جست‌وجوی استاد و پژوهشگر

• Multivariate Methods for Change Analysis
• Latent Class Models in Developmental Processes
• Machine Learning in Psychology
• +۳ مورد دیگر

Kevin John Grimm is a Professor in the Department of Psychology at Arizona State University (ASU), serving as Director of Operations and Research within the College of Health Solutions. He holds a B.A. in Mathematics and Psychology from Gettysburg College (2000), and M.A. (2003) and Ph.D. (2006) in Psychology from the University of Virginia. Previously, he served as faculty at UC Davis before joining ASU in 2014. His research focuses on multivariate methods for analyzing developmental change, including nonlinear growth modeling, latent class analysis, and integrating machine learning with psychological data. Notable contributions include co-authoring the textbook *Growth Modeling: Structural Equation and Multilevel Modeling Approaches* (Guilford Press, 2017). Teaches courses like Structural Equation Modeling, Longitudinal Growth Modeling, and Machine Learning in Psychology at ASU. Active in professional service: Associate Editor of *Structural Equation Modeling: A Multidisciplinary Journal* since 2012. Recipient of NIH/NIDA grants for drug abuse/HIV prevention research and NICHD-funded studies on sleep health in children. His methodological work bridges quantitative innovation with substantive developmental research, emphasizing rigorous model specification and cross-disciplinary applications.

• Structural Equation Modeling
• Psychometrics
• Statistics
• +۵ مورد دیگر

Yves Rosseel is a Professor at Ghent University (UGent) specializing in Structural Equation Modeling (SEM) , Psychometrics , and Statistical Methodology . With over 15 recent publications (2024-2025), he focuses on small-sample SEM solutions, factor score regression, measurement error, and Bayesian extensions. His work bridges Statistics with applications in Psychology , Education , and Neuroimaging . Research Trends Developed Mixture Multigroup SEM for cross-group comparisons Proposed Information-Theoretic Hypergraphs in psychometrics Advanced Two-Stage Estimation for round-robin data Created blavaan R package for Bayesian SEM Investigated Measurement Error in hypothesis testing Scientific Contributions Published 84 Social Sciences papers, 37 Statistics works, and 11 Neuroimaging studies Promoted 8 PhDs including Sara Dhaene and Julie De Jonckere Co-authored 12+ works with Marijke Welvaert and 10+ with Stijn Vanheule

Kevin Vinsenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• High-performance Computing
• Peta-scale systems
• +۷ مورد دیگر

Kevin Vinsen is a Senior Research Fellow at the University of Western Australia, working in the Data Intensive Astronomy (DIA) Program of the International Centre for Radio Astronomy Research (ICRAR) since 2009. He is also affiliated with the UWA Defence and Security Institute and holds an ORCID ID of 0000-0001-5332-3784. His work focuses on translating ICRAR software capabilities into practical industry applications across diverse domains. His research interests include: Peta-scale systems High-performance Computing Machine Learning applications in multiple fields Wave and weather forecasting Digital Assistive Technologies Agricultural applications of ML Large language models Vinsen heads the Translation and Impact work of the DIA team and leads the development of Machine Learning systems. His current projects include ML for wave forecasting on the NW shelf, wind and temperature forecasting, honey traceability and provenance, and digital assistive technology for people with disabilities. His work contributes to UN Sustainable Development Goals related to industry, oceans, agriculture, food, disability, and defense. His research output demonstrates a strong trend toward applying machine learning techniques to solve real-world problems across astronomy, environmental science, agriculture, and disability support. This interdisciplinary approach showcases the versatility of his computational expertise across scientific and social domains. Vinsen has an h-index of 11 with 621 citations across 33 research outputs. As the ICRAR/UWA Summer Studentship Co-ordinator, he mentors emerging researchers and contributes to building research capacity. His collaborative network spans multiple institutions and research areas, reflecting his ability to bridge academic research with practical applications.

