جست‌وجوی استاد و پژوهشگر

Silke Behrendtمشاهده پروفایل

دانشیار

• Alcohol Use Disorder
• Substance Abuse
• Clinical Psychology
• +۶ مورد دیگر

Silke Behrendt is an Associate Professor at the Department of Psychology within the Faculty of Health Sciences at the University of Southern Denmark. She is affiliated with the Research Unit of Psychiatry (Odense) and Odense University Hospital (OUH), with a focus on clinical psychology and alcohol use disorder (AUD) research. Primary Affiliation: University of Southern Denmark Secondary Affiliation: Karolinska Institutet (KI) Collaboration: Odense University Hospital (OUH) Research Interests : Behrendt specializes in Alcohol Use Disorder among older adults, investigating treatment outcomes, predictors of recovery, and the intersection of AUD with physical illnesses. Her work addresses healthcare access disparities, quality-of-life improvements, and digital interventions like text messaging for high-risk alcohol use. She employs methodologies such as Motivational Interviewing , Generalized Linear Modeling , and DSM-5 diagnostics. Her research also explores comorbidities like Alcoholic Polyneuropathy and Nicotine Dependence in aging populations. Scientific Contributions : Recent work includes studies on the AUD treatment gap in middle-aged and older adults, age-moderated psychotherapy effectiveness, and quality-of-life outcomes post-treatment. She leads projects like Mind the Gap (2023–2026) and Text Messaging to Reduce High-Risk Alcohol Use (2023–2025). Awards and Roles : Behrendt serves on the editorial review board of the journal Addiction (2023–) and has organized conferences on AUD treatment for older adults (e.g., 2024). She mentors via PhD committee roles and delivers guest lectures on targeted alcohol interventions.

CAROLINA DALL ANTONIA DA MOTTAمشاهده پروفایل

پژوهشگر

• Psychometrics
• Factor Analysis
• Posttraumatic Stress Disorder
• +۱۱ مورد دیگر

Carolina Dall Antonia da Motta is a Researcher at the School of Psychology and Life Sciences (Instituto Lusófono de Investigação e Desenvolvimento), actively contributing to diverse fields including psychometrics, clinical psychology, and cybersecurity. She leads the Human Environment Interaction Lab (HEI-LAB) and participates in multiple interdisciplinary projects. Psychometrics and Clinical Assessment Cybersecurity Behavior Modeling Neurodegenerative Disease Psychology Adolescent Mental Health Virtual Reality Applications Her research spans both traditional clinical domains and emerging technological applications, with recent work focusing on emotional intelligence in adolescent self-injury, psychological impacts of multiple sclerosis, and innovative cybersecurity training simulations. She has co-authored over 33 research outputs and participated in 11 professional activities since 2021. Current projects include: Cybersecurity Awareness Training Simulator (Principal Investigator) A TEU LADO socio-emotional program for at-risk youth Severe Weather Warning Response Studies

Andrea Cerioliمشاهده پروفایل

استاد

• Robust Statistical Methodologies
• Multivariate Data Analysis
• Outlier Detection
• +۳ مورد دیگر

Andrea Cerioli is a full Professor of Statistics at the Department of Economics and Management, University of Parma, Italy, since 2002. He specializes in robust statistical methodologies, including the Forward Search approach, multivariate outlier detection, Benford’s Law, and tempered stable distributions, with applications to economic data and international trade fraud detection. Ph.D. in Statistics, University of Padua (1992) Editor-in-Chief of Statistical Methods and Applications (2011-2015) Current Editor of Advances in Data Analysis and Classification President of CLADAG (2009-2011) His research spans robust classification, clustering, and spatial data analysis. He has co-authored over 100 peer-reviewed works, including two Springer books, and has served on numerous conference committees. Recent work focuses on Benford’s Law for fraud detection and tempered stable distributions for modeling heavy-tailed data. He teaches courses like Data Science for Marketing and Statistics for Management at the University of Parma, contributing to graduate programs in trade and consumer marketing. His editorial and academic leadership roles highlight his influence in statistical methodology development.

