جست‌وجوی استاد و پژوهشگر

Anna Gottardمشاهده پروفایل

دانشیار

• Multivariate Statistical Models
• Graphical Models
• Statistical Machine Learning
• +۵ مورد دیگر

Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.

Rachel Pottingerمشاهده پروفایل

استاد

• Data Management
• Database Systems
• Data Integration
• +۷ مورد دیگر

Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.

Elisa Perroneمشاهده پروفایل

استادیار

• Statistics
• Mathematical Statistics
• Dependence Modeling
• +۴ مورد دیگر

Elisa Perrone is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Her research focuses on dependence modeling, copula theory, and their applications in fields such as public transport analysis, environmental risk assessment, and renewable energy forecasting. Academic Rank: Assistant Professor University: Eindhoven University of Technology (TU/e) Department: Mathematics and Computer Science Elisa’s work explores discrete copulas, zero-inflated data, optimal experimental design, and uncertainty quantification. She has contributed to modeling dependence structures in complex datasets, particularly in transportation systems and climate science. Recent research outputs highlight copula-based statistical post-processing for weather forecasts, analysis of multi-way contingency tables, and uncertainty reduction in LED health management. Her publications span top-tier journals and conferences in statistics and applied mathematics. Scientific Award : Second Best Poster Presentation Award (2015) Elisa actively organizes workshops like the Eurandom Workshop on Dependence Modeling and contributes to editorial activities. She teaches courses on linear statistical models, regression models, and dependence modeling.

Aleksandra Slavkovicمشاهده پروفایل

استاد

• Statistical Data Privacy
• Differential Privacy
• Algebraic Statistics
• +۵ مورد دیگر

Aleksandra Slavković is a Professor of Statistics and Associate Dean for Graduate Education at Pennsylvania State University's Eberly College of Science. She holds a PhD in Statistics from Carnegie Mellon University (2004) and has held academic roles since 2004, including appointments at the Institute for Computational and Data Sciences and Penn State College of Medicine. Her research focuses on statistical data privacy, differential privacy, algebraic statistics, and applications in social and health sciences. She has authored over 50 peer-reviewed publications and serves on editorial boards of top journals like Journal of Privacy and Confidentiality and Annals of Applied Statistics . Slavković has received major honors including Fellowships from the Institute of Mathematical Statistics (2021) and American Statistical Association (2018). She leads initiatives to enhance graduate education, including the Science Achievement Graduate Fellows Program, and actively promotes diversity in STEM through her leadership roles. Her recent work emphasizes privacy-preserving techniques for genomic, healthcare, and network data, with contributions to synthetic data generation and secure multiparty computation protocols. Her academic service includes chairing ASA committees and advising at the National Academy of Sciences. She maintains collaborative ties with institutions like Cornell University and UC Berkeley through visiting scholar programs, and her research bridges statistics, computer science, and applied mathematics.

• Statistics
• Machine Learning
• Statistical Inference
• +۳ مورد دیگر

Peter Grünwald is full professor of Statistical Learning at Leiden University's Mathematical Institute and senior researcher in the Machine Learning group at CWI (Centrum Wiskunde & Informatica) in Amsterdam. His pioneering work on e-values establishes a transformative framework for statistical inference that overcomes critical limitations of classical p-values, enabling flexible experimental designs while maintaining rigorous error control. His research centers on e-values and e-processes as a unifying paradigm between Bayesian and frequentist statistics, with core innovations in safe testing, anytime-valid inference, and optional continuation. These methods allow researchers to gather additional data after initial analysis without inflating Type I errors and to determine significance levels post-hoc—addressing longstanding rigidity in Neyman-Pearson hypothesis testing. His publication trajectory reveals rapid adoption of e-values across disciplines: from foundational theory in PNAS and JRSSB to clinical applications in survival analysis (NEJSDS) and epidemiology (medrxiv meta-analysis). The 2022–2024 publications demonstrate methodological maturation, with implementations in R (safestats package) and growing use in social sciences (PsyArXiv) and causal inference (JASA). Scientific recognition includes: ERC Advanced Grant (2024) for developing flexible statistical inference theory via e-values His ERC-funded project drives current research, while his internship policy restricts non-Dutch master’s/bachelor’s students but welcomes advanced international PhD candidates. Collaborative work spans statisticians (Ly, de Heide, Koolen), machine learning researchers (Ramdas, Shafer), and medical scientists (van Werkhoven). As core member of CWI's Machine Learning group, he advances theoretical foundations with practical impact—evidenced by the first live deployment of e-values in a BCG vaccine meta-analysis. His work redefines statistical practice for adaptive data collection in clinical trials, AI, and social science research.

