جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Cyber-Physical Systems
• Intelligent Embedded Systems
• +۵ مورد دیگر

Cesare Alippi is a Professor at Politecnico di Milano, Italy, and Università della Svizzera italiana, Switzerland, with visiting appointments at the University of Kobe (Japan) and Guangzhou (China). His research focuses on machine learning in nonstationary environments, intelligent embedded systems, and cyber-physical systems. Alippi holds IEEE Fellow status and has received notable awards, including the 2016 Gabor Award and the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award. He has authored a monograph, 6 edited books, and over 200 papers. Alippi's academic roles include Vice-President for Education of the IEEE Computational Intelligence Society and Distinguished Lecturer (2014-2016). He has organized major conferences like IJCNN and IEEE SSCI and contributed to initiatives promoting education in computational intelligence. His research addresses challenges in adaptive systems, embedded intelligence, and fault diagnosis. Key projects include deploying wireless sensor networks for environmental monitoring and developing machine learning algorithms for real-world applications like landslide prediction and network security. Alippi's awards reflect his contributions to neural networks and adaptive learning, including the IBM Faculty Award (2013) and IEEE Young Engineer Award (2004).

Eduardo G. Altmannمشاهده پروفایل

استاد

• Complex Systems
• Data Science
• Network Analysis
• +۵ مورد دیگر

Eduardo G. Altmann is a Professor in the School of Mathematics and Statistics at the University of Sydney. His research focuses on mathematical models and computational methods applied to complex systems, data science, and statistical laws. He is part of the Complex Systems and Data Science group and the Computational Social Science Lab, and contributes to interdisciplinary collaborations in areas like urban scaling and language dynamics. Altmann teaches courses such as MATH3076/3976/4076 (Mathematical Computing) and DATA5441 (Networks and High-Dimensional Inference). He serves on editorial boards for journals including the Journal of Statistical Mechanics and New Journal of Physics. His recent work includes studies on Monte Carlo methods for manifold triangulations, generative models for network communities, and statistical laws in complex systems. He has authored a monograph, 'Statistical Laws in Complex Systems,' published by Springer Nature, and frequently engages in academic outreach via platforms like Bluesky.

Germain Van Beverمشاهده پروفایل

پژوهشگر ارشد

• Nonparametric Inference
• Statistical Depth
• Functional Data Analysis
• +۳ مورد دیگر

Germain Van Bever is a Research Fellow (FNRS) at Université Libre de Bruxelles (2016–2018) and a former Research Associate at The Open University (2014–2016). He is affiliated with the Namur Institute for Complex Systems. His expertise spans statistical depth, functional data analysis, and high-dimensional classification. Education: Doctor of Science in Contributions to Nonparametric and Semiparametric Inference based on Statistical Depth from Université Libre de Bruxelles (2013). Research interests include statistical depth measures, asymptotic solutions, and applications in functional data. Recent projects focus on imperfect data analysis and high-dimensional classification methods. He has contributed to over 22 research outputs, with notable work on influence functions and functional data prediction. Awards: None explicitly mentioned beyond academic degrees. Grants: Active in projects like 'IMAL: Imperfect Data: From Mathematical Foundations to Applications in Life Sciences' (2020–2025) and 'High-dimensional and functional classification' (2020–2021). Labs/Teams: Collaborates with the Namur Institute for Complex Systems and co-leads research initiatives in statistical foundations and applications.

Mohamed Abdalmoatyمشاهده پروفایل

پژوهشگر

• Data-Driven Modelling
• Control Systems
• Signal Processing
• +۵ مورد دیگر

Dr. Mohamed Abdalmoaty is a Researcher at ETH Zürich's Institut für Automatik, specializing in the Department of Automatic Control. His work focuses on Data-Driven Modelling and Control , with expertise in system identification, stochastic systems, and optimal control. He holds an affiliation within the Professorship for Complex Systems Control. His research interests emphasize data-driven approaches for predictive control, frequency-domain identification, and nonlinear dynamical systems. He has contributed to advancements in Kalman filter formulations, stochastic Wiener models, and cybersecurity in control systems, particularly in medical applications like the artificial pancreas. Abdalmoaty’s recent publications (2022–2024) highlight innovations in time-varying normalizing flows, privacy-preserving network control, and robust parameter estimation under uncertainty. His work bridges theoretical control systems with practical applications in machine learning and biomedical engineering. He has developed software tools for system identification and simulation, including implementations for frequency-domain analysis and nonparametric closed-loop identification. His research aligns with emerging trends in hybrid machine learning-control frameworks and resilient cyber-physical systems.

