جست‌وجوی استاد و پژوهشگر

Weihua Guiمشاهده پروفایل

استاد

• Industrial automation
• Non-ferrous metal production
• Control theory
• +۷ مورد دیگر

Professor Weihua Gui is an Academician of the Chinese Academy of Engineering (elected in November 2013) and a renowned expert in industrial automation of non-ferrous metal production processes at Central South University. He serves as the director of the Engineering Research Center of 'Colored Metallurgical Automation' of the Ministry of Education and leads the National Natural Science Fund for Creative Research Groups. Professor Gui's research focuses on addressing resources, energy, and environmental issues in China's non-ferrous metals industry through advanced control theory, artificial intelligence, and deep learning applications. His work has successfully identified key automation challenges in smelting processes for copper, aluminum, lead, and zinc, leading to practical industrial implementations that have improved efficiency, resource utilization, and environmental performance. His research interests span across several interconnected fields: Industrial automation of non-ferrous metal production Complex process control systems Artificial intelligence and deep learning applications in industrial processes Fault detection and isolation techniques Soft sensor development for industrial monitoring Optimization of metallurgical processes Professor Gui's publication record demonstrates a consistent focus on applying advanced computational methods to industrial process control problems. His recent work (2018-2021) shows a strong emphasis on deep learning techniques for feature extraction, fault diagnosis, and soft sensor modeling in complex industrial processes. The research spans applications in chemical processes, hydrocracking, and non-ferrous metal production, reflecting an evolution from traditional control theory toward integration with modern AI methods. Professor Gui has been recognized with numerous prestigious awards: 3 National Science and Technology Progress Awards 15 provincial and ministerial Scientific and Technological Progress Awards Ho Leung Ho Lee Foundation Science and Technology Progress Award (2009) Hunan Guangzhao Technology Award (2012) China Process Control Technology Contribution Award As the director of the Engineering Research Center of 'Colored Metallurgical Automation,' Professor Gui leads a research team focused on developing innovative automation solutions for the non-ferrous metals industry. His work bridges theoretical advances in control systems and artificial intelligence with practical industrial applications, resulting in significant improvements in process efficiency, resource utilization, and environmental performance. His leadership in this field has established him as a key figure in China's efforts to modernize and optimize its critical non-ferrous metals sector.

Thomas Ruschمشاهده پروفایل

دانشیار

• Computational Statistics
• Psychometrics
• Exploratory Data Analysis
• +۶ مورد دیگر

PD Dr. Thomas Rusch serves as Deputy Head of the Competence Center for Empirical Research Methods at the Vienna University of Economics and Business (WU Vienna), where he provides statistical consultation and supports faculty and graduate students in applying appropriate statistical methodologies. He has also taught at Harvard University (2019-2021) and FH Technikum, delivering courses in applied statistics, data analysis, computational statistics, and related fields. His educational background includes: Habilitation ("Venia Docendi") in Statistics, Vienna University of Economics and Business (2021) Doctoral studies in Social and Economic Sciences, majoring in Statistics, Vienna University of Economics and Business (2012) Master's degree in Statistics, University of Vienna (2010) Graduated with a degree in Psychology, University of Vienna (2008) Bachelor's degree in Statistics, University of Vienna (2007) Dr. Rusch's research focuses on improving various aspects of modern data analysis, with particular emphasis on discrete data analysis, data mining and statistical learning, exploratory data analysis and visualization, multivariate statistics, natural language processing, and psychometrics. His work bridges statistical methodology with applications in social and behavioral sciences, especially business research and youth mental health. He is particularly known for his contributions to multidimensional scaling, clustering algorithms, and psychometric modeling. His recent publications demonstrate a strong trend toward developing and applying advanced statistical methods to solve real-world problems. His work spans computational statistics, with a focus on R programming implementations, categorical data analysis, and applications in clinical psychology and business analytics. Many of his recent papers address challenges in data visualization, model stability, and the application of machine learning techniques to social science data. His scientific achievements have been recognized with multiple WU Awards for Outstanding Research Achievements (2017, 2018, 2019, 2021, 2022). As a statistical consultant, Dr. Rusch has advised numerous faculty members and graduate students on research methodology and data analysis. He has also been active in developing statistical software, particularly R packages for data analysis and visualization. His collaborative work extends to international projects, including research on youth mental health interventions in Kenya. He is actively involved in organizing academic events, such as the Vienna Workshop on Data and Model Visualisation, and serves as a reviewer for prominent journals in computational statistics.

