جست‌وجوی استاد و پژوهشگر

Lynne Steuerle Schofieldمشاهده پروفایل

استاد

• Statistics
• Public Policy
• Cognitive Science
• +۱ مورد دیگر

Lynne Steuerle Schofield is a Professor in the Department of Mathematics and Statistics at Swarthmore College, with additional affiliations to Cognitive Science. She holds a Ph.D. in Statistics and Public Policy from Carnegie Mellon University (2008). Previously, she served as Associate Dean of Faculty for Diversity, Recruitment, and Retention at Swarthmore, and held positions at Temple University and the National Academies of Sciences. Her research focuses on statistical methodology , public policy analysis , and cognitive science applications . Specific interests include survey methodology, cognitive decline measurement, and environmental policy analysis. Her extensive publication record demonstrates consistent focus on statistical applications in social sciences, with recent work covering: Cognitive function measurement techniques Environmental policy and climate governance Methodological improvements in latent variable modeling Educational assessment and psychometrics She maintains active collaborations through the National Opinion Research Center where she serves as Senior Fellow.

Nicholas Riosمشاهده پروفایل

استادیار

• Experimental Design
• Computational Statistics
• Functional Data Analysis
• +۵ مورد دیگر

Nicholas Rios is an Assistant Professor of Statistics at George Mason University (GMU), where he has been affiliated since August 2022. He holds a PhD in Statistics from The Pennsylvania State University (2022), an MS from Montclair State University, and a BS from the University of Delaware. His research focuses on experimental design under real-world constraints, computational statistics, functional data analysis, Gaussian processes, and compositional data analysis, with applications in chemical engineering and pharmaceuticals. Dr. Rios teaches applied statistics (STAT 554) and emphasizes critical thinking in statistical methodology. His doctoral work developed novel algorithms for efficient mixture experiments, particularly addressing the impact of component addition order. His current research bridges theoretical statistical methods with practical industrial challenges, leveraging his expertise in high-dimensional data and constrained experimental designs. His recent publications (2022–2025) explore advanced experimental design techniques, bootstrap aggregation for generalized linear models, and compositional data analysis in chemical recycling. While no scientific awards are explicitly mentioned, his work demonstrates significant contributions to optimizing experimental setups in constrained environments. Dr. Rios’ advising and grants are not detailed in the provided texts, though his CV (linked) may provide further insights into collaborative projects or funding. He actively contributes to statistical education and maintains a personal website for professional engagement.

Daniel Roweمشاهده پروفایل

استاد

• Bayesian Statistics
• Computational Neuroscience
• Medical Imaging
• +۳ مورد دیگر

Dr. Daniel Rowe is a Professor of Data Science and Co-Director in the Department of Mathematical and Statistical Sciences at Marquette University. His research focuses on Bayesian statistics, computational neuroscience, and medical imaging, particularly in fMRI analysis. He has contributed extensively to improving fMRI signal processing techniques, including Bayesian approaches for brain activity mapping and parallel imaging reconstruction methods like GRAPPA and SENSE. His work emphasizes statistical methodologies to address challenges in neuroimaging, such as noise reduction, artifact correction, and the development of complex-valued analysis frameworks. Notable contributions include the Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for enhanced fMRI detection and efficient fully Bayesian approaches for brain activity mapping. Dr. Rowe collaborates on interdisciplinary projects, including cardiac chamber modeling using Bayesian neural networks and studies on radiation-induced cardiotoxicity. His publications span high-impact journals like the Annals of Applied Statistics, Magnetic Resonance Imaging, and the Journal of the Royal Statistical Society.

Brian Habingمشاهده پروفایل

دانشیار

• Psychometrics
• Item Response Theory (IRT)
• Multivariate Analysis
• +۳ مورد دیگر

Brian Habing is an Associate Professor in the Department of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He joined the university in 1998 following his PhD in Statistics from the University of Illinois at Urbana-Champaign. His primary roles include directing the undergraduate programs in Data Analytics and Data Science, serving as an associate director at the National Institute of Statistical Sciences, and acting as an associate editor for Applied Psychological Measurement . His research focuses on psychometrics, particularly multidimensional item response theory (IRT), scale construction, and applications in educational and psychological testing. Key areas include computer adaptive testing, differential item functioning, and dimensionality assessment in testing. He has also contributed to statistics education, developing curricula at both undergraduate and graduate levels. Habing has advised or co-advised sixteen PhD students and maintains active involvement in academic communities through journal contributions and professional organizations. His work bridges statistical theory and practical applications in educational measurement, emphasizing latent variable modeling and test design.

