جست‌وجوی استاد و پژوهشگر

• Stochastic Model Updating
• Active Vibration Control
• Aeroelasticity
• +۳ مورد دیگر

John Mottershead is the Alexander Elder Professor of Applied Mechanics at the University of Liverpool, affiliated with the School of Engineering under the Faculty of Science and Engineering. His research focuses on stochastic model updating, active vibration control, aeroelasticity, and full-field vibration analysis. He has held editorial roles in journals like Mechanical Systems and Signal Processing and is a Fellow of the Institution of Mechanical Engineers. Education: BSc, PhD, DEng (degrees not specified in detail). Research Interests : Stochastic model updating with uncertainty quantification Active vibration control strategies for nonlinear systems Aeroelastic stability and flutter suppression Full-field vibration measurement techniques using digital image correlation Grants & Collaborations : Leadership in EU and EPSRC-funded projects on model updating, aeroelasticity, and structural dynamics. Collaborations with institutions like University of Kassel, Politecnico di Torino, and Airbus. Awards & Recognition : Over 20 keynote presentations at international conferences (e.g., Uncertainties 2016, ICSV 2015). Recipient of the 2021 Aerospace Best Paper Award for morphing actuator research. Labs & Teams : Active in interdisciplinary teams addressing aerospace structural dynamics and experimental modal analysis.

César A. Uribeمشاهده پروفایل

استادیار

• Distributed Optimization
• Decentralized Control
• Algorithm Analysis
• +۵ مورد دیگر

César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.

Deep Rayمشاهده پروفایل

استادیار

• Applied Mathematics
• Numerical Methods
• Machine Learning
• +۳ مورد دیگر

Deep Ray is an Assistant Professor of Mathematics at the University of Maryland, College Park (UMD), with a joint appointment at the Institute for Physical Science and Technology (IPST). His research focuses on the intersection of numerical analysis and machine learning, particularly in developing robust numerical methods for partial differential equations (PDEs) and enhancing computational efficiency through AI integration. He has expertise in entropy-stable schemes, shock-capturing algorithms, and operator learning for surrogate modeling. Education and Teaching: Ray has taught advanced courses in computational methods, data science, and numerical analysis at institutions including UMD, University of Southern California (USC), and École Polytechnique Fédérale de Lausanne (EPFL). He has also developed specialized courses like Machine Learning and Computational Physics and contributed to workshops on scientific computing and deep learning. Research Interests: His work spans scientific machine learning, hyperbolic conservation laws, and physics-informed AI. Recent projects include fusion of wildfire models with satellite data, entropy-stable schemes for fluid dynamics, and Bayesian inference via generative adversarial networks (GANs). He actively collaborates on open-source tools, including a GitHub repository for deep learning tutorials. Labs and Teams: Ray leads a research group exploring AI-driven numerical methods and interdisciplinary computational challenges. His lab emphasizes practical applications in fluid dynamics, inverse problems, and reduced-order modeling.

• Mathematical Optimization
• Multiobjective Optimization
• Parametric Optimization
• +۵ مورد دیگر

Margaret Wiecek is a Professor in the Department of Mathematical and Statistical Sciences at Clemson University, affiliated with the College of Science. She holds a PhD and MS in Systems Engineering and Electrical Engineering from AGH University of Science and Technology in Krakow, Poland. Her research focuses on mathematical optimization, particularly multiobjective optimization, distributed systems, and optimization under uncertainty. Applications span engineering design, portfolio optimization, and network routing. She has developed algorithms like the distributed computation of Pareto sets and robust multiobjective optimization frameworks. Wiecek's work emphasizes interdisciplinary collaboration, with contributions to fluid-structure interaction modeling and parametric optimization. Notable publications include studies on robust efficiency, biobjective Bayesian optimization, and decomposition methods for complex systems. Her research has been recognized through awards such as the MCDM Gold Medal (2019) and the Excellence in Discovery Faculty Award (2020). Education: PhD, Systems Engineering, AGH University of Science and Technology (Krakow, Poland) MS, Electrical Engineering, AGH University of Science and Technology (Krakow, Poland) Awards: Sofia Kovalevskaia Visiting Professorship (1995) Mercator Visiting Professorship (2002) MCDM Gold Medal (2019) Advising & Grants: Guided students achieving awards (e.g., Brian Dandurak, Garrett Dranichak) Recipient of Collaborative Award (2012) and Research Award (2014) Labs/Teams: Involved in multiobjective optimization research groups and interdisciplinary projects at Clemson University.

