جست‌وجوی استاد و پژوهشگر

• Urban Mobility
• Smart Cities
• Transportation Policy
• +۷ مورد دیگر

Sanaz Nikfalazar is a Teaching Fellow in the Department of Human Centred Computing at Monash University. Her work spans urban mobility, data science, and transportation policy, with a focus on smart cities and sustainable urban development. Her research interests include urban mobility , data imputation , autonomous vehicles , and informal housing practices in Australian cities. She applies computational methods such as decision trees and fuzzy clustering to real-world urban challenges, contributing to policy-relevant insights in transportation and city planning. The recent trend in her publications shows a strong interdisciplinary focus, combining computer science techniques with urban policy analysis. Her work contributes to UN Sustainable Development Goals related to sustainable cities and communities. Scientific contributions include several peer-reviewed articles and conference papers. Notable outputs include research on city mobility indices, missing data imputation, and transport policy for autonomous vehicles. She has collaborated with researchers across institutions on topics related to urban informality and transportation systems. While no formal advising or grant information is available, her consistent research output indicates active engagement in academic and applied research projects.

Kim May Leeمشاهده پروفایل

پژوهشگر ارشد

• Clinical Trial Methodology
• Adaptive Designs
• Platform Trials
• +۴ مورد دیگر

Kim May Lee is a Research Fellow at King’s College London (since 2021), specializing in clinical trial methodology. She previously held roles as Lecturer in Medical Statistics at Queen Mary University of London (2020–2021) and Research Associate at the MRC Biostatistics Unit, University of Cambridge (pre-2020). She earned her PhD in Statistics from the University of Southampton, following an undergraduate degree in Mathematics with Actuarial Science from the same institution. Her research focuses on advancing clinical trial methodologies, particularly adaptive designs, platform trials, and subgroup analysis. Key interests include improving trial efficiency through personalized approaches, handling missing data, and optimizing experimental designs. She also provides statistical consultation for clinical trials and has lectured in medical statistics across multiple institutions. Recent publications emphasize innovative trial designs, sample size calculations, and methodological challenges in adaptive approaches. Notable work includes evaluating the Personalised Randomized Controlled Trial (PRACT) framework and exploring the impact of heterogeneity in platform trials. Her expertise bridges statistical rigor with practical clinical applications, addressing gaps in trial conduct and data quality. Current research trends highlight collaboration with multi-disciplinary teams to enhance methodological solutions for modern clinical challenges.

• Biostatistics
• Medical Statistics
• Clinical Trials
• +۷ مورد دیگر

Birgit Debrabant is an Associate Professor at the Department of Mathematics and Computer Science, University of Southern Denmark, with additional roles in Data Science and the STEM Center for Educational Research – FNUG. Her interdisciplinary work bridges statistical methodology with applications in neuroscience, clinical medicine, and public health. Department: Department of Mathematics and Computer Science Research Centers: STEM Center for Educational Research – FNUG Professional Affiliation: Active in Danish Society for Theoretical Statistics Her research focuses on biostatistics in clinical contexts, particularly neurosurgical outcomes, chronic subdural hematoma, intraventricular hemorrhage, and public health interventions. She employs advanced statistical methods including randomized trials, observational studies, meta-analyses, and genetic association studies. Her work often involves large-scale registry data and methodological innovation in handling complex health data. The 15 most recent publications reveal a strong trend in medical statistics, especially in neurosurgery and public health. Topics include optimal drainage times, antithrombotic therapy, BMI policy impacts, and statistical methods for DNA methylation and hidden population estimation. These reflect a cohesive research program applying rigorous statistical science to pressing clinical and public health questions. She has no listed scientific awards in the provided text. Birgit Debrabant actively contributes to teaching and curriculum development, particularly in biostatistics and applied statistics for health sciences. She has led or co-taught courses such as 'Advanced Biostatistical Methods in Health Sciences' and 'Theory of Science and Statistics'. While no formal student advising or grant information is mentioned, her involvement in multi-center trials and educational initiatives suggests collaborative leadership. She participates in national and international conferences, including DAGStat and the International Workshop on Applied Probability. She is involved in collaborative research networks, particularly in theoretical statistics and health data science, and contributes to professional events as both organizer and presenter.