Ralf Beckerمشاهده پروفایل

مدرس ارشد

• Econometrics
• Financial Econometrics
• Variance-Covariance Forecasting
• +۵ مورد دیگر

Ralf Becker serves as a Senior Lecturer in Economics at the University of Manchester, holding the position of Deputy Head of Department with responsibility for Teaching. He is an active Executive Group Member of the Economics Network and maintains a professional presence through his research website and ORCID profile. His academic career spans over 20 years with continuous contributions to both teaching and research in economics. Becker earned his undergraduate degree in Economics from Johannes Gutenberg University in Mainz, Germany, followed by a PhD from Queensland University of Technology in Brisbane, Australia, completed in 2003 under the supervision of Professor Stan Hurn. After serving as a Lecturer at QUT, he joined the University of Manchester in January 2005 where he has remained throughout his academic career. His research expertise centers on econometrics and financial econometrics, with specific focus on using high-frequency information for variance-covariance matrix forecasting, analysis of Australian wholesale electricity prices and pre-dispatch systems, and evaluation of educational innovations including flipped tutorials and peer-assisted learning. His scholarly fingerprint reveals significant contributions to volatility modeling, macroeconomic variables, capital market returns, inflation dynamics, and factor models. Becker's research trends demonstrate a dual focus on advancing econometric methodologies while simultaneously innovating economics education. His work intersects with Sustainable Development Goals through the university's Digital Futures research platform, particularly in developing quantitative skills training using programming environments like R and MATLAB. As an educator, Becker teaches Undergraduate Mathematics and Statistics for Economists, Econometrics, and Time-Series Econometrics. He is passionate about developing students' quantitative capabilities and has supervised multiple research projects, though specific student names are not publicly documented. His service contributions include extensive professional roles within the department, currently as Deputy Head for Teaching. His laboratory and research team activities center around the DataSquad initiative (visible through his website), focusing on quantitative methods development and educational technology implementation. Current projects appear to integrate traditional econometric approaches with modern computational techniques for both research and pedagogical applications.

Jennifer Greenمشاهده پروفایل

دانشیار

• Statistics Education
• Teacher Development
• Educational Program Evaluation
• +۳ مورد دیگر

Jennifer Green holds dual appointments as Associate Professor at Michigan State University: Department of Statistics and Probability Program in Mathematics Education (PRIME) She teaches advanced statistics courses including Theory of Probability and Statistics and Teaching College Mathematics, while leading proseminars in mathematics education (CEP 926/MTHE 926/TE 926). Her research mission centers on enhancing STEM education through statistical literacy development across K-16 educational settings. Green's research program investigates educator development and statistical methodologies for evaluating educational interventions. She focuses on K-12 data integration in classrooms, modernization of university statistics curricula, and graduate teaching assistant development in scientific communication. Her work bridges theoretical statistics with practical applications to improve teaching efficacy and learning outcomes through evidence-based approaches. Analysis of her recent publications reveals consistent focus on statistics education research, particularly teacher professional development and curriculum innovation. She pioneers value-added modeling techniques for educational assessment and develops interactive tools like Shiny apps for teaching statistical concepts. Her scholarship emphasizes equity in statistics education and authentic data use in graduate teaching training across diverse educational contexts. No scientific awards or honors were documented in available sources. Student advising details and research grant information were not specified in source materials, though her work involves mentoring through graduate teaching assistant development programs. Research team structures and laboratory facilities were not described in provided documentation.