Andrea Hupmanمشاهده پروفایل

دانشیار

• Decision Analysis
• Predictive Modeling
• Uncertainty Quantification
• +۱ مورد دیگر

Andrea Hupman is an Associate Professor in the Department of Supply Chain & Analytics at the University of Missouri–St. Louis's College of Business Administration. She teaches business analytics, decision analysis, and predictive modeling, earning the 2017 Gitner Excellence in Teaching Award. Research Focus: Hupman develops decision-analytic frameworks for operations management, including risk-averse classification algorithms, drone logistics optimization, and behavioral modeling of supply chain decisions. Her work integrates predictive analytics with economic and psychological insights. Awards: IEEE Systems Journal Best Paper Award (2019) William A. Chittenden Award (2016) INFORMS New Faculty Colloquium Participant (2015) Doctoral Advising: Has supervised PhD dissertations on vaccine supply chain optimization, software effort estimation, and maintenance scheduling.

• Psychometrics
• Bayesian Statistical Methods
• Educational Assessment
• +۳ مورد دیگر

Alfonso J. Martinez is an Assistant Professor of Psychology at Fordham University's Department of Psychology. His research focuses on advanced statistical methodologies in psychometrics, particularly Bayesian approaches, latent variable modeling, and diagnostic classification systems. He contributes to improving measurement techniques in educational and psychological assessments through innovative model development and validation. His work addresses challenges in personality assessment, rapid guessing behavior in testing, and the application of structural equation modeling (SEM) across diverse contexts. He actively publishes in high-impact journals, with a focus on methodological advancements and their practical implications for assessment design. While no specific academic awards are listed, his prolific publication record reflects dedication to advancing statistical methodologies in psychology. His teaching and professional affiliations include involvement with academic organizations central to psychometric research and educational measurement.

Shan Huangمشاهده پروفایل

استاد

• Measurement Error Modeling
• Latent Variables Models
• Model Misspecification
• +۴ مورد دیگر

Shan Huang is a Professor in the Department of Statistics at the University of South Carolina , College of Arts and Sciences. His research focuses on measurement error modeling, latent variables, and nonparametric statistics, with a particular emphasis on methodologies for non-Gaussian data and robust inference. Research Interests include: Developing inferential methods for data with measurement errors Model misspecification and diagnostic techniques Nonparametric approaches for mean, mode, and density estimation Analysis of heavy-tailed, directional, and compositional data Recent Publications highlight advancements in Bayesian networks, modal regression with mixture distributions, and directional data modeling. His work addresses challenges in group testing data, coarsened data, and error-prone nodes. Teaching includes courses such as Advanced Statistical Inference, Latent Variable Models, and Nonlinear Statistical Models. His students have contributed to multiple publications marked with asterisks.

Mohamedou Ould Hayeمشاهده پروفایل

دانشیار

• Stochastic Processes
• Time Series Analysis
• Limit Theorems
• +۵ مورد دیگر

Mohamedou Ould Haye is an Associate Professor in the School of Mathematics and Statistics at Carleton University. He holds a PhD in Mathematics and Statistics from Université Lille 1, France (2001), followed by postdoctoral research at Lille and HEC Montreal. He joined Carleton in 2003 as an Assistant Professor and was promoted to Associate Professor in 2007. His research focuses on Stochastic Processes , Time Series Analysis , Limit Theorems , and Long Memory Processes , with applications to forecasting and dependence modeling. He has contributed extensively to methodologies in seasonal long-memory data , nonstationarity detection , and empirical process analysis . His publications span over two decades, addressing topics such as frequency-domain testing for long-range dependence , confidence interval estimation for linear processes , and robust regression techniques . He teaches advanced courses in time series analysis, stochastic processes, and statistical theory. While no formal advisees are listed, his teaching portfolio includes courses like Statistical Methods for Business and Probability Theory . His work bridges theoretical advancements with practical statistical challenges in time series and dependent data analysis.