Xiaoyan Iris Linمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Survival Analysis
• Medical Diagnostic Accuracy
• +۶ مورد دیگر

Xiaoyan Iris Lin is an Associate Professor in the Department of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. She joined the university in 2008 after earning her Ph.D. in Statistics from the University of Missouri-Columbia. Her research focuses on Bayesian statistical methodologies with applications in survival analysis, medical diagnostic accuracy, and ROC analysis. Key interests include developing advanced techniques for censored and ordinal data, such as latent graphical models for ordinal variable dependencies, neural networks for ordinal classification, and Bayesian Additive Regression Trees (BART) for survival data analysis. Dr. Lin's work challenges conventional approaches in statistical modeling, emphasizing innovative applications in healthcare and medical diagnostics. Her office is located in LeConte 209B, and further details can be found on her website or via her curriculum vitae.

Wolfgang Wiedermannمشاهده پروفایل

استاد

• Causal Inference
• Person-Oriented Research
• Longitudinal Data Analysis
• +۳ مورد دیگر

Wolfgang Wiedermann is a Professor and William A. and Jean S. Stauffer Faculty Fellow in the Department of Educational, School & Counseling Psychology at the University of Missouri's College of Education & Human Development. He holds a Ph.D. in Quantitative Psychology from the University of Klagenfurt, Austria. His research focuses on: Developing statistical methods for causal inference and direction dependence analysis Person-oriented research methodologies Analysis of intensive longitudinal data Risk perception in substance use and health contexts His publications demonstrate strong emphasis on: Advanced psychometric modeling (55% of recent articles) Causal inference techniques (30%) Person-centered analytical approaches (15%) with consistent applications in psychological and behavioral sciences. Awards include the William A. and Jean S. Stauffer Faculty Fellowship. He serves as associate editor for Behaviormetrika and Journal of Person-Oriented Research .

Bin Yangمشاهده پروفایل

استاد

• Computer Science
• Data Management
• Machine Learning
• +۹ مورد دیگر

Bin Yang is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, artificial intelligence, and machine learning, with emphasis on spatiotemporal data analysis, representation learning, and time series forecasting. He leads projects like aSTEP (spatio-temporal data analytics) and Light-AI for Cognitive Power Electronics. Key contributions include advancements in trajectory data processing, anomaly detection, and lightweight neural architectures. Research interests span machine learning, data mining, intelligent transport systems, and material science applications. Notable awards include the IJCAI 2019 Distinguished PC Member and Sapere Aude Research Leader (2018). He has supervised six PhD students and contributed to over 119 publications. His work is supported by grants from Villum Foundation and EU initiatives. Yang is involved in interdisciplinary collaborations, including data-driven decision-making frameworks and environmental monitoring systems. Labs and teams include the Daisy Center for Data-intensive Systems and AI for the People initiatives. Projects emphasize real-world applications in smart cities, traffic forecasting, and sustainable technologies. His research bridges theoretical foundations with practical implementations in domains like oceanography and crowdsourcing systems.

Samuele Tosattoمشاهده پروفایل

استادیار

• Reinforcement Learning
• Robotics
• Machine Learning
• +۶ مورد دیگر

Samuele Tosatto is an Assistant Professor in the Computer Science Department at the University of Innsbruck and affiliated with the Digital Science Center. His research focuses on enabling robots to learn in real-world environments through reinforcement learning and abstraction techniques, addressing challenges in scalability and efficiency. Ph.D. in Computer Science from Technical University of Darmstadt (2020) M.Sc. and B.Sc. in Software Engineering from Polytechnic University of Milan His work emphasizes theoretical aspects of reinforcement learning, particularly policy gradient estimation and temporal-difference methods. Recent publications highlight innovations in data-driven teleoperation, movement primitives, and off-policy optimization, aligning with his goal of bridging simulation-to-reality gaps. He teaches courses including Introduction to Robotics, Optimization and Numerical Computation, Machine Learning, and Data Analysis II. His research is disseminated via platforms like Google Scholar and samueletosatto.com.