• Sports Informatics
• Sports Games Research
• Data Analytics in Sports
• +۵ مورد دیگر

Manuel Bassek is a PhD student and researcher at the Institute of Exercise Training and Sport Informatics, German Sport University Cologne, specializing in sports informatics and sports games research. His work focuses on data-driven analysis of team sports, particularly soccer and handball. His research interests span Sports Informatics , Machine Learning Applications in Sports , and Team Behavior Analysis . Bassek investigates spatiotemporal patterns in elite soccer, develops machine learning approaches for sports forecasting, and analyzes space control in football. His work combines computer science techniques with sports science to extract meaningful insights from complex sports data. Bassek's publication portfolio shows a strong focus on data analytics in sports , with recent work appearing in high-impact venues including Nature, Scientific Data . His research demonstrates expertise in handling complex sports datasets, developing analytical frameworks for team sports, and applying computational methods to understand player and team performance. He is actively involved in two major research projects: ITSR (Integrative Team Sport Research) (ongoing since 2020) and floodlight – Rahmenbedingungen für datenbasierte Sportspielanalyse (2021-2024). These projects focus on data analytics in team sports, with particular emphasis on training methodologies, group behavior analysis, and performance metrics. Bassek collaborates extensively with researchers across the university, most notably with Dominik Raabe (8 shared publications, 2 shared projects), Albert Deuker (2 shared publications, 1 shared project), and Ashwin Phatak (1 shared publication, 1 shared project). His research network spans multiple institutes including the Institute of Professional Sport Education and Sport Qualifications and the Institute of Sport Economics and Sport Management.

Pedro Simões Coelhoمشاهده پروفایل

استاد

• Statistics
• Sampling Techniques
• Structural Equation Modeling
• +۱۲ مورد دیگر

Pedro Simões Coelho is a Full Professor and President of the Scientific Board at NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, where he also serves as an Integrated Researcher in the Information Management Research Center (MagIC). He is a senior expert for the European Commission in statistical methods and sampling techniques, and holds leadership roles in several national and European bodies, including the European Master of Official Statistics (EMOS) board and the Portuguese Health Technologies Commission. His research interests lie at the intersection of statistics, data analysis, and public policy. He specializes in sampling techniques, structural equation modeling, survey methodology, and data quality, with applications in health economics, business intelligence, and official statistics. His work bridges theoretical statistical innovation with real-world policy impact, particularly in health and administrative systems. His recent publications demonstrate a strong trend toward integrating artificial intelligence and advanced modeling techniques into public administration and health policy. Topics include AI-based detection of legislative burdens, economic burden assessments in dermatology, consumer segmentation via social media, and cardiovascular health optimization. These works reflect a multidisciplinary approach combining statistics, machine learning, and domain-specific knowledge to solve complex societal challenges. Senior Expert, European Commission (Statistical Methods & Sampling) Member, EMOS Board Member, Ischools Accreditation Committee Head of Information and Statistics, NOVA Clinical Research Unit (NOVA CRU) Former President, Portuguese Association for Classification and Data Analysis (CLAD) Former Member, Portuguese High Council for Statistics (CSE) Former President, Fiscal Board, CESD-Lisboa He has supervised numerous graduate and undergraduate courses and has been a consultant and trainer for major institutions including Eurostat, the Portuguese Statistical Office, and the Portuguese Central Bank. His work includes over 100 peer-reviewed publications and around 200 research projects resulting in more than 500 reports. He has delivered nearly 100 invited talks and conference presentations worldwide. He is actively involved in research labs and teams such as MagIC and NOVA CRU, where he leads initiatives in data-driven public health and statistical innovation. His ongoing projects focus on AI for policy assessment, sustainable health systems, and advanced statistical modeling for small area estimation and data integration.

• Business Analytics
• Computer Information Systems
• Predictive Analytics
• +۵ مورد دیگر

Richard V. McCarthy is a Professor of Business Analytics and Information Systems and Associate Dean for the School of Business at Quinnipiac University. His work bridges academia and industry through predictive analytics, data management, and virtual team pedagogy. BS, Central Connecticut State University MBA, Western New England College DBA, Nova Southeastern University Dr. McCarthy's research focuses on healthcare analytics, financial fraud detection, and data science pedagogy. He applies machine learning to prevent post-surgical falls, combat money laundering, and enhance educational strategies. His publications emphasize predictive modeling techniques, including decision trees, neural networks, and regression analysis, across healthcare, finance, and information systems domains. Computer Educator of the Year, International Organization for Computer Information Systems (2019)