Francesco D'Erricoمشاهده پروفایل

استاد پژوهشی

• Paleolithic Archaeology
• Prehistoric Archaeology
• Human Evolution
• +۵ مورد دیگر

Francesco D'Errico serves as a Research Director at CNRS (French National Center for Scientific Research) affiliated with the University of Bordeaux through UMR PACEA (De la Préhistoire à l'Actuel: Culture, Environnement et Anthropologie). His extensive research career spans several decades with significant contributions to paleoanthropology and prehistoric archaeology. Specialization Diploma in Prehistoric Archaeology (University of Pisa, Italy) Doctoral Thesis, National Museum of Natural History (Paris, 1989) HDR (University of Bordeaux 1, 2003) D'Errico's research focuses on the cognitive evolution of fossil hominins and early modern humans through the analysis of symbolic representations, technical behaviors, and relationships with the environment. His groundbreaking work has challenged the long-accepted model of a symbolic revolution corresponding to the arrival of anatomically modern humans in Europe 40,000 years ago, demonstrating that elaborate bone ornaments, engravings, pigments, and tools were already in use in Africa at least 80,000 years ago. His research falls primarily within PACEA Theme 2 (Archaeology of Death, Rites, and Symbols), addressing fundamental questions about the emergence of modern human behaviors across different continents. Analysis of D'Errico's recent publications reveals consistent focus on symbolic cognition, material culture analysis, and human cognitive evolution across multiple continents. His work spans from early hominin bone technology in Africa to Upper Paleolithic ornamentation in Europe, with increasing integration of neuroimaging and interdisciplinary approaches to understand cognitive evolution. The trend shows growing attention to methodological innovation, particularly through spatial statistics, multivariate analyses, and neuroarchaeological approaches that bridge archaeological evidence with cognitive science. Coordinator of the LABEX CUMILA Consolidation Project (2020-2021) Coordinator of the CNRS PRIME 80 NEUROBEADS Project (2019-2022) CO-PI ERC Synergy Grant QUANTA (2021-2027) Leader of the HUMAN PAST Project (2021-2028) D'Errico leads major international research collaborations and coordinates the PACEA Theme 2 research group focused on the archaeology of death, rites, and symbols. His laboratory work integrates archaeological fieldwork with experimental approaches, neuroimaging studies, and material science analyses to investigate the cognitive foundations of early human symbolic behavior. The research environment he directs brings together archaeologists, anthropologists, neuroscientists, and material scientists to address fundamental questions about human cognitive evolution.

Luke Mazurمشاهده پروفایل

پژوهشگر ارشد

• Applied Statistics
• Computational Statistics
• Statistical and Quantitative Genetics
• +۳ مورد دیگر

Dr. Luke Mazur is an active Research Fellow at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. His work focuses on computational methods for statistical modeling in agricultural and financial contexts. Education: Bachelor of Mathematics and Economics (First Class Honours, 2014), PhD in progress Key Affiliation: Centre for Bioinformatics and Biostatistics at University of Wollongong (CBaDSSPI) Research Interests: Mazur specializes in optimizing linear mixed models for datasets from sustainable primary industries. His methodology emphasizes: Exploitation of sparse matrix techniques Multi-threaded computational approaches Integration of efficient third-party libraries Modern coding practices for statistical analysis Publishing Trends: His research bridges computational statistics and practical applications, with recent work covering financial network analysis and data privacy techniques. Earlier publications focus on statistical formulations for agricultural trials and multivariate data analysis. Laboratory Affiliation: Currently works with the Computational Biology and Data Science Solutions for Primary Industries (CBaDSSPI) research team.