• Combinatorics
• Algebraic Combinatorics
• Log-Concave Polynomials
• +۱۳ مورد دیگر

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research lies at the intersection of combinatorics, optimization, and theoretical computer science, with a focus on log-concave and Lorentzian polynomials and their applications in discrete and continuous settings. Assistant Professor, University of Waterloo (2022–present) Dirichlet Postdoctoral Fellow, TU Berlin (2020–2022) Postdoctoral Fellow, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoctoral Fellow, KTH, Stockholm (Fall 2019) James H. Simons Fellow, Simons Institute, UC Berkeley (Spring 2019) His research explores the deep connections between algebraic structures and combinatorial phenomena, particularly through polynomial capacity and Lorentzian polynomials. He applies these tools to problems in optimization, sampling, and representation theory. His work often involves developing new algebraic and analytic techniques to tackle longstanding conjectures and algorithmic challenges. The recent publications highlight a consistent focus on Lorentzian polynomials, capacity bounds, and their applications in combinatorics, optimization, and theoretical computer science. Key themes include matroid theory, log-concavity, sampling algorithms, volume approximation, and connections to Lie theory and representation theory. The research spans both theoretical developments and algorithmic applications, often in collaboration with leading researchers in the field. Dirichlet Postdoctoral Fellowship, TU Berlin Postdoc Fellowship in Algebraic and Enumerative Combinatorics, Institut Mittag-Leffler James H. Simons Fellowship, Simons Institute, UC Berkeley Jonathan Leake has advised or collaborated with several researchers, though formal advisees are not listed in the provided text. His work has been supported by prestigious fellowships and collaborations with institutions such as the Simons Institute and TU Berlin. He has taught courses including CO 250: Introduction to Optimization, MATH 239: Introduction to Combinatorics, and CO 739: Lorentzian Polynomials at the University of Waterloo and TU Berlin. While specific lab or research group names are not mentioned, Leake's collaborative work with researchers like Petter Brändén, Nisheeth Vishnoi, and Leonid Gurvits suggests active participation in research teams focused on algebraic combinatorics, optimization, and theoretical computer science. His publicly shared code for sampling from HCIZ densities and verifying positivity in Lie-theoretic contexts indicates an active computational research component.

Amy G Froelichمشاهده پروفایل

دانشیار

• Statistics Education
• Psychometrics
• Educational Assessment
• +۳ مورد دیگر

Amy G Froelich serves as Associate Professor and Director of Undergraduate Studies in the Department of Statistics at Iowa State University, where she has taught over 3,000 students since joining in 2000. Her academic leadership includes curriculum development for undergraduate statistics programs and oversight of degree requirements. Ph.D. in Statistics, University of Illinois, Urbana-Champaign (2000) B.S. in Secondary Mathematics Education, University of Illinois, Urbana-Champaign (1994) Dr. Froelich specializes in statistics education, psychometrics, and educational assessment with particular expertise in categorical data analysis and probability instruction. Her research develops innovative teaching methodologies for introductory statistics courses and focuses on training future secondary mathematics teachers through specialized curriculum design. She investigates how electronic assessments and resampling techniques enhance student learning outcomes in statistical reasoning. Her publication record (2008-2018) demonstrates consistent contributions to statistics pedagogy, emphasizing practical classroom applications, assessment design, and teacher preparation. Key themes include the development of engaging activities (e.g., M&M's weight analysis, iPod randomness testing), psychometric methods for educational measurement, and curriculum reforms for mathematics educators. Waller Education Award from the American Statistical Association (2010) Multiple teaching and advising awards at Iowa State University Dr. Froelich has secured significant research funding from the National Science Foundation, U.S. Department of Agriculture, and U.S. Department of Education to support her statistics education initiatives. As Director of Undergraduate Studies since 2018, she mentors students through curriculum development projects and advises on academic pathways in statistics. Her co-authorship of Business Statistics and Analytics in Practice (9th ed., 2019) extends her impact to textbook development.

Nicholas Wormaldمشاهده پروفایل

استاد

• Combinatorics
• Random Graphs
• Graph Theory
• +۴ مورد دیگر

Nicholas Wormald is a Professor in the Department of Mathematical Sciences at Monash University, Australia. He joined the university in 2013 as an Australian Laureate Fellow, following a decade as a Canada Research Chair in Combinatorics and Optimization at the University of Waterloo, Canada. His primary research focuses on combinatorics, probabilistic methods, and random structures, with notable contributions to random graph theory, enumeration, and applications in optimization. Wormald holds a PhD from the University of Newcastle. His work spans topics including Hamilton cycles in random regular graphs, k-core emergence, probabilistic combinatorics, and algorithms for graph generation. He has contributed to foundational results such as the sudden emergence of giant components in random graphs and the contiguity of random graph models. He serves on editorial boards of journals like the Electronic Journal of Combinatorics and Random Structures and Algorithms . His research interests also include underground mine optimization and the study of Steiner trees. Notable projects include enumeration and random generation of contingency tables, properties of large discrete structures, and the analysis of random structures' applications. Wormald has authored or co-authored over 250 publications, with recent work focusing on asymptotic enumeration, hypergraphs, and the probabilistic analysis of combinatorial structures.