• Graph Neural Networks
• Time Series Analysis
• Anomaly Detection
• +۵ مورد دیگر

Cesare Alippi is a Professor at Politecnico di Milano in the Department of Electronics, Information and Bioengineering within the School of Industrial and Information Engineering. With an extensive publication record spanning over three decades from 1991 to 2025, he has established himself as a leading researcher in machine learning, particularly in graph neural networks, time series analysis, and anomaly detection. His research interests focus on developing advanced machine learning methodologies for time series analysis, graph-based learning, and anomaly detection systems. Professor Alippi's work bridges theoretical foundations with practical applications across various domains including smart systems, healthcare, and cybersecurity. His recent publications demonstrate a strong emphasis on graph-based approaches to time series forecasting, structural anomaly detection, and spatiotemporal modeling. His publication trends from 2023-2025 reveal a concentration on graph neural networks for time series applications, with significant contributions to forecasting, anomaly detection, and representation learning. These works often address challenges in spatiotemporal data, missing values, and structural pattern recognition, demonstrating both theoretical depth and practical utility. Professor Alippi has mentored numerous researchers who have become prominent collaborators, including Andrea Cini, Daniele Zambon, and Lorenzo Livi, among others. His collaborative network extends across multiple institutions and research domains.

• Cultural Algorithms
• Evolutionary Computation
• Artificial Intelligence
• +۵ مورد دیگر

Robert G. Reynolds is a Professor of Computer Science at Wayne State University's College of Engineering, where he leads pioneering research in Cultural Algorithms and evolutionary computation. With over 197 publications spanning from 1982 to 2024, he has established himself as a leading authority in computational intelligence and socially-inspired optimization techniques. His work bridges computer science, social modeling, and practical applications across diverse domains including education, healthcare, and archaeology. Reynolds' research interests center around Cultural Algorithms, which he developed as a framework for modeling how cultures solve problems through social learning and knowledge transmission. His work extends to evolutionary computation, swarm intelligence, and agent-based modeling, with applications ranging from virtual reality educational systems to medical imaging analysis. He has published the influential book 'Culture on the Edge of Chaos - Cultural Algorithms and the Foundations of Social Intelligence' (2018) that formalized his theoretical contributions. His recent publications (2022-2024) demonstrate continued innovation in applying Cultural Algorithms to diverse challenges including STEM education through virtual reality environments, medical image analysis, economic modeling, and climate change impact assessment. These works consistently show his approach of integrating social dynamics into computational problem-solving frameworks. Among his notable scientific contributions is the development of the Cultural Algorithms framework as a comprehensive approach to optimization and problem-solving that incorporates social dimensions often missing in traditional evolutionary algorithms. His work has influenced numerous researchers in the field of computational intelligence. Professor Reynolds has mentored numerous students who have become frequent collaborators, including Mostafa Z. Ali, Thomas Palazzolo, Sarah Saad, and Chencheng Zhang. His research has been supported by various grants that have enabled the development of projects like the Deep Dive Virtual Reality system for exploring ancient submerged civilizations and educational applications of AI. His laboratory work focuses on developing the Cultural Algorithms Toolkit (CAT) and applying it to real-world problems through virtual environments that simulate social and cultural dynamics. Current projects include extending Cultural Algorithms for trustable AI systems and developing more sophisticated models of cultural knowledge transmission.

Gary F. Templetonمشاهده پروفایل

استاد

• Organizational Learning
• Construct Development
• Messy Data Problems
• +۴ مورد دیگر

Gary F. Templeton is a Professor of Management Information Systems (MIS) at West Virginia University's John Chambers College of Business and Economics. He holds a PhD in MIS from Auburn University and has previously held faculty positions at the University of Alabama in Huntsville and Mississippi State University. Education: PhD in MIS, Master of MIS, MBA, and Bachelor of Finance from Auburn University Teaching Experience: 10,000+ students at undergraduate, master's, and doctoral levels in business analytics, decision support systems, programming, and healthcare-related topics His research spans three decades, focusing on organizational learning, methodological challenges in MIS research, and the economics of information technology firms. Current work emphasizes messy data problems like non-normality and missing data, with applications in healthcare analytics, cybersecurity, and financial technology. Recent publications (2021-2025) examine topics including regression imputation methods, cluster analysis applications, AI frameworks for financial portfolio optimization, and vulnerability diffusion models in software ecosystems. His research demonstrates interdisciplinary applications of MIS principles across business, healthcare, and public policy domains. Key Contributions: Developed the Two-Step Normality Transformation method Created frameworks for organizational learning constructs Advanced methodologies for handling messy data in MIS research Explored digital resource clusters in software ecosystems Investigated economic structures of emerging technology providers Professor Templeton has taught across multiple MIS domains including systems analysis and design, information resource management, and advanced research topics. His recent work increasingly integrates artificial intelligence and machine learning techniques to address complex business and technical challenges.