• Sexual violence risk assessment
• Psychopathy
• Recidivism prediction
• +۴ مورد دیگر

Mark Olver is a Professor and Registered Doctoral Psychologist at the University of Saskatchewan's College of Arts and Science, Department of Psychology and Health Studies. He specializes in forensic psychology, focusing on sexual violence risk assessment, psychopathy, and recidivism prediction. His work integrates clinical practice with research, particularly in evaluating treatment efficacy for justice-involved individuals and assessing risk factors among incarcerated populations. Educated at Simon Fraser University (B.A.) and the University of Saskatchewan (Ph.D.), Olver has extensive clinical experience with correctional clients, including roles at the Regional Psychiatric Centre and Stony Mountain Institution. His research spans over 20 years, with over 150 peer-reviewed publications and book chapters. Key areas include psychopathy's role in treatment outcomes, sexual offender rehabilitation, and culturally sensitive risk assessment. Olver currently supervises graduate students in clinical psychology and applied social psychology, guiding projects on topics like intimate partner violence risk assessment and psychopathy's impact on therapeutic change. His work emphasizes evidence-based practices, with contributions to national and international correctional policy. Recent projects include validating risk assessment tools (e.g., VRS-SO), examining recidivism trends among Not Criminally Responsible populations, and exploring public perceptions of sex offenders. He collaborates with Correctional Service of Canada and academic institutions to bridge research and clinical practice.

Alfò Marcoمشاهده پروفایل

استاد

• Statistics
• Causal Inference
• Socio-Economic Data Analysis
• +۳ مورد دیگر

Marco Alfò is a Full Professor at the Department of Statistical Sciences, Sapienza University of Rome. His research focuses on mixture models, latent variable approaches, and causal inference applied to socio-economic data analysis. He coordinates the FIRB Futuro in Ricerca 2012 project titled "Mixture and latent variable models for causal inference and analysis of socio-economic data." His office is located at Piazzale Aldo Moro 5, in room 19 on the 4th floor. Contact details include institutional email and phone number +39 06 49910488. Teaching responsibilities include the Master's program in Statistical Sciences, with regular office hours on Tuesdays and Thursdays from 12:00 to 16:00. Research interests emphasize statistical methodologies for complex data structures, with applications in social sciences and economics. The ongoing grant-funded project highlights his expertise in advancing statistical techniques for causal analysis and socio-economic data interpretation.

John Gleesonمشاهده پروفایل

استاد

• Youth Mental Health
• Digital Interventions
• Psychosis
• +۲ مورد دیگر

Professor John Gleeson is Director of the Healthy Brain and Mind Research Centre at Australian Catholic University and Program Lead for Psychological Mechanisms and Innovations. A clinical psychologist with 30 years' experience, his research develops digital interventions for youth mental health including the Moderated Online Social Therapy (MOST) platform. He holds over AUD$8 million in research funding and has published extensively on psychosis interventions. Research Focus: Professor Gleeson specializes in psychological interventions for psychosis and borderline personality disorder using digital technologies. His MOST platform integrates peer support and therapy for young people, with adaptations for depression, social anxiety, and early psychosis. Publications: His recent work includes clinical trials of digital interventions for psychosis, meta-analyses of mindfulness therapies, and studies on loneliness and social functioning in mental health. Research consistently demonstrates the efficacy of technology-assisted therapies for improving mental health outcomes. Affiliations: Editorial boards of Early Intervention in Psychiatry, Psychiatric Rehabilitation Journal, and Psychosis. Registered psychologist with the Victorian Psychological Council and Fellow of the American Psychological Society.

Tianshu Fengمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Modeling
• Explainable AI
• +۳ مورد دیگر

Tianshu Feng is an Assistant Professor in the Department of Systems Engineering and Operations Research at George Mason University's College of Engineering and Computing. His research focuses on data-driven approaches in machine learning, statistical modeling, and explainable AI, with applications in healthcare, finance, transportation, and bioinformatics. PhD in Industrial Engineering, University of Washington BS in Statistics, University of Science and Technology of China His work emphasizes developing flexible and robust models through interdisciplinary collaboration. Key research areas include model fairness, active learning, and data exploration. Recent projects include predicting drug responses using neural networks and improving credit decision transparency via Shapley decomposition. His publications span topics like federated learning privacy, zero-inflated transportation models, and gene-environment interactions in diabetes research. He previously worked as a Quantitative Analytics Specialist at Wells Fargo before joining academia.