Guido Montúfarمشاهده پروفایل

استادیار

• Mathematical Machine Learning
• Deep Learning Theory
• Graphical Models
• +۷ مورد دیگر

Guido Montúfar is Assistant Professor at the University of California, Los Angeles (UCLA) in the Departments of Mathematics and Statistics, and Research Group Leader at the Max Planck Institute for Mathematics in the Sciences (MPI MIS). His work bridges mathematical machine learning, deep learning theory, and information geometry. Current affiliations: UCLA (since 2017) and MPI MIS (since 2018) ERC Starting Grant on Deep Learning Theory Research interests focus on the interplay between model capacity, optimization landscapes , and generalization in deep learning, combining tools from algebraic geometry , optimal transport , and information theory to analyze neural network behavior. Recent publications address tropical geometry of neural networks , algebraic optimization in reinforcement learning , and information-theoretic approaches to data representation . His work reveals connections between policy gradient methods and Wasserstein gradient flows . Scientific grants: ERC Starting Grant, DFG SPP 2298, NSF CAREER

Jacky Moمشاهده پروفایل

مدرس

• Business Analytics
• Big Data Analytics
• Healthcare Services
• +۶ مورد دیگر

Dr Jacky Mo serves as a Lecturer in the School of Information Systems & Technology Management at the University of New South Wales, Australia. His academic journey includes completing a PhD in Marketing with specialization in Marketing and Data Analytics at UNSW, following an MPhil in Marketing from the same institution and a BA in Management from Sichuan Normal University. Mo's research expertise spans both conventional analytical methods including regression-based structural equation modeling and hierarchical linear modeling, and modern big-data analytics techniques such as text mining and artificial intelligence applications. His primary research focus examines behavioral decision making within business-to-business and business-to-consumer contexts, particularly in marketing distribution channels and online brand communities. His scholarly contributions have appeared in prestigious peer-reviewed journals including Industrial Marketing Management and European Journal of Marketing. Mo has successfully secured research funding, most notably the Health@Business and UNSW Medicine Collaboration Seed Funds Grant ($14,500) for 2020-2021. PhD Placement Scholarship for Research Excellence (2017) As an educator, Mo actively contributes to program and course development in Business Analytics, teaching multiple courses at both undergraduate and postgraduate levels including INFS3603 Introduction to Business Analytics, INFS3873 Business Analytics Methods, INFS5700 Introduction to Business Analytics, and INFS5720 Business Analytics Methods. His research profile indicates significant contributions across various publication formats with 1 book chapter, 5 journal articles, 4 conference papers, 1 edited conference proceedings, and 2 conference abstracts.

• Longitudinal Data Analysis
• Bayesian Analysis
• Joint Modeling
• +۵ مورد دیگر

Taban Baghfalaki is a Lecturer in Statistics with a focus on advanced statistical methodologies applied to longitudinal data analysis, Bayesian inference, and joint modeling. His work integrates complex statistical techniques to address challenges in health sciences, genetics, and epidemiology. He has contributed to developing Bayesian approaches for handling zero-inflated data, competing risks, and pleiotropy in genomic studies. Key research areas include dynamic prediction in longitudinal studies, variable selection in high-dimensional datasets, and the application of copula models for multivariate data analysis. He has pioneered tools like the GCPBayes pipeline for cross-phenotype genetic association studies, demonstrating expertise in both theoretical and applied statistics. His publications span 2011–2025, emphasizing Bayesian methods, survival analysis, and longitudinal modeling. Notable contributions include frameworks for joint modeling of longitudinal and survival outcomes, methods for analyzing skewed mixed responses, and computational tools for genomic data. Awards and grants are not explicitly mentioned in the provided texts, but his prolific publication record reflects sustained research activity. He collaborates on projects involving healthcare data (e.g., Tehran Lipid and Glucose Study) and genetic epidemiology.