José E. Chacónمشاهده پروفایل

استاد

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +۳ مورد دیگر

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.

Daeyoung Kimمشاهده پروفایل

استاد

• Statistics
• Asymmetric Dependence
• Categorical Data Modelling
• +۴ مورد دیگر

Daeyoung Kim is a Full Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. in Statistics from The Pennsylvania State University (2008), and M.S. and B.S. degrees in Statistics from Korea University (2002, 2000). His research focuses on statistical dependence modeling via copulas, likelihood inference in categorical data, mixture modeling, and interdisciplinary applications in food science and environmental engineering. He has authored/co-authored numerous publications, including a book on Direction Dependence in Statistical Modeling . His work spans statistical methodology development and collaborative research in environmental and biomedical sciences. Education: Ph.D. in Statistics, The Pennsylvania State University, 2008 M.S. in Statistics, Korea University, 2002 B.S. in Statistics, Korea University, 2000 Research Interests: Kim’s work emphasizes asymmetric dependence analysis, categorical data modeling, and mixture models. He applies these methods to food science (e.g., lipid metabolism) and environmental challenges (e.g., cyanobacterial bloom modeling). His statistical contributions include copula-based regression and likelihood inference techniques for incomplete data. Collaborative Projects: Kim has contributed to studies on antimicrobial additives’ effects on colonic inflammation, lipid metabolism modulation in disease models, and the role of dietary fats in tumorigenesis. His interdisciplinary collaborations span environmental engineering, nutrition science, and biomedical research. Awards: Noted for award-winning teaching and research excellence, though specific honors are not detailed in the provided text.

Bersimis Sotiriosمشاهده پروفایل

دانشیار

• Statistical Process Control
• Public Health Surveillance
• Health Analytics
• +۵ مورد دیگر

Sotirios Bersimis is an Associate Professor at the University of Piraeus, Department of Business Administration. He holds additional roles as an elected member of the board of directors of the National Statistical Institute (Greece) and representative for FenSTATs and ECAS. Previously, he served as President of the Hellenic Organization for Health Care Services (EOPYY) and as President of the European Healthcare Fraud & Corruption Network (EHFCN). His education includes a PhD in Statistics from the University of Piraeus, an MSc in Statistics from Athens University of Economics and Business, and a BSc in Statistics and Insurance Science from the University of Piraeus. His research focuses on stochastic models for process monitoring, statistical process control, and health analytics. He has published over 60 peer-reviewed articles in journals like Journal of Quality Technology , Statistics in Medicine , and Annals of the Institute of Statistical Mathematics . His work emphasizes applications in healthcare surveillance, quality management, and fraud detection. Notable contributions include the development of the Process Monitoring Group and the 'Multivariate Statistical Process Control Charts: An Overview' paper, which remains highly cited in the field. Bersimis has received awards such as the 2018 ENBIS Best Manager Award and a public honor from the Greek Prime Minister. He actively collaborates with healthcare institutions, pharmaceutical companies, and international organizations, contributing to projects on health expenditure modeling and anti-fraud initiatives. His teaching spans undergraduate and postgraduate programs in statistics, biostatistics, and data science.

Hanbaek Lyuمشاهده پروفایل

استادیار

• Discrete Probability
• Matrix Factorization
• Machine Learning
• +۳ مورد دیگر

Hanbaek Lyu is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison, with an affiliation in the Department of Computer Science and membership in the Institute for Foundations of Data Science. His research spans discrete probability, matrix factorization, and machine learning, focusing on large discrete systems including interacting particle systems, networks, and structured random matrices. His educational background includes: Ph.D. in Mathematics, The Ohio State University (2018); Thesis: "Combinatorial and probabilistic aspects of coupled oscillators" (Advisor: David Sivakoff) B.S. in Mathematics, Seoul National University Lyu's research bridges theoretical probability and practical machine learning, with emphasis on optimization for dependent data and complex systems. His work develops foundational algorithms for matrix/tensor factorization while exploring synchronization phenomena in oscillator networks and phase transitions in particle systems. Recent publications highlight interpretable models for biological data and rigorous convergence guarantees for nonconvex optimization. Analysis of his 15 most recent publications reveals three dominant threads: (1) optimization theory for constrained nonconvex problems applied to dictionary learning, (2) interacting particle systems and random matrix theory with combinatorial aspects, and (3) interpretable latent models for network dynamics and genomics. His work consistently combines probabilistic methods with computational applications. Lyu leads two active NSF grants: DMS-2206296 (2022-2025): "Online Dictionary Learning for Dependent and Multimodal Data Samples: Convergence, Complexity, and Applications" DMS-2010035 (2020-2023): "Combinatorial and Probabilistic Approaches to Oscillator and Clock Synchronization" He currently mentors five doctoral students across Mathematics and Computer Science departments, organizes UW-Madison's probability seminar, and collaborates with over 30 researchers including Janko Gravner, Lionel Levine, and Wenpin Tang on interdisciplinary projects spanning genomics, network science, and statistical physics.