D. Keith Williamsمشاهده پروفایل

استاد

• Biostatistics
• Linear Models
• Statistical Graphics
• +۵ مورد دیگر

D. Keith Williams serves as Professor and Vice-Chair of Education in the Department of Biostatistics at the University of Arkansas for Medical Sciences (UAMS), with dual appointments in the College of Medicine and College of Public Health. He additionally directs Biostatistics and Data Innovation at ACNC, demonstrating leadership in academic administration and research infrastructure. His academic foundation includes: B.S. in Mathematics Education from University of Central Oklahoma M.S. in Statistics from Oklahoma State University M.P.H. from University of Oklahoma Ph.D. in Biostatistics from University of Oklahoma Dr. Williams' research centers on advanced statistical methodologies including linear models, graphical techniques, and simulation approaches. His current focus targets efficient analysis of complex multivariate datasets from microbiome, metabolic, genetic, and whole-organism studies. This work addresses critical challenges in handling high-dimensional biological data where traditional methods fail, particularly in modeling multiple interdependent responses common in modern biomedical research. Analysis of his 2024-2025 publications reveals a strong interdisciplinary pattern spanning pediatric obesity, pregnancy complications, toxicology, and nutritional science. His biostatistical expertise consistently enables novel insights across diverse domains—from cannabis extract pharmacology to rheumatoid arthritis immunology—by developing specialized analytical frameworks for complex datasets. A notable trend involves translating methodological innovations into clinical applications, especially for maternal-child health and metabolic disorders. As Vice-Chair of Education, Dr. Williams oversees biostatistics curriculum development and training programs. His directorship at ACNC involves leading data science initiatives that support UAMS research enterprise. While specific grant details aren't provided, his extensive publication record indicates sustained extramural funding. He directs the Biostatistics and Data Innovation unit at ACNC, which integrates statistical expertise with cutting-edge computational resources to advance research across UAMS. This role positions him at the nexus of methodological development and translational science, fostering collaborations between statisticians and domain experts in medicine and public health.

Jose M Penaمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Artificial Intelligence
• Probabilistic Graphical Models
• +۴ مورد دیگر

Jose M Pena is a Senior Associate Professor and Head of Unit at Linköping University's Department of Computer and Information Science, specializing in the Division of Statistics and Machine Learning (STIMA). His research focuses on probabilistic graphical models including Bayesian networks, Markov networks, and chain graphs within machine learning and artificial intelligence. His research interests center on causal inference, machine learning, and statistical modeling. He actively develops methods for causal discovery, counterfactual reasoning, and sensitivity analysis using normalizing flows and graphical models. His work bridges theoretical statistics with practical applications in complex data analysis. His recent publications (2020-2025) demonstrate a strong focus on causal machine learning, with particular emphasis on directed acyclic graphs (DAGs), counterfactual modeling, and handling unobserved confounding. Key trends include the integration of deep learning with causal structures, robust causal inference under distribution shifts, and scalable methods for high-dimensional causal analysis. As Head of Unit within STIMA, he contributes to the international master's programme in Statistics and Machine Learning. The division hosts significant research activities in modern data analysis and is part of Linköping University's Department of Computer and Information Science—one of northern Europe's largest departments in this field.

Jolanta Pielaszkiewiczمشاهده پروفایل

دانشیار

• Multivariate Analysis
• Statistics
• Machine Learning
• +۳ مورد دیگر

Jolanta Pielaszkiewicz is an Associate Professor at Linköping University, affiliated with the Department of Computer and Information Science (IDA) and the Division of Statistics and Machine Learning (STIMA). She holds the role of Director of Studies within her division. Her research focuses on multivariate analysis, statistical theory, and high-dimensional data, with notable contributions to covariance structures and research ethics. She is involved in teaching and supervising within the master's program in Statistics and Machine Learning. Her work spans topics such as approximate normality in high-dimensional settings, likelihood ratio tests for mean vectors, and mixture models involving Wishart matrices. She collaborates with researchers like Christian Berggren, Daniel Klein, and Dietrich von Rosen. Her division emphasizes modern data analysis and interdisciplinary applications, reflecting her engagement with both theoretical and applied statistics. While no formal awards are explicitly listed, her publications in journals like Journal of Multivariate Analysis and Communications in Statistics highlight her scholarly impact. Her research also touches on ethics in academia, addressing norms and practices in research environments.