Valen Johnsonمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Medical Imaging
• +۷ مورد دیگر

Valen E. Johnson is a University Distinguished Professor and Dean Emeritus of the College of Science at Texas A&M University, where he has been a faculty member since 2012. He previously held professorships at the University of Texas M. D. Anderson Cancer Center (2004–2012), the University of Michigan (2002–2004), and Duke University (1989–2001). He also served as a Technical Staff Member at Los Alamos National Laboratory (2001–2002). Johnson earned his Ph.D. in Statistics from the University of Chicago (1989), M.A. in Applied Mathematics from the University of Texas at Austin (1985), and B.S. in Mathematics from Rensselaer Polytechnic Institute (1981). His research focuses on Bayesian methodology, including hypothesis testing, variable selection in high-dimensional spaces, latent variable models, and applications in medical imaging, clinical trials, and educational assessment. He has contributed to the development of non-local prior densities and Bayesian diagnostics for MCMC convergence. His work bridges Bayesian and classical statistical approaches, emphasizing reproducibility and rigorous evidence assessment in scientific research. Johnson has held editorial roles, including Co-Editor of Bayesian Analysis (2010–2014) and Associate Editor of the Journal of the American Statistical Association (2011–present). He is a Fellow of the American Statistical Association and the Royal Statistical Society and has served on the Board of Directors of the International Society for Bayesian Analysis. His advocacy for revised statistical significance standards has sparked major debates in scientific methodology. Johnson has supervised numerous doctoral students, including those whose theses won prestigious awards like the Savage Award. He has collaborated on grants addressing medical imaging, system reliability, and cancer symptom management, reflecting his interdisciplinary impact in biostatistics and public health.

• Mental Health
• Mindfulness Training
• Educational Psychology
• +۳ مورد دیگر

Matt Allwood is a Clinical Trial Data Co-ordinator at the University of Oxford, primarily involved in mental health and educational psychology research. His work focuses on evaluating school-based interventions like mindfulness training and teacher classroom management programmes. Role: Clinical Trial Data Co-ordinator Affiliation: University of Oxford Research interests span mental health prevention in adolescents, implementation science, and educational interventions. Key projects include the MYRIAD trial assessing mindfulness training and the STARS trial examining classroom management strategies. Recent publications highlight findings that universal school-based mindfulness training may not benefit high-risk adolescents and could worsen outcomes, while pharmacist-prescriber partnerships show promise in antibiotic stewardship. His work emphasizes rigorous trial design and real-world applicability.

• Artificial Intelligence
• Machine Learning
• Computational Biology
• +۷ مورد دیگر

Dr. Antony McCabe is a Lecturer in Computer Science who divides his time between academia and the Computational Biology Facility. His work focuses on bridging artificial intelligence with biomedical challenges through software development, data analysis, and user interface design. Lecturer in Computer Science Member of Computational Biology Facility Research Interests McCabe specializes in applying AI techniques like neural networks and large language models to biomedical problems. His contributions include: Developing the lcmsWorld 3D visualization software for mass spectrometry Advancing the Allele Frequency Net Database for HLA diversity analysis Creating immunoinformatics tools for ethnicity-specific vaccine design Building infrastructure for biomedical data processing and storage Research Trends His publications reveal a consistent focus on computational biology infrastructure (4/6 papers), AI applications in immunology (3/6), and software development for proteomics (2/6). Key themes include data standardization, ethnic diversity considerations, and visualization techniques. Current Projects He is currently tuning large language models to analyze biological literature through a Biotechnology & Biological Science Research Council (BBSRC) grant (2024-2026). Teaching McCabe co-ordinates the COMP222 module on computer game design while also teaching algorithmic game theory, programming languages, and human-centric computing.

• Econometrics
• Microeconometrics
• Partial Identification
• +۳ مورد دیگر

Adam M. Rosen is a Professor of Economics at Duke University's Department of Economics, where he has served since 2019. Previously, he held academic roles at University College London (UCL), including Associate Professor (2013–2016) and Assistant Professor (2006–2013). He is an affiliated researcher with the Centre for Microdata Methods and Practice (CeMMAP) and the Institute for Fiscal Studies (IFS). His research focuses on econometric theory and applications, particularly in instrumental variable methods, partial identification, and structural models. Rosen earned his Ph.D. in Economics from Northwestern University (2006) and a B.A. in Economics and Mathematics with Computer Science from Cornell University (1999). His work has been published in top journals such as the Review of Economic Studies, Econometrica, and the Journal of Econometrics. He has received numerous awards, including Fellow of the Journal of Econometrics (2024) and the Best Associate Editor Award (2024). Rosen's research interests span econometric theory, partial identification, instrumental variables, and applied microeconometrics. His recent work includes advancements in IV methods for Tobit models, discrete choice models, and counterfactual analysis. He has advised over a dozen PhD students and contributes actively to professional service, including editorial roles at the Journal of Econometrics and Econometrics Journal. He has held grants from the European Research Council and ESRC, among others, and his teaching spans advanced econometrics courses at both UCL and Duke. His affiliations with CeMMAP and IFS reflect his commitment to advancing microdata methods and policy analysis.