Yanglei Songمشاهده پروفایل

استادیار

• Sequential Decision-Making
• Mathematical Statistics
• High-Dimensional Statistics
• +۷ مورد دیگر

Yanglei Song is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University, affiliated with the Faculty of Arts and Science. His research focuses on sequential decision-making problems, including hypothesis testing, change detection, and multi-armed bandits, alongside mathematical statistics such as U-statistics, high-dimensional statistics, and cut-point analysis. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2019) M.Sc. in Mathematics, University of Illinois at Urbana-Champaign (2016) B.Eng. in Electronic Engineering, Tsinghua University (2012) Research Interests: Sequential decision-making frameworks with applications in hypothesis testing and stochastic optimization. Statistical methodologies for high-dimensional data and nonparametric modeling. Development of robust algorithms for reinforcement learning and adaptive systems. Mathematical foundations of U-statistics and their computational guarantees. Publications Trends: Recent work emphasizes theoretical advancements in bandit algorithms, robust model approximation, and statistical inference under covariate-adaptive randomization. Contributions span machine learning, computational statistics, and applied mathematics, with a focus on practical implementations and rigorous theoretical analysis. Students: J. Liu Y. Zhou Z. Wang Z. Zhao N. Li M. Zhou Labs/Teams: Maintains an active research group focused on statistical learning and sequential decision-making, with open-source projects such as Stratified incomplete local simplex tests for nonparametric regression analysis.

Jens Joachim Rogmannمشاهده پروفایل

پژوهشگر

• Educational Psychology
• Conflict Resolution
• Mediation
• +۴ مورد دیگر

Dr. Jens Joachim Rogmann is a researcher and academic coordinator at the University of Hamburg's Faculty of Education, affiliated with the Department of General, Intercultural and International Comparative Education and Educational Psychology (EW 1). He holds roles as Post-Doc in Pedagogical Psychology, Coordinator of Interdisciplinary Studies modules in the BA Educational Science program, Module Leader for PM-2 'Key Competencies', and Head of the FüB-WP Area for Educational Psychology/Interdisciplinary Studies. He also serves as Ombudsman in the Faculty's Complaints Office for Examination Matters (per §66 HmbHG). His research focuses on critical and ethical learning processes, ordinal statistical methods, higher education pedagogy, communication psychology, and conflict mediation. Notable contributions include work on 'communicative resilience' in education and the development of R packages for ordinal dominance statistics. He teaches courses on scientific methodology, ethical competence, and conflict mediation, and oversees the 'Konfliktberatung und Mediation' postgraduate program since 2006.

• Bayesian Inference
• Spatial Statistics
• Biostatistics
• +۵ مورد دیگر

Maura Mezzetti is a Full Professor of Statistics at the Department of Economics and Finance, Università degli Studi di Roma II (Tor Vergata), Rome, Italy. Her career spans institutions such as Harvard School of Public Health, Università Bocconi, and European Institute of Oncology. She holds a PhD in Methodological Statistics from Università di Trento (1997) and a Laurea in Mathematics from Università di Milano (1992). Current Position: Full Professor (2021–Present) Former Roles: Associate Professor (2010–2021), Assistant Professor (2004–2010, Tor Vergata; 2000–2005, Bocconi) Research Interests: She specializes in Bayesian statistical methods for spatially correlated data, hierarchical modeling, survival analysis, and biostatistics. Her work addresses public health challenges like cancer incidence modeling, socioeconomic disparities in disease burden, and psychometric/motor behavior analysis. She has developed Bayesian compartmental models for chemical exposure studies and contributed to epidemiological research on breast and colorectal cancer risk factors. Scientific Awards: Yamagiwa-Yoshida Fellowship (2014) ICRETT Fellowships (2012) Excellence in Research (2005) from Bocconi AIRC Fellowships (1998, 1993) Teaching Contributions: She has taught Statistics at undergraduate and graduate levels across Tor Vergata and Bocconi, including specialized courses in medicine, European economy, and data analysis. Her pedagogical efforts include authoring educational materials like "Statistica Descrittiva: Esercizi risolti e guida alla soluzione usando Excel" (2002).