Larry Wassermanمشاهده پروفایل

استاد

• nonparametric inference
• causal inference
• machine learning
• +۶ مورد دیگر

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

Jun Shaoمشاهده پروفایل

استاد

• Resampling Methods
• High-Dimensional Data Analysis
• Sample Surveys
• +۴ مورد دیگر

Jun Shao is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on statistical theory and methodology, including resampling methods, high-dimensional data analysis, and medical statistics. He holds prestigious fellowships from the Institute of Mathematical Statistics and the American Statistical Association. Education: B.S., Mathematics, East China Normal University (1982) Ph.D., Statistics, University of Wisconsin-Madison (1987) Research Interests: Shao's work addresses challenges in statistical inference, such as handling missing data, longitudinal studies, and covariate-adaptive designs in clinical trials. He develops methodologies for resampling techniques, variable selection, and nonignorable nonresponse scenarios. His contributions include influential papers on log-rank tests, robust treatment effect estimation, and integration of external data sources. Professional Activities: Editor of Statistical Theory and Related Fields (2017–present) Co-Editor of Journal of Systems Science and Complexity (2014–2019) Fellow of the Institute of Mathematical Statistics and American Statistical Association Awards: Fellow, Institute of Mathematical Statistics (Year not specified) Fellow, American Statistical Association (Year not specified) Teaching & Mentorship: Shao teaches advanced courses like Mathematical Statistics and Variable Selection , emphasizing rigorous theoretical foundations and practical applications. His courses cover topics such as asymptotic theory, likelihood-based methods, and modern variable selection techniques.

Nicholas J. Hortonمشاهده پروفایل

استاد

• Statistics
• Data Science
• Biostatistics
• +۳ مورد دیگر

Nicholas J. Horton is a distinguished faculty member in the Department of Statistics at Amherst College, where he teaches courses in statistics, data science, probability, mathematical statistics, regression, and design of experiments. With a passion for improving quantitative and data literacy for students of diverse backgrounds, he has made significant contributions to statistics education and applied biostatistical research. His educational background includes: Sc.D. from Harvard School of Public Health (1999) A.B. from Harvard College (1987) Horton's research bridges the mathematical, statistical, and computational sciences with interdisciplinary applications. As an applied biostatistician and data scientist, his methodological work focuses on multivariate response models, longitudinal studies, and missing data. He completed a fellowship through the ASA/NSF/Bureau of Labor Statistics program, leading a project to improve imputation methods for the Occupational Employment Survey. His work emphasizes bridging the gap between theory and practice in interdisciplinary statistics and data science settings, with a particular focus on developing novel solutions for real-world data problems that existing methodology sometimes cannot address. An analysis of Horton's recent publications reveals a strong focus on data science education, statistical methodology, and interdisciplinary applications. His work spans from foundational statistics education to cutting-edge applications in public health, obesity research, and audiometry. A notable trend is his commitment to improving statistical and data literacy across educational levels, from K-12 to undergraduate programs. His publications also demonstrate expertise in handling complex data structures including longitudinal studies and missing data problems across diverse fields. Horton has received numerous prestigious awards recognizing his contributions to statistics and education: Waller Distinguished Career Teaching Award, American Statistical Association (2025) Fellow of the Institute of Mathematical Statistics (2025) Peter Holmes Prize (2023) Mosteller Award (2023) Concord Consortium Tinker Fellow (2020) Fellow of the American Association for the Advancement of Science (2018) Robert V. Hogg Award For Excellence in Teaching Introductory Statistics (2015) Fellow of the American Statistical Association (2012) Through his teaching and research mentorship, Horton has actively involved undergraduate students in research projects, viewing this as a key component of his educational mission. He has participated in data science education initiatives through the National Academies and has contributed to developing guidelines for undergraduate programs in statistics. His work with the Project MOSAIC package demonstrates his commitment to integrating computing in the statistics curriculum. Horton maintains active involvement with the R community, contributing to resources like the "mosaic" package that helps students "think with data" using R. He has also been instrumental in developing educational resources including "Modern Data Science with R" and "A Student's Guide to R," supporting both students and educators in the field.