• Biostatistics
• Mental Health Research
• Longitudinal Data Analysis
• +۷ مورد دیگر

Clayton H. Brown is a Professor in the Department of Epidemiology & Public Health at the University of Maryland School of Medicine (UMSOM). He serves as the Director of the Biostatistics Unit within the VA Capitol Healthcare Network MIRECC (Mental Illness, Research, Education and Clinical Center). His primary collaborations span medicine, epidemiology, health services, and behavioral interventions research. B.S. in Mathematics and Psychology, University of Oregon M.S. in Biostatistics, UCLA Fielding School of Public Health Ph.D. in Biostatistics, Johns Hopkins Bloomberg School of Public Health Dr. Brown specializes in biostatistical methodologies for longitudinal and clustered data analysis, clinical trials, and missing data frameworks. His research intersects mental health with quantitative techniques, including factor analysis applications in psychiatric studies. His recent publications (2010-2017) demonstrate cross-disciplinary engagement: from neuroimaging studies on dopamine pathways to community mental health interventions and ophthalmological tissue analysis. Key themes include statistical rigor in clinical research, mental illness comorbidities, and behavioral health outcomes. Coursemaster for Principles of Biostatistics (UMSOM Graduate School) Mentored NIH K awardees and VA Career Development Associates Former statistical consultant for NIMH Summer Research Institute Dr. Brown leads the VA MIRECC Biostatistics Unit, providing methodological support for mental health research across multiple institutions.

Paulo Félix Lamasمشاهده پروفایل

استاد

• Machine Learning
• Biomedical Signal Processing
• Probabilistic Reasoning
• +۳ مورد دیگر

Paulo Félix Lamas is a Full Professor at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He obtained his PhD in Physics from USC in 1999 and became an Associate Professor in 2002. As a foundational leader of CiTIUS (Research Centre in Intelligent Technologies), he served as Head (2010–2019) and Deputy Director (2019–2020). His research focuses on probabilistic learning , temporal abductive reasoning , and biomedical signal interpretation . Key applications include ECG analysis, glucose monitoring, and AI-driven healthcare solutions. His work bridges machine learning with clinical diagnostics, emphasizing interpretability and real-time systems. Recent publications (2015–2024) demonstrate interdisciplinary innovation, spanning: Healthcare AI : Personalized drug dosing, arrhythmia detection, and continuous glucose monitoring. Computational methods : Stochastic embeddings, kernel-based missing data handling, and adaptive clustering. Theoretical advances : Abductive reasoning frameworks for time-series interpretation. He leads major projects like: SOSFood (2024–2028): AI for sustainable food systems. XAI4SOC (2022–2025): Explainable AI for healthy aging. INSIDE (2022–2024): Predictive cardiac rehabilitation. At CiTIUS, he directs research on intelligent healthcare technologies, integrating sensor data with machine learning for chronic disease management.

Roman Hornungمشاهده پروفایل

پژوهشگر ارشد

• Random Forests
• Statistical Methodology for Cross-Lab Prediction
• Statistical Methodology for Multi-Omics Data
• +۵ مورد دیگر