Quanjun Langمشاهده پروفایل

استادیار

• Quantum Information Theory
• Applied Mathematics
• Interacting Particle Systems
• +۳ مورد دیگر

Quanjun Lang is an Assistant Research Professor of Mathematics at Duke University's Trinity College of Arts & Sciences since 2023. He holds a Ph.D. from Johns Hopkins University (2023). His research focuses on quantum information theory, applied mathematics, and statistical mechanics, particularly in kernel learning for operators, interacting particle systems, and non-Markovian dynamics. Lang teaches courses in probability (MATH 230/STA 230) and differential equations (MATH 353/753), emphasizing foundational mathematical concepts. His recent work includes developing methods for quantum channel learning and memory kernel estimation in generalized Langevin equations. Research Interests : Lang's work bridges theoretical mathematics and applied sciences, addressing challenges in inverse problems and data-driven modeling. Key areas include: Quantum system characterization via low-rank matrix sensing Nonparametric estimation of interaction kernels in particle systems Regularization techniques for stochastic processes Teaching : Recent courses include Probability (Fall 2023-2024), Ordinary and Partial Differential Equations (Spring 2025), and graduate-level Probability (Spring 2024). These reflect his expertise in stochastic processes and mathematical analysis. Research Trends : His publications (2020–2025) demonstrate progression from foundational work on Dirichlet spaces to cutting-edge quantum information applications, showcasing interdisciplinary impact. Current trends focus on unifying measurement design with machine learning for complex dynamical systems.

Scott Monroeمشاهده پروفایل

دانشیار

• Educational Measurement
• Psychometrics
• Item Response Theory (IRT)
• +۵ مورد دیگر

Scott Monroe is an Associate Professor at the University of Massachusetts Amherst in the Department of Educational Policy, Research & Administration (EPRA). His research focuses on latent variable modeling, item response theory (IRT), and structural equation modeling, with applied work in state testing and student growth evaluation. He holds a Ph.D. from UCLA's Graduate School of Education & Information Sciences and an M.S. in Statistics from UCLA. B.A., University of California San Diego, 2000 M.S., Brooklyn College, 2007 M.S., University of California Los Angeles, 2013 J.D., University of California Los Angeles, 2004 Ph.D., University of California Los Angeles, 2014 His research interests emphasize methodological advancements in IRT model evaluation, teacher evaluation frameworks, and statistical computing. He has contributed to global discussions on educational measurement, including work published in Multivariate Behavioral Research and Educational and Psychological Measurement . Prior to academia, Monroe was a high school mathematics teacher in Brooklyn, New York, and Culver City, California. His recent projects include examining learning progressions in middle-school mathematics and evaluating educational interventions like the Green Dot Locke Transformation Project. His work on IRT model fit evaluation and student growth modeling has been influential in shaping assessment practices. He has collaborated on CRESST reports addressing topics like MIRT-based student growth percentiles and diagnostic classification models.

Rui Tuoمشاهده پروفایل

استادیار

• Data Science
• Computer Experiments
• Uncertainty Quantification
• +۴ مورد دیگر

Rui Tuo is an Assistant Professor in the Department of Industrial & Systems Engineering at Texas A&M University. He is also affiliated with the Department of Statistics and serves as a Texas A&M Institute of Data Science Career Initiation Fellow. His research focuses on data science, computer experiments, uncertainty quantification, probabilistic machine learning models, and nonparametric statistics. Dr. Tuo holds a Ph.D. in Statistics from the University of Chinese Academy of Sciences (2013). His work emphasizes methodological advancements in statistical modeling and computational efficiency, particularly in high-dimensional and complex systems. His research interests include Gaussian process models, computer model calibration, kernel methods, and their applications in engineering and data-driven decision-making. Recent publications explore topics like multi-fidelity optimization, privacy-aware machine learning, and distributed Gaussian processes. Key achievements include the Career Initiation Fellowship (2022) and contributions to foundational works in calibration theory and uncertainty quantification. His research bridges statistical theory and practical applications, addressing challenges in simulation-based inference and scalable algorithms.

Robert Shermanمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Asymptotic Methods
• +۴ مورد دیگر

Robert Sherman is a Professor of Economics and Statistics at the California Institute of Technology (Caltech), affiliated with the Division of Humanities and Social Sciences. He holds a Ph.D. in Statistics from Yale University (1991) and joined Caltech in 1996 as an Assistant Professor, advancing to his current rank in 2005. His research focuses on asymptotic methods, semiparametric estimation, and addressing data deficiencies such as misreporting or nonresponse. Sherman has contributed to econometric theory, panel data analysis, and environmental pollutant modeling. Education: B.A. in Philosophy and Latin (Marquette University, 1978), M.A. in Mathematics (University of Louisville, 1985), M.Phil. in Statistics (Yale, 1987), and Ph.D. in Statistics (Yale, 1991). Prior to Caltech, he was at Bellcore's Statistics and Economics Research Group (1990–1996). Research Interests: Asymptotic theory, causal effects estimation in regression models, correlated random coefficient models, and applications to environmental and educational data. His work emphasizes large-sample behavior of estimators and robustness to data contamination. Publications span econometric theory journals (e.g., Econometrica, Journal of Econometrics) and interdisciplinary topics like network traffic analysis (e.g., IEEE/ACM Transactions on Networking). Notable contributions include methodologies for bounding treatment effects and analyzing biased survey data. Advising and Grants: While formal advisee names are unspecified, his research collaborations include co-authors such as Jeff Dominitz, Roger Klein, and Martin S. Taqqu. His work has been supported by grants related to econometric modeling and data analysis.

