain expertise to enable meaningful comparisons through latent space transformations. Key areas include mass spectrometry-based metabolomics (chemical identification/similarity prediction), natural language processing (e.g., lyrical evolution analysis), and audio data science, with emphasis on interpretable and reproducible computational approaches for biology, medicine, and cultural studies. Analysis of his 2023-2025 publications reveals dominant themes in mass spectrometry data science (tools like matchms, MS2Query, Mass2SMILES) and NLP applications in musicology. His work increasingly integrates deep learning for cross-domain similarity prediction while creating educational resources, reflecting a trend toward versatile open-source solutions for interdisciplinary data challenges. Huber actively contributes to open science through GitHub repositories and reproducible pipelines, suggesting collaborative funding models centered on software sustainability. His teaching includes the "Data Science" module (MMI 05.35) with practical components for engineering programs, offered in German or English. He leads initiatives within HSD's Center for Digitalization and Digitality, fostering collaborations between data scientists and domain experts. Current projects involve interdisciplinary teams tackling metabolomics, cultural analytics, and educational tool development using Python-based open-source ecosystems, with future work likely expanding into new application domains while advancing methodological rigor.

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۵ مورد دیگر

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

• High Energy Physics
• Experimental Particle Physics
• Medical Imaging
• +۳ مورد دیگر

Erik Brücken is a University Lecturer and Docent at the Department of Physics, University of Helsinki. He actively participates in the Doctoral Programme in Materials Research and Nanosciences, as well as the Doctoral Programme in Particle Physics and Universe Sciences. His research spans high-energy physics, experimental particle physics, and detector development. Primary affiliation: Department of Physics, University of Helsinki Supervisory roles in two doctoral programs Active in CMS and ALICE collaborations Research Interests: Brücken's work focuses on experimental particle physics at colliders like the LHC, particularly in detector optimization and data analysis. His projects include the ALICE Time Projection Chamber Upgrade and multispectral photon-counting for medical imaging. Current research examines B meson decays, Higgs boson properties, and jet substructure in heavy-ion collisions. Recent Publications: His 2025 contributions include analyses of B0 meson decays, Higgs self-coupling constraints, top quark production cross-sections, and jet substructure studies in nuclear collisions. Projects: He leads a 2024-2027 European particle physics infrastructure project (TOTEM) and participates in collaborations related to detector development and beam characterization. Past projects include CMS Tracker upgrades and CDF experiments spanning 2002-2017. Academic Activities: Brücken organizes and presents at international conferences like the European Researcher's Night and Euroschool on Exotic Beams. He serves as a doctoral thesis committee member and contributes to peer review processes.

Bård Støveمشاهده پروفایل

استاد

• Statistics
• Financial Mathematics
• Actuarial Science
• +۷ مورد دیگر

Bård Støve is a Professor of Statistics at the Department of Mathematics , University of Bergen . His academic responsibilities include teaching courses such as STAT100 , STAT101 , STAT211 , STAT230 , STAT231 , STAT250 , STAT621 , STAT623 , and STATRISK . Research Focus: Financial mathematics, insurance risk modeling, nonlinear dependence structures, local Gaussian correlation, and time series analysis. Technical Expertise: Regime-switching models, copula functions, Bayesian inference, and TMB-based parameter estimation. Notable Trends in Publications: Recent work emphasizes applications of local Gaussian correlation in financial contagion analysis and insurance risk modeling, with methodological advancements in hidden Markov models and spatial dependence in weather-related claims. Earlier contributions focus on nonlinear regression, backfitting, and convolution estimators. Collaborations: Active in interdisciplinary research, particularly with co-authors like Dag Tjøstheim, Jan Bulla, and Sverre Sandberg, spanning finance, medical diagnostics, and environmental science.

Thomas B. Fombyمشاهده پروفایل

استاد

• Econometrics
• Applied Econometrics
• Predictive Analytics
• +۷ مورد دیگر

Thomas B. Fomby is a Professor of Economics at Southern Methodist University (SMU) within the Dedman College of Humanities and Sciences. He is affiliated with the Department of Economics and can be contacted via tfomby@mail.smu.edu and TFOMBY@SMU.EDU . Education: Ph.D. in Economics from the University of Missouri, Columbia Research Interests: Applied econometrics, time series forecasting (especially seasonal models), financial modeling, marketing research, social network impacts on child food insecurity (with Hunger Center Grant), predictive analytics for crime prevention (with Dallas Police Department and SMU Computer Science Department), epidemiological data analysis using count/panel models (with University of Texas Southwestern Medical Center), economic growth after natural disasters (with World Bank), and multivariate copulas for financial return dependence (with Federal Reserve Bank of Dallas) Editorial Roles: Senior Co-Editor of Advances in Econometrics since 1986; Research Associate with Federal Reserve Bank of Dallas since 1982 Scientific Awards: M Award for Outstanding Service to SMU (2000) Distinguished Lecturer, Godbey Lecture Series (Fall 2010) Rotunda Yearbook Outstanding Professor at SMU (2010-11) University of Missouri Arts and Sciences Outstanding Alumni Award (2018) Teaching: Undergraduate and graduate courses in Predictive Analytics for Economists, Economic and Business Forecasting, and advanced topics in econometrics Affiliations: Robert H. and Nancy Dedman Trustee Professor at SMU; Research Fellow at IZA; Fellow at GLO; Member of HCEO Global Working Group