Dr. Roman Hornung is a Research Fellow at the Institute of Medical Informatics, Biometry and Epidemiology (IBE) within Ludwig-Maximilians-Universität Munich (LMU), specializing in Biometry in Molecular Medicine. His work focuses on advancing statistical and machine learning methodologies for biomedical applications. Academic Rank: Research Fellow Key Research Areas: Random Forests, Multi-Omics Data Integration, Interpretable Machine Learning, Cross-Validation Techniques, Biomedical Informatics, and Meta-Research on Prognostic Modeling Teaching: Leads seminars on applied statistics in epidemiology and contributes to clinical epidemiology tutorials Software Contributions: Maintainer of R packages like diversityForest and prioritylasso His research bridges statistical theory and practical biomedical applications, with a particular emphasis on reproducibility and robustness in predictive modeling. He has developed novel random forest variants for multi-class outcomes and multi-omics data. Hornung’s recent publications and submitted work highlight trends in benchmarking machine learning methods, improving cross-study prediction, and understanding the impact of data preparation on model performance. Notably, he has contributed to methodologies for generalization error estimation, variable importance measures, and diversity-enhanced forest algorithms. As a member of the Biometry in Molecular Medicine working group, he collaborates on interdisciplinary projects integrating clinical and omics data. His habilitation at LMU Munich (2024) and editorial roles at Biometrical Journal and Scientific Reports underscore his academic leadership in statistical methodology for health sciences.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Mixture Models
• +۱۳ مورد دیگر

Keefe Murphy is a Lecturer in Statistics within the Department of Mathematics and Statistics at Maynooth University , Faculty of Science & Engineering, and is affiliated with the Hamilton Institute . He is an active researcher in statistical machine learning, Bayesian nonparametrics, and clustering/classification of complex, high-dimensional data. Education: PhD in Statistics, University College Dublin MSc in Statistics, University College Dublin BSc in Economics & Mathematics, University of Limerick Research Interests: His work centres on developing and extending statistical methodologies for supervised and unsupervised learning , with emphasis on mixture models, latent variable models, Bayesian nonparametrics, and computational statistics . He actively contributes novel algorithms and software implementations, including the R packages IMIFA , MoEClust , and MEDseq available on CRAN. Current projects include extensions to Bayesian Additive Regression Trees (BART) , handling missing data , modelling multivariate count data , and variable selection in model-based clustering. Publication Profile: His recent publications (2021–2025) demonstrate a clear trajectory in advancing Bayesian machine learning methods, with contributions to Gaussian process BART models , sparse factor analysis , and educational data mining . Collaborative work spans learning analytics and multi-omic prostate cancer biomarker discovery , illustrating broad interdisciplinary impact. Awards & Recognition: Distinguished Dissertation Award (The Classification Society, 2020) Service & Advising: He serves as Associate Editor for Statistical Analysis and Data Mining and on departmental committees (Course Committee, PR Committee). He has successfully supervised PhD student Mateus Maia (graduated 2024) and currently teaches modules such as Advanced R Programming , Introduction to Data Science , and Nonparametric Statistics . Labs & Collaborations: He is affiliated with the Hamilton Institute , which fosters interdisciplinary research in applied mathematics and statistics, providing a collaborative environment for advancing computational and methodological statistics.

• Dynamic Modeling
• Bayesian Statistics
• Multilevel Analysis
• +۵ مورد دیگر

Dr. Noémi Schuurman is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social Sciences. Specializing in Bayesian multilevel time series analysis and intensive longitudinal data, she leads the Dynamic Modeling Lab under Ellen Hamaker's supervision. Her work bridges psychological theory with advanced statistical techniques, particularly through N=1 modeling and person-specific reliability estimation. Co-founder of dynamic VAR networks in psychology Developer of methods implemented in Mplus software Supervisor of four PhD candidates in longitudinal data analysis Her research focuses on: Measurement error in intensive longitudinal studies Statistical standardization techniques Time series analysis for psychological dynamics Bayesian estimation frameworks Person-specific reliability modeling Recent publications address reliability estimation, night gap challenges in ESM data, and multi-timescale modeling. She actively contributes to methodological training through: Utrecht's summer school programs Workshops on R and Mplus Development of e-learning courses Mentorship for early-career researchers

Baojiang Chenمشاهده پروفایل

استاد

• Statistical Methods for Missing Data
• Measurement Error
• Longitudinal Data
• +۵ مورد دیگر

Baojiang Chen is a Professor in the Department of Biostatistics and Data Science at the University of Texas Health Science Center at Houston (Austin campus) . His research focuses on statistical methodology for handling missing data, measurement error, longitudinal data, and survival data, with applications in tobacco regulation, physical activity/nutrition, cancer studies, and other public health domains. He is affiliated with the Michael & Susan Dell Center for Healthy Living. His methodological work supports rigorous analysis of complex datasets in behavioral and biomedical research. Fields of Interest : Missing data, measurement error, longitudinal data, survival data, tobacco control, physical activity, nutrition, cancer biostatistics