• Bayesian Statistics
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Emanuele Dolera is an Associate Professor at the University of Pavia, affiliated with the Probability and Statistics research group. His work focuses on Bayesian nonparametric methods, probability theory, and mathematical physics, with a particular emphasis on statistical mechanics and optimal transport frameworks. He has contributed to foundational research in posterior contraction rates, stochastic processes, and the analysis of complex models like the Boltzmann equation and Ewens-Pitman sampling schemes. His research interests span Bayesian predictive inference, asymptotic theory, and the interplay between probability and partial differential equations. Notable contributions include studies on credible intervals in nonparametric estimation, exponential convergence in kinetic equations, and counterexamples in stochastic operator theory. Dolera has also edited special issues honoring prominent statisticians like Eugenio Regazzini. His publications reflect a strong theoretical orientation, addressing topics such as optimal transport applications, count-min sketches in data streams, and the asymptotic behavior of statistical estimators. While no specific grants, awards, or advisees are listed, his extensive bibliography underscores his role as a leading figure in advanced statistical methodology and mathematical analysis.

David Källbergمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Bioinformatics
• +۷ مورد دیگر

David Källberg is an Associate Professor at the School of Business, Economics and Statistics (USBE), Umeå University , specializing in Statistics . His research focuses on statistical methods for high-dimensional data analysis, entropy estimation, and mixture modeling. Research Interests: David's work bridges statistical theory and practical applications in genomics and machine learning. Key areas include: Entropy balancing for causal effect estimation Feature selection in high-dimensional clustering (e.g., RNA-seq data) Mixture models for biomedical applications Nonparametric methods for entropy-type functionals Image matching algorithms in large databases Integration of epigenetic and clinicopathological data Collaborations: He collaborates with researchers like Patrik Rydén, Linda Vidman, and Ingeborg Waernbaum, contributing to journals such as Frontiers in Genetics , PLOS ONE , and Lecture Notes in Computer Science . He is also a member of the Stat4Reg research group.

Joakim Wallmarkمشاهده پروفایل

پژوهشگر ارشد

• Item Response Theory
• Psychometrics
• Educational Statistics
• +۵ مورد دیگر

Joakim Wallmark is a Postdoctoral Fellow at Umeå School of Business, Economics and Statistics , affiliated with the Department of Statistics at Umeå University. His work focuses on psychometrics and statistical analysis. His research specializes in Item Response Theory (IRT) , including applications to clinical rating scales, polytomous test data, and kernel equating methods. He has developed the IRTorch Python package for IRT modeling and explored flexible monotone multiple-choice models. Publications highlight his contributions to test data analysis , with a focus on improving statistical efficiency and equating techniques for mixed-format assessments. His collaborations include researchers like James O. Ramsay, Juan Li, Maria Josefsson, and Marie Wiberg.

Pankaj Kumar Choudharyمشاهده پروفایل

استاد

• Biostatistics
• Statistical Methodology
• Method Comparison Studies
• +۴ مورد دیگر

Pankaj Kumar Choudhary is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas. His academic credentials include a B.Sc. in Statistics from the University of Delhi (1996), M.Sc. in Statistics from Indian Institute of Technology Kanpur (1998), and Ph.D. in Statistics from Ohio State University (2002). Contact information includes office FO 2.408B, phone 972-883-4436, and email pankaj@utdallas.edu. Dr. Choudhary's research focuses on biostatistics, statistical methodology, method comparison studies, and risk prediction. He has developed specialized statistical models including the CBCRisk calculator for contralateral breast cancer prediction and has published extensively on functional data analysis, Bayesian modeling, and measurement error methodologies. His recent publications (2016-2023) demonstrate strong emphasis on medical applications of statistics, particularly in oncology and substance abuse research. Trends include Bayesian learning models for cannabis use disorder, personalized cancer risk prediction frameworks, functional data tolerance bands, and methodological advances in statistical comparison studies. He has co-authored two significant books: Ordered Data Analysis, Modeling and Health Research Methods (Springer, 2015) and Measuring Agreement: Models, Methods, and Applications (Wiley, 2017) with companion R resources. He has taught specialized short courses on method comparison data analysis and statistical machine learning at international venues.