• Econometrics
• Econometric and statistical methods
• Economics
• +۳ مورد دیگر

Andrew Chesher is the William Stanley Jevons Professor of Economics and Economic Measurement at University College London (UCL) and holds Honorary Professor status at Beihang University. He previously served as Professor of Econometrics at the University of Bristol (1984–1999) and Lecturer in Econometrics at the University of Birmingham (1971–1983). He earned a Bachelor of Social Science from the University of Birmingham (1970) and a Doctor of Science, Honoris Causa from the University of Birmingham (2017). His research focuses on econometric methods, structural modeling, measurement error analysis, and applied microeconometrics. Key contributions include work on instrumental variable methods, panel data analysis, and applications in transportation economics, population policy, and market structure. Chesher has advised international agencies like the World Bank and UNDP, contributing to real-world policy solutions. He is a Fellow of the British Academy, Econometric Society, and Academy of Social Sciences. Chesher founded UCL’s Centre for Microdata Methods and Practice (2000), a global leader in econometric methodology. His awards include the 2017 Doctor of Science (honoris causa) and leadership roles such as President of the Royal Economic Society (2016–2018). Research highlights include modeling highway maintenance costs in Brazil/India (World Bank), Malaysia’s marriage/fertility policies (UNDP), and private-sector efficiency studies with firms like EDF Energy and regulators like Ofcom. His work bridges theory and practice, emphasizing robust methods for endogeneity, discrete choice models, and incomplete data scenarios.

C. Colt Andersonمشاهده پروفایل

استاد

• Historical Theology
• Church Reform
• Christian Spirituality
• +۳ مورد دیگر

C. Colt Anderson is a Professor at Fordham University, currently listed in the Graduate School of Education faculty directory. He previously served as Academic Dean of Fordham's Graduate School of Religion and Vice President of Academic Affairs at the Washington Theological Union, bringing extensive administrative experience to his academic role. His educational background includes: BA from the University of Georgia MA in Religious Studies from the University of Georgia PhD in Historical Theology from Marquette University (1998) Professor Anderson's research centers on historical theology , church reform , and eschatological perspectives , with particular focus on: The early Franciscan movement as theological framework Contemporary religious nationalism Catholic sexual abuse crisis analysis Christian spirituality and ecclesial leadership Communication of the Gospel in modern contexts His publication trajectory reveals a significant evolution from medieval Franciscan studies toward urgent contemporary issues. Recent works increasingly address institutional failures in religious organizations, faith's relationship to political systems, and crisis response mechanisms within ecclesial structures, demonstrating consistent application of historical insights to modern challenges. Professor Anderson has received notable recognition including: Catholic Press Association award for history (2007) for 'The Great Catholic Reformers' Bronze Award for political science (2021) for 'Faith, Nationalism, and the Future of Liberal Democracy' Honorable mention from The Catholic Media Association (2021) Prior to academia, he served as theological consultant to the Democratic Party of Georgia and worked as an award-winning scriptwriter and film producer, demonstrating interdisciplinary engagement beyond traditional academic boundaries. Information regarding student advising and research grants is not documented in available sources.

• Human-Environment Interactions
• Urban Ecology and Sustainability
• Environmental Geography and Governance
• +۳ مورد دیگر