D. Betsy McCoachمشاهده پروفایل

استاد

• Educational Measurement
• Latent Variable Modeling
• Multilevel Modeling
• +۵ مورد دیگر

Dr. D. Betsy McCoach is a Professor of Educational Psychology and Research Methods at the University of Connecticut's School of Education. She specializes in quantitative research methodologies, including latent variable modeling, multilevel modeling, and educational measurement. Her work focuses on gifted education, underachievement, and open science practices. She contributes to the M.S. in Data Science program and chairs the Modern Modeling Methods conference. Roles: Professor of Educational Psychology, Co-Principal Investigator on federally funded grants (e.g., National Center for Research on Gifted Education), and Board Member of the National Association for Gifted Children (NAGC). Research Interests: Gifted education disparities, longitudinal analysis, instrument design, and addressing equity in public education. Her work integrates statistical rigor with real-world educational challenges. Grants & Leadership: Led projects like Project NEEDs2 (vocabulary intervention), SLAC (NSF-funded), and PAPER (training in psychometrics). Served as co-editor of Gifted Child Quarterly and founding co-editor of Journal of Advanced Academics . Awards: 2022 NAGC Distinguished Scholar Award and Fellow of APA's Division 5 (Educational Psychology). Labs/Groups: Modern Modeling Methods Conference organizer and collaborator on initiatives like the National Exploration of Emotional/Behavioral Detection in School Screening (NEEDs2). Her recent work emphasizes equity in gifted identification, teacher bias in rating scales, and methodological advancements in mixture models. She advocates for reproducible research practices and has authored/co-authored over 100 publications, including seminal textbooks on multilevel modeling and gifted education.

Steffen Pauwsمشاهده پروفایل

استاد

• Healthcare Risk Modeling
• Clinical Communication
• Personalized Medicine
• +۲ مورد دیگر

Steffen Pauws is a Professor in Risk Modeling and Communication at Tilburg University and a Principal Scientist at Philips Research. His work integrates data science, AI, and clinical expertise to develop personalized tools for cancer treatment communication, leveraging Dutch cancer registries and NLP for automated risk narratives. Research examines how uncertainty in risk estimates affects patient decisions, with experimental studies on colorectal cancer scenarios. Grants include EU projects on health informatics. He teaches data-driven healthcare courses and leads executive programs on medical AI.

Oludare Ariyoمشاهده پروفایل

مدرس

• Biostatistics
• Data Science
• High-Dimensional Data Analysis
• +۴ مورد دیگر

Dr. Oludare Ariyo is a Lecturer in the Department of Mathematical Sciences at the University of Essex, part of the School of Mathematics, Statistics and Actuarial Science (SMSAS). He holds a PhD in Biostatistics from KU Leuven (2020) and has extensive experience in academia, including a previous position as Lecturer in Statistics at the Federal University of Agriculture, Abeokuta (2013–2022), and a Research Associate role at KU Leuven’s Department of Public Health and Primary Care (2016–2020). Education: PhD (Biostatistics), KU Leuven, Belgium (2020) M.Sc. (Statistics), Federal University of Agriculture (2010) B.Sc. (Mathematical Sciences), Federal University of Agriculture (2005) Research Interests: Dr. Ariyo focuses on statistical methodology in health, environment, agriculture, and social sciences. His work emphasizes Bayesian model selection for longitudinal and mixed-effects models, high-dimensional data analysis, survival analysis, and applications in biostatistics and data science. He currently leads a Royal Society-funded project developing structured covariance Bayesian models and collaborates with institutions like the International Institute of Tropical Agriculture. Grants & Supervision: Recipient of Royal Society funding for Bayesian covariance modeling research Open to PhD supervision in Bayesian statistics, biostatistics, mixed models, and machine learning Labs & Collaborations: Engaged in interdisciplinary projects at the intersection of statistics, agriculture, and public health, with past affiliations at the National Horticultural Research Institute (NIHORT), Ibadan, Nigeria.