Jeb F. Willenbringمشاهده پروفایل

استاد

• Representation Theory
• Discrete Mathematics
• Mathematical Physics
• +۳ مورد دیگر

Jeb F. Willenbring is a Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee. His academic career spans multiple institutions, including Yale University, where he held a research position under NSF VIGRE funding (2000-2003). He serves as the Graduate Program Chair and is actively engaged in research, teaching, and academic collaboration. Educational Background: PhD in Mathematics from the University of California at San Diego (2000) BS in Mathematics from North Dakota State University (1995) Research Interests: His work focuses on Representation Theory, Discrete Mathematics, and Mathematical Physics. Key themes include quantum entanglement, symmetry analysis, and the interplay between algebraic structures and geometric invariants. Scientific Contributions: NSF VIGRE funding recipient for research at Yale University Published extensively on Hilbert series, Howe duality, and branching rules Collaborated with prominent mathematicians like Thomas J. Enright and Roger Howe Academic Engagement: Willenbring mentors graduate students and collaborates on research projects. His publications highlight interdisciplinary applications in quantum mechanics and combinatorics. He maintains an open-door policy for students and colleagues at his office (E461 EMS Building) and welcomes inquiries via email at jw@uwm.edu .

Hammou Elbarmiمشاهده پروفایل

استاد

• Order Restricted Inference
• Survival Analysis
• Categorical Data Analysis
• +۳ مورد دیگر

Hammou Elbarmi is a Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, CUNY. His academic journey includes a PhD in Statistics from the University of Iowa (1993), an MS in Statistics (1989), a DEA in Applied Mathematics from University Mohamed V, Morocco, and a BS in Applied Mathematics from the same institution. PhD, Statistics, University of Iowa (1993) MS, Statistics, University of Iowa (1989) DEA, Applied Mathematics, University Mohamed V, Morocco BS, Applied Mathematics, University Mohamed V, Morocco Elbarmi specializes in Order Restricted Inference , Survival Analysis , and Categorical Data Analysis . His research focuses on statistical methods for comparing survival and cumulative incidence functions under stochastic ordering constraints, with applications to competing risks and biased sampling. He has developed novel nonparametric estimation techniques for distributions with type I/II bias and pioneered empirical likelihood approaches for hypothesis testing under inequality constraints. His work spans over two decades of funded research projects from PSC-CUNY , including grants for "Nonparametric estimation under stochastic precedence" (2020-2022) and "Consistent estimation of survival functions under uniform stochastic ordering" (2023-2024). He has delivered over 30 presentations at institutions like Rice University, University of Iowa, and Joint Statistical Meetings. Elbarmi has received the Faculty Scholarship and Creative Achievement Award at Baruch College annually from 2003-2011 and served as a Co-Chair for the Seminar Series in Statistics & Operations Research. His editorial and peer-review roles include associate editorships for journals like Statistica Sinica and Journal of Nonparametric Statistics .

Alireza Ahadiمشاهده پروفایل

مدرس پاره‌وقت

• Data Science
• Machine Learning
• Educational Data Mining
• +۹ مورد دیگر

Dr Alireza Ahadi is a PartTime Lecturer at the Faculty of Engineering & Information Technology , University of Technology Sydney. He holds a PhD in Data Analytics from UTS (2018) and has previously worked as a postdoctoral researcher at Macquarie University (2019-2022) focusing on skills analytics and educational data mining. Education: PhD in Data Analytics (UTS, 2018), Master of Information Technology Extended (UTS, 2013), Bachelor in Computer Science (UTS, 2010). Current Role: Teaching IT and Science subjects at UTS with expertise in databases, programming languages, and data science. Research Interests center on data-driven educational solutions , including text mining in education, skills gap analysis between academia and job markets, code comprehension studies, and miRNA regulation in cancer research. His work integrates machine learning with curriculum analytics to improve educational outcomes and workforce alignment. Recent publications (15 most) reveal trends in applying NLP to educational data, eye-tracking analysis for programming behavior, and skills analytics engines for curriculum-job market mapping. His 2022 article on skills gap identification using Burning Glass data highlights his focus on real-world educational impact. Teaching Experience includes coordination and lecturing in core subjects like Database Fundamentals, Programming Languages, and Data Structures at UTS. He also explores educational data mining , optimization of analytics engines , and recommendation system design in pedagogical contexts.