Kelli Larson is a Professor at Arizona State University (ASU), holding dual appointments in the School of Geographical Sciences and Urban Planning and the School of Sustainability. She is affiliated with multiple research centers, including the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) project, where she serves as Lead Social Scientist. Her research focuses on human-environment interactions, urban sustainability, and the social dimensions of ecological conservation. Dr. Larson earned a Ph.D. in Resource Geography from Oregon State University (2005) and has been at ASU since that time. Her work integrates mixed-methods approaches to study residential landscape management, urban biodiversity, and public perceptions of ecosystem services. She directs the Phoenix Area Social Survey, a key initiative for CAP LTER's long-term data collection. Her research interests include urban ecology, environmental governance, and the social drivers of landscape choices. Notable contributions examine how residents’ attitudes toward wildlife, vegetation, and water governance influence urban sustainability outcomes. She has led NSF-funded projects and published extensively on topics like yard ordinances, biodiversity in urban areas, and climate adaptation. Larson mentors undergraduates, graduate students, and postdocs, emphasizing undergraduate research through programs like the Sustainability Undergraduate Research Experience (SURE). She has received awards for mentoring and service, including the Outstanding Service Award (2019) and Distinguished Alumni Award (2018). Her work bridges academia and public engagement, addressing challenges such as urban heat mitigation, green infrastructure planning, and equitable access to urban ecosystems. She serves on editorial boards and professional associations, advocating for interdisciplinary approaches to environmental challenges.

• Computer Science
• Economics
• Mathematics
• +۳ مورد دیگر

Assoc Prof Xiaohui Bei is an Associate Professor at Nanyang Technological University (NTU), holding dual appointments in the School of Physical & Mathematical Sciences (Division of Mathematical Sciences) and the College of Computing & Data Science. His research focuses on algorithmic game theory, fair division, auction design, and computational economics. He earned his Ph.D. from Tsinghua University in 2012 under Prof. Andrew Yao and held postdoctoral positions at NTU and the Max Planck Institute for Informatics. His work bridges theoretical computer science and economics, addressing challenges in fair resource allocation, truthful mechanisms, and market design. Key interests include cake-cutting protocols, auction optimization, and voting systems with mixed goods. He has contributed to foundational studies on Nash welfare, submodular valuations, and connectivity constraints in fair division. Prof. Bei’s research is supported by grants exploring topics such as optimal auction mechanisms and dynamic resource allocation. His publications often emphasize practical algorithmic solutions to complex economic challenges, ensuring both efficiency and fairness.

• Machine Learning
• Statistical Computing
• Cluster Analysis
• +۲ مورد دیگر

Theresa Scharl-Hirsch is a Senior Scientist and Deputy Scientific Director at the Core Facility Bioinformatics, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds concurrent appointments at the Institute of Statistics, BOKU, and has extensive experience in bioprocess modeling, machine learning, and statistical computing. Her work bridges biochemical engineering with advanced data science methodologies. Her research focuses on real-time monitoring of biopharmaceutical processes, clustering of high-dimensional data (particularly RNA sequencing), and application of explainable machine learning techniques. She has developed statistical models for process optimization and quality prediction in antibody capture and protein purification, with a strong emphasis on industrial implementations using R programming. Key trends in her publications include three-way data analysis, matrix-variate Gaussian mixture models, and permutation-based variable importance methods for deep learning architectures. Her work spans bioprocess engineering, bioinformatics, and industrial data science applications.

Leila Moslemi Naeniمشاهده پروفایل

مدرس ارشد

• Project Management
• Fuzzy Methods
• Mathematical Modelling
• +۵ مورد دیگر

Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.

Saumen Mandalمشاهده پروفایل

استاد

• Optimal experimental design
• Biostatistics
• Data science
• +۷ مورد دیگر

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

• Complex Systems Dynamics
• Information Theory
• Neuroscience
• +۵ مورد دیگر

Leonardo Ricci is an Associate Professor at the Department of Physics, University of Trento , with a 28-year teaching career spanning 56 courses (31 in English) and extensive roles in the Interdepartmental Center for Mind/Brain Sciences - CIMEC (30% affiliation). His research bridges nonlinear dynamics , information theory , and neuroscience , focusing on chaos detection in time series and entropy analysis. Academic Career : From 1994 post-doc at Max-Planck-Institut to 2022 promotion to Associate Professor Teaching : Courses in Experimental Physics, Advanced Electronics, and Statistical Methods across Physics and Computer Science programs Ricci leads the NSE Lab (Nonlinear Systems and Electronics) , developing hardware/software systems for experimental research. His 2022 Entropy cover story on permutation entropy highlights his impact in information theory. Scientific Contributions : 20 patents (visibility measurement devices), collaborations with international researchers on complex systems Editorial Roles : Associate Editor for Chaos, Solitons & Fractals and Frontiers in Network Physiology