Antonio Punzoمشاهده پروفایل

استاد

• Mixture Models
• Hidden Markov Models
• Model-Based Clustering
• +۷ مورد دیگر

Antonio Punzo is a Full Professor of Statistics at the Department of Economics and Business, University of Catania, Italy. He holds a Ph.D. in Methodological and Applied Statistics from the University of Milano-Bicocca (2008) and has held academic positions including Assistant Professor (2010-2015) and Associate Professor (2015-2018) at the University of Catania. He has also served as a Visiting Professor at the University of Guelph, Canada (2013). His research focuses on mixture models, hidden Markov models, model-based clustering, and statistical methodologies for financial and insurance data. Punzo has developed R packages like ContaminatedMixt and flexCWM , advancing computational statistics. His work emphasizes robust statistical techniques and applications to real-world problems in economics, finance, and environmental science. Education: Ph.D. in Methodological and Applied Statistics, University of Milano-Bicocca (2008) Laurea (cum laude) in Statistical and Actuarial Sciences, University of Calabria (2004) Research Interests: Punzo specializes in advanced statistical modeling, including mixture models, hidden Markov models, and robust clustering techniques. His recent work addresses heavy-tailed distributions, serial dependence, and applications in insurance risk, economic data analysis, and environmental modeling. He also explores computational methods for high-dimensional data and develops software tools for statistical analysis. Publications: Punzo has authored/co-authored over 80 peer-reviewed articles in top journals like the Journal of the Royal Statistical Society , Journal of Classification , and Insurance: Mathematics and Economics . His research frequently addresses methodological innovations and practical applications in finance, economics, and biometry. Awards & Recognition: While no specific awards are listed, his prolific publication record and contributions to statistical software highlight his academic impact. He is a key figure in the cluster-weighted modeling community and has organized workshops on statistical methodologies. Teaching: Punzo teaches courses on statistical models for economics and finance, finite mixture models, and general statistics at the undergraduate and graduate levels at the University of Catania and the University of Messina. Collaborations: He collaborates with researchers globally, including Paul D. McNicholas (University of Guelph) and Salvatore Ingrassia (University of Catania), on projects in mixture modeling and robust statistics.

Konstantinos Fokianosمشاهده پروفایل

استاد

• Time Series Analysis
• High-Dimensional Statistics
• Integer-Valued Time Series
• +۲ مورد دیگر

Konstantinos Fokianos is a Professor of Statistics at the University of Cyprus. He completed undergraduate studies in Mathematics at the University of Ioannina and earned his M.A. (1993) and Ph.D. (1996) in Statistics from the University of Maryland, USA. His academic career includes positions at Ohio State University (Visiting Assistant Professor), Lancaster University (2018-2020), and the University of Cyprus where he advanced from Lecturer to full Professor. Professor Fokianos specializes in time series analysis with focus areas including integer-valued time series models, high-dimensional time series, spectral analysis, semiparametric modeling, and isotonic estimation. His methodological research encompasses network autoregression, multivariate count time series, causal inference for health questionnaires, and clustering techniques. Applied work includes epidemiological network analysis, medical prognostics for leukemia, and tourism competition modeling. He has held visiting positions at Ludwig Maximilians University, National Institutes of Health, EPFL, and other institutions. Elected member of the International Statistical Institute since 2005, he serves on editorial boards of Journal of Time Series Analysis and Statistical Modeling, and edits Springer's 'Frontiers in Probability and Statistical Sciences' series.

Bjørge Herman Hansenمشاهده پروفایل

استاد

• Physical Activity and Health
• Public Health Surveillance
• Exercise Epidemiology
• +۴ مورد دیگر

Bjørge Herman Hansen is a Professor in the Department of Sport Science and Physical Education at the University of Agder (UiA). His research focuses on the relationship between physical activity and health across the lifespan, particularly in children, adults, and the elderly. He also examines surveillance of health behaviors and methodological advancements in device-based measurement of physical activity and sedentary behavior. Education: Master's degree completed in 2008 PhD defended at the Norwegian School of Sport (NIH) in 2013 Research Interests: Physical activity and health outcomes Accelerometry-based behavioral analysis Public health surveillance systems Pediatric and geriatric activity patterns Impact of sedentary behavior His work emphasizes translating research into actionable public health guidelines and interventions. Key Contributions: Over 150 peer-reviewed articles and book chapters, including influential studies on step-count mortality associations, device-measured activity metrics, and global cohort analyses. Labs/Teams: Active member of the Physical Activity and Health across the LifeSpan (PAHLS) research group, collaborating internationally on large-scale epidemiological studies.