Mohand Said Alliliمشاهده پروفایل

استاد

• Machine Vision
• Machine Learning
• Multimedia Data Processing
• +۵ مورد دیگر

Mohand Said Allili is a full professor in the Department of Computer Science and Engineering at the University of Quebec in Outaouais (UQO). He directs the Imaging, Vision, and Artificial Intelligence Research Laboratory (LARIVIA), focusing on machine vision, machine learning, multimedia data processing, and cybersecurity applications in medical and aerial imagery analysis. His research spans over three decades with notable contributions in image segmentation, texture analysis, deep learning models for medical diagnostics, and AI-driven infrastructure inspection systems. Key projects include drone-based bridge defect detection systems and dermatological imaging networks. His work integrates probabilistic modeling (e.g., Gaussian mixtures) with modern neural networks for robust computer vision solutions. Publications highlight trends in: Deep learning applications for infrastructure monitoring (bridge defect detection) Medical imaging advancements (skin lesion segmentation) Augmented reality systems for remote inspection Innovative texture modeling techniques Allili's lab develops cutting-edge tools like MEDiXNet for dermatological analysis and SES-ReNet for human detection in adverse conditions. His work bridges theoretical computer science with practical engineering solutions. Research focuses on: Semantic image/video annotation Multimodal data fusion Lightweight neural networks for edge computing Context-aware feature extraction

• Fish Biodiversity
• Evolutionary Morphology
• Phylogenetics
• +۳ مورد دیگر

William Fink is a Professor of Ecology and Evolutionary Biology and Director of the Museum of Zoology at the University of Michigan. His research focuses on fish biodiversity, morphological evolution, and phylogenetic systematics, with particular expertise in characiform fishes and geometric morphometrics. He leads efforts in natural history collections curation and georeferencing projects. As a key figure in ichthyology, Fink has pioneered methods in geometric morphometrics and contributed to taxonomic revisions of piranhas and electric fishes. His work integrates fossil records with modern specimens, advancing understanding of South American freshwater fish evolution. He has been actively involved in public outreach through events like Saturday Morning Physics, delivering lectures on topics ranging from museum collections to fish behavior. Research Interests: Fish systematics, evolutionary morphology, biodiversity informatics, paleontology Key Projects: Georeferencing US fish collections, digital archiving of fossil specimens Outreach: Speaker for public science events, museum directorship His publications span over four decades, emphasizing methodological advancements in morphometrics and phylogenetics. Though no specific grants are detailed in the text, his collaborative projects indicate significant research funding involvement. He oversees the Museum of Zoology’s collections, which serve as vital resources for biodiversity studies.

Shyamalika Gopalanمشاهده پروفایل

استادیار

• Genetics
• Evolutionary Biology
• Epigenetics
• +۵ مورد دیگر

Dr. Shyamalika Gopalan is an Assistant Professor in the Department of Genetics & Biochemistry at Clemson University, affiliated with the Center for Human Genetics. She joined Clemson in 2023 after postdoctoral training at the University of Southern California (Center for Genetic Epidemiology) and Duke University (Department of Evolutionary Anthropology). Her research focuses on understanding the interplay between genetics, environment, and evolutionary forces shaping human phenotypic diversity and aging processes. Dr. Gopalan holds a B.Sc. in Biology from McGill University (2012) and a Ph.D. in Ecology and Evolution from Stony Brook University (2019). Her doctoral work explored evolutionary trajectories of human populations through genomic analysis. Her research interests include: Computational modeling of genetic and epigenetic variation Drivers of phenotypic change in domesticated canids Evaluation of complex trait evolution via genomic tools Epigenetic mechanisms influencing human aging Genetic admixture patterns across populations Recent work highlights contributions to understanding: Epigenetic aging biomarkers in diverse populations Genetic differentiation impacts on methylation studies Demographic history of Eastern African hunter-gatherers COVID-19 susceptibility genes through integrative genomics No scientific awards are explicitly listed in the provided materials. While no student advisees are noted, her research lab actively pursues studies in aging, domestication biology, and computational evolution modeling. Lab activities include developing epigenetic clocks and genomic tools for population analysis.