Xiaojing Wangمشاهده پروفایل

دانشیار

• Bayesian modeling
• Latent variable models
• Item response models
• +۵ مورد دیگر

Xiaojing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. She holds multiple affiliations including the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), the Connecticut Institute for the Brain and Cognitive Sciences (IBACS), and the Center for Environmental Sciences and Engineering. Education: Ph.D. in Statistics and M.A. in Economics from Duke University (2012) M.S. in Probability and Mathematical Statistics from Chinese Academy of Sciences (2008) B.S. in Information and Computational Science from Hunan University (2005) Her research focuses on Bayesian methodologies with applications spanning neural decoding, environmental science, psychometrics, and computational biology. She develops novel statistical models for high-dimensional data including latent variable frameworks for neural spiking activity and dynamic item response theory for educational testing. Her work integrates machine learning with traditional statistical inference to address complex problems in interdisciplinary domains. Wang's publications demonstrate a consistent focus on Bayesian computation and model development, with significant contributions to Gaussian process methodologies, subgroup analysis in clinical applications, and ecological forecasting. Recent work shows increasing emphasis on neural data science and computationally intensive methods for high-dimensional inference. Awards and Honors: NSF Faculty Early Career Development (CAREER) Award (2019) Elected Member of International Statistical Institute (ISI) (2017) Jeffrey’s Excellence Prize for Best Methodological Poster (2013) As a statistical consultant, she advises through UConn's Statistical Consulting Services. Her methodological innovations support applications across health interventions, cognitive science, and environmental research through interdisciplinary collaborations.

Yogendra P. Chaubeyمشاهده پروفایل

استاد

• Sampling
• Distribution Theory
• Survival Analysis
• +۳ مورد دیگر

Dr. Yogendra P. Chaubey is a Professor in the Department of Mathematics and Statistics at Concordia University, Montréal, Canada. Holding a Ph.D. from the University of Rochester (1977), his research focuses on statistical methodology with emphasis on sampling theory, distribution modeling, and survival analysis. Education: Ph.D., University of Rochester (1977) His work spans nonparametric density estimation for circular and non-negative data, wavelet-based noise reduction in biological imaging, entropy estimation, left-censored spatial modeling (e.g., arsenic contamination), and inverse Gaussian distributions. Recent collaborations include applications in protein classification and public health. Dr. Chaubey has published extensively on statistical theory and methodology, including corrections and extensions to classical estimators. His teaching includes advanced courses in sample survey theory and applications.

Jinbo Biمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine learning
• Data mining
• +۷ مورد دیگر

Jinbo Bi is the Frederick H. Leonhardt Professor of Computer Science and Associate Head in the Department of Computer Science & Engineering at the University of Connecticut's School of Engineering. He holds additional affiliations with the Department of Community Medicine and Health Care at UConn Health Center, Department of Mathematics, Department of Statistics in the College of Liberal Arts and Sciences, and the Department of Biomedical Engineering. Bi earned his Ph.D. from Rensselaer Polytechnic Institute and M.Sc. from Beijing Institute of Technology. His research spans artificial intelligence, machine learning, data mining, pattern recognition, computer vision, bioinformatics, medical informatics, and drug discovery. He directs two laboratories: the Laboratory of Machine Learning & Health Informatics and the Laboratory of Artificial Intelligence & Drug Discovery. His recent publications demonstrate a strong focus on applying machine learning to healthcare challenges, particularly in mental health monitoring through smartphone data, depression prediction, substance use disorder analysis, and drug discovery. His work combines theoretical advances in AI with practical healthcare applications, showing particular strength in multi-view learning, graph neural networks, and optimization techniques. Bi has received recognition including a best paper award at BIBM 2014 workshop and two highly accessed articles in BMC Genomics and BMC Genetics. His research is supported by numerous grants from NSF, NIH, VA, and industry partners including Travelers Insurance and Pfizer. As an educator, Bi teaches advanced courses in machine learning and reinforcement learning. His laboratory currently has multiple postdoctoral fellowship openings, reflecting his active research program and mentorship of numerous students who appear as co-authors on his publications.

Todd Kuffnerمشاهده پروفایل

دانشیار

• Statistical Inference
• Asymptotic Theory
• Bayesian Statistics
• +۷ مورد دیگر

Todd Kuffner is an Associate Professor in the Department of Mathematics and Statistics at Washington University in St. Louis , holding a Ph.D. in Mathematics from Imperial College London . His research bridges statistical theory , Bayesian asymptotics