Hokwon Choمشاهده پروفایل

دانشیار

• Statistical Decision Theory
• Sequential Analysis
• Ranking and Selection Methods
• +۶ مورد دیگر

Hokwon Cho is an Associate Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His expertise includes Statistical Decision Theory, Sequential Analysis, Ranking and Selection Methods, Linear Models, and Sampling Methods with a focus on Statistical Computing. His research emphasizes methodologies for multinomial models, clinical trials, radial basis functions, and numerical approximations. Cho's work centers on developing statistical methodologies for decision-making under uncertainty, particularly in sequential sampling frameworks. His recent articles address confidence interval estimation for risk ratios, multinomial cell counts, and binomial proportions, reflecting his deep engagement with sequential analysis and statistical inference. His contributions also extend to numerical methods for solving partial differential equations using Trefftz methods and addressing computational challenges in statistical modeling. No scientific awards are explicitly mentioned in the provided text. His advising and grant activities are not detailed here, though his research interests suggest involvement in methodological advancements. Cho's research integrates theoretical statistics with computational techniques, bridging applied and computational mathematics.

Anna Panorskaمشاهده پروفایل

استاد

• Probability Theory
• Applied Statistics
• Extreme Event Modeling
• +۴ مورد دیگر

Anna Panorska is a Professor at the University of Nevada, Reno (UNR), affiliated with the Department of Mathematics & Statistics within the College of Science. She holds expertise in theoretical and applied probability and statistics, with collaborative research in ecology, engineering, climate science, hydrology, and medicine. Her work spans limit theorems for extreme events, multivariate statistical models, and their applications. She has taught courses including Introductory Statistics, Probability Theory, and Multivariate Analysis at both undergraduate and graduate levels. Education: M.S. in Mathematics (University of Warsaw, Poland), M.S. in Statistics (UT El Paso, 1988), and Ph.D. in Mathematics (UC Santa Barbara, 1992). Her research emphasizes extreme value theory, stochastic models, and interdisciplinary applications. Key areas include precipitation volatility, climate variability, and high-dimensional data analysis. Publications focus on statistical distributions, stochastic processes, and applied problems in engineering and environmental science. Notable works include studies on slash distributions, extreme precipitation modeling, and trivariate stochastic episodes. Her contributions bridge theoretical foundations with real-world applications across diverse fields.

• Neuroscience
• Statistics
• Machine Learning
• +۳ مورد دیگر

David Zoltowski is a Postdoctoral Researcher in the Department of Statistics at Stanford University, advised by Scott Linderman. His research focuses on developing and applying statistical and machine learning methods to understand neural dynamics, decision-making processes, and multi-region neural communication. He specializes in latent variable models, Bayesian inference techniques, and dynamical systems analysis. Key contributions include benchmarking frameworks for neural activity models and methods for uncovering structure in complex neural datasets. His work bridges computational neuroscience and statistics, with applications spanning neural decoding, state-space modeling, and neuromodulation. Recent projects address the integration of time and reward signals in decision-making circuits and the development of scalable algorithms for high-dimensional spike train analysis. David collaborates on interdisciplinary projects involving neuroimaging, behavioral neuroscience, and probabilistic modeling. His tools and benchmarks are widely used in the computational neuroscience community for evaluating latent variable models and neural decoding systems.

Jack O'Brienمشاهده پروفایل

دانشیار

• Malaria Genetics
• Statistical Genetics
• Bioinformatics
• +۳ مورد دیگر

Jack O'Brien is an Associate Professor of Mathematics at Bowdoin College. His research focuses on statistical genetics, molecular evolution, and bioinformatics, particularly in malaria and microbial systems. He holds a PhD in Biomathematics from UCLA (2008) and a BA in Mathematics from Pomona College (2002). His work integrates advanced statistical methods with genomic data to study pathogen diversity, population structure, and drug resistance. Education: PhD, Biomathematics, University of California-Los Angeles, 2008 BA, Mathematics (Honors), Pomona College, 2002 Research Interests: O’Brien develops statistical models to analyze genomic data from rapidly evolving pathogens like Plasmodium falciparum . His work addresses challenges in estimating evolutionary parameters, detecting strain mixtures in clinical samples, and understanding population dynamics. Recent projects involve Bayesian methods for voting bloc analysis and ecological pattern detection in archaeological datasets. Awards: Royal Society International Travel Grant (2009) UC Chancellor’s Fellowship (2003-2004, 2007-2008) NIH Systems and Integrative Biology Fellowship (2004-2007) Fulbright Scholar (Norway, 2002-2003) Pomona College Mathematics Honors Thesis (2002) Teaching & Grants: Current courses: MATH 1300 (Introduction to Statistics and Computation) and MATH 2606 (Statistics Education) Grants include NIH funding for malaria genomics and UC support for biomathematical research Labs/Teams: Collaborates with malaria research consortia (e.g., MalariaGEN) and interdisciplinary teams in bioinformatics, ecology, and archaeology.