Anna Wildingمشاهده پروفایل

پژوهشگر ارشد

• Health Economics
• Primary Care Workforce
• Health Inequalities
• +۵ مورد دیگر

Anna Wilding is a Research Fellow in Health Economics at the University of Manchester’s Health Organisation, Policy & Economics (HOPE) Centre within the Centre for Primary Care and Health Services Research. Her current work evaluates the national rollout of social prescribing link workers in primary care, supported by the NIHR HSDR Grant 134066. She holds a PhD in Health Economics (2022) and has received prestigious awards including the President’s Doctoral Scholar Award and Jon Stewart Prize for Econometrics. Education: PhD in Health Economics, University of Manchester (2022) MSc Economics (Economics of Health), University of Manchester (2017, Distinction) BEconSc(Hons) Economics, University of Manchester (2016, First Class) Research Interests: Health economics and policy Primary care workforce dynamics Social participation and health outcomes Health inequalities Mental health and gender disparities Econometric analysis of healthcare interventions Articles Overview: Her research focuses on evaluating healthcare interventions, particularly social prescribing, and analyzing the socioeconomic determinants of health outcomes. Key themes include geographic disparities in healthcare access, mental health impacts of family dynamics, and policy-driven changes in primary care systems. Awards: President’s Doctoral Scholar Award (2019) Jon Stewart Prize for Econometrics (2017) Dean’s Award (2016) Manchester School Award (2016) Grants & Projects: NIHR HSDR Grant 134066 (Social Prescribing Evaluation) Comparative analysis of primary care models in England/Scotland (2025–2027) Labs/Teams: Collaborates closely with the HOPE Centre and interdisciplinary teams studying healthcare policy and economics.

James Demmelمشاهده پروفایل

استاد

• Numerical Analysis
• Linear Algebra
• Scientific Computing
• +۴ مورد دیگر

James Demmel is a Professor of Computer Science and Mathematics at the University of California, Berkeley, with joint appointments since 1990. His research focuses on numerical linear algebra, high-performance computing, and parallel algorithms. He co-developed widely used libraries like LAPACK and ScaLAPACK. Demmel holds ACM, SIAM, and IEEE Fellowships, and is a member of both the National Academy of Engineering and Sciences. Education: Ph.D. in Computer Science, UC Berkeley, 1983 B.S. in Mathematics, Caltech, 1975 Research Interests: Demmel’s work emphasizes numerical methods for linear algebra, including algorithms for eigenvalue problems, matrix factorizations, and high-performance computing architectures. His contributions bridge theory and practice, addressing challenges in floating-point arithmetic and algorithm scalability. Publications & Awards: Over 150+ publications, including foundational papers on LAPACK and iterative methods. Recipient of the SIAM Activity Group Linear Algebra Best Paper Prize (1988, 1991), Wilkinson Prize (1993), and ACM Supercomputing Test of Time Award (2019). Labs & Teams: Demmel collaborates with groups like Berkeley Benchmarking and Optimization Group (BeBOP) and CLIMB, focusing on algorithmic efficiency and exascale computing.

Pietro Amentaمشاهده پروفایل

دانشیار

• Statistics
• Multivariate Analysis
• Transport Systems
• +۲ مورد دیگر

Pietro Amenta is an Associate Professor at the University of Sannio, affiliated with the Department of Law, Economics, Management and Quantitative Methods (DEMM). His academic work focuses on statistical methods, multivariate analysis, and transport systems optimization. Position: Associate Professor Department: DEMM Research: Statistics & Transport Systems Contact: amenta@unisannio.it Dr. Amenta's research interests center on Co-Inertia Analysis , Ordinal Data Handling , and Dimensionality Reduction Techniques . He has developed novel scaling methods for categorical variables and explored applications in transportation planning and social data analysis. His publications reveal trends in Transport Systems (2018-2024), Statistical Methodology (1991-2020), and Data Analysis Techniques (1994-2015). Recent work emphasizes non-negotiable group decisions in transport policy and sustainable mobility frameworks.

Philipp Sibbertsenمشاهده پروفایل

استاد

• Time Series with Long Memory
• Non-linear Time Series Econometrics
• Statistics on Financial Markets
• +۲ مورد دیگر

Professor Philipp Sibbertsen is a distinguished faculty member at the Institute of Statistics within the Faculty of Economics and Management at Leibniz University Hannover. He holds the position of Professor of Statistics since 2005 and has maintained an active research and teaching career throughout his academic journey. Professor of Statistics at Leibniz University Hannover (since 2005) Visiting Professor at CREATES, Aarhus University, Denmark (2011) Heisenberg Fellow of the Deutsche Forschungsgemeinschaft (DFG) (2004-2005) Research stay at Cardiff Business School, Cardiff University, UK (2001-2002) University Assistant at Technical University of Dortmund (2001-2004) Professor Sibbertsen's research primarily focuses on time series analysis with emphasis on long memory processes, nonlinear time series econometrics, and financial market statistics. His work bridges theoretical statistical methods with practical applications in economics and finance, particularly in the areas of fractional integration, cointegration, structural breaks, and volatility modeling. His research has significant implications for understanding market dynamics, forecasting economic trends, and developing robust statistical methodologies for complex data structures. His extensive publication record demonstrates a consistent focus on long memory processes and their applications across various domains. Recent publications (2021-2025) show his work expanding into climate data analysis, carbon pricing mechanisms, coastal risk assessment, and medical statistics, while maintaining his core expertise in time series econometrics. His research shows a strong pattern of interdisciplinary collaboration, with publications spanning economics, finance, environmental science, and medical research. Heisenberg Fellowship from DFG (2004-2005) Member of the Editorial Board of 'Statistical Papers' and 'ASta' Member of the Board of the German Statistical Society (since 2016) Professor Sibbertsen has held significant administrative roles including Spokesperson for the research focus 'Innovation and Learning' at Leibniz University Hannover (since 2018), Dean of Studies at the Faculty of Business and Economics (2011-2013), and Chairman of the Committee for Statistical Theory and Methodology of the German Statistical Society (2008-2012). His teaching portfolio includes Descriptive Statistics, Inductive Statistics, Advanced Statistics, and Time Series Analysis. While specific details about his advisees are not provided in the available materials, his extensive publication record suggests active mentorship of graduate students in statistics and econometrics.

Joona Cornerمشاهده پروفایل

پژوهشگر

• Meteorology
• Geosciences
• Physical Sciences
• +۳ مورد دیگر

Joona Corner is a Doctoral Researcher at the Institute for Atmospheric and Earth System Research (INAR) , University of Helsinki, Faculty of Science. He is also a member of the Doctoral Programme in Atmospheric Sciences . Education: Master of Science in Meteorology (University of Helsinki, 2023) Research Interests: Dynamic meteorology, classification of extratropical cyclones, intensity measures, volcanic eruption impacts on cyclone frequency, Baltic Sea extreme sea levels, cluster analysis, and meteorological modeling. His work bridges geosciences, physical sciences, and climate science. Publications (2021-2025): 9 research outputs including articles on cyclone dynamics, climate impacts, and windstorm analysis. Key collaborations with Sinclair, Bouvier, and Andreasen. Projects: Active participant in Quantifying controls on the intensity, variability and impacts of extreme European STORMS (2021–2025). Activities: Delivered oral presentations at MedCyclones Training School (2023), hosted academic visitors, and participated in international collaborations. Email: joona.corner@helsinki.fi

Yuwen Guمشاهده پروفایل

استادیار

• High-dimensional statistics
• Variable selection
• Model combination
• +۳ مورد دیگر

Yuwen Gu is an Assistant Professor in the Department of Statistics at the University of Connecticut. Their research focuses on high-dimensional statistics, variable selection, model combination, nonparametric methods, causal inference, and optimization techniques. Their recent publications highlight advancements in quantile regression, sparse modeling, and high-dimensional data analysis. Notable trends include applications of kernel methods, distributed computing for large-scale statistics, and regularization techniques for insurance and multi-source data. Yuwen Gu's work also explores interdisciplinary domains, such as bioacoustic signal processing in ecological studies, though their primary contributions remain in statistical methodology and computational efficiency.

• Paleoecology
• Fire History
• Climate Change
• +۳ مورد دیگر

Mitchell Power is a Professor at the University of Utah's School of Environment, Society & Sustainability , with expertise in paleoecology and biogeography. He maintains dual roles as Curator of Botany at the Natural History Museum of Utah and leads international research projects in Ethiopia, South Africa, Colombia, and the western U.S. Academic Pathway: University of Oregon PhD (2008), Northern Arizona University MS (1998), University of Maine BA (1992) Research Focus: Historical biogeography, climate-vegetation-fire interactions, and human impacts on ecosystem dynamics through deep time Paleofire Research Trends from his 2021-2025 publications show cross-continental analysis of fire regimes through sedimentary charcoal records and multi-proxy datasets, spanning from the Amazonian ecotone to Mediterranean olive cultivation regions. His team employs advanced dating techniques (OSL, 14C) and statistical modeling (linear regression, ecometrics) to disentangle climate and human drivers. Collaborative Network includes institutions like: National Science Foundation University of Exeter (NERC funding) USDA Forest Service DOI Bureau of Land Management Teaching Portfolio encompasses: PhD/Master's thesis supervision Paleoclimatology courses Archaeology-paleoecology methodology Global environmental patterns

• Quantitative Methods
• Statistics
• Statistical Process Control
• +۷ مورد دیگر

Emmanuel Grenier is an Assistant Professor at NEOMA Business School, specializing in Quantitative Methods and Statistics. He holds a Master's degree in Signal Processing - Statistics and has extensive experience in both academic and applied statistical research. Affiliation: NEOMA Business School (France) Academic Role: Assistant Professor Research Expertise: Bayesian Statistics, Quantitative Risk Analysis, Structural Equation Modeling His research focuses on interdisciplinary applications of statistics in business, healthcare, and food safety. Key contributions include: Developing Bayesian frameworks for risk assessment Advancing Partial Least Squares Path Modeling (PLS-PM) Consulting in Statistical Process Control for industry partners Recent publications demonstrate trends in: Bayesian networks for operational risk Behavioral prediction models Food safety epidemiology He has also contributed to statistical education through book chapters on: Descriptive statistics ANOVA techniques Regression analysis

Maria Helena Correiaمشاهده پروفایل

استادیار

• Production Planning
• Operations Research
• Paper Industry Optimization
• +۲ مورد دیگر

Maria Helena Correia is an Assistant Professor at Católica Porto Business School, Universidade Católica Portuguesa. Her research focuses on production planning, operations research, and optimization within the paper industry. She has contributed to both textbook development in quantitative methods and peer-reviewed articles addressing cutting-edge optimization challenges in manufacturing systems. Her work often bridges theoretical advancements with practical applications in paper mill operations and resource allocation. Key affiliations: Católica Porto Business School Research emphasizes production systems optimization and integer programming applications Her articles analyze complex problems like reel/sheet cutting in paper production, cylinder packing algorithms, and integrated assignment-sequencing processes. These contributions demonstrate expertise in both academic and industrial problem-solving. No formal awards are explicitly mentioned in the provided materials. She has participated in interdisciplinary activities such as the 2024 Projeto U©P2 Mental Health conference. Ongoing work maintains focus on operational efficiency in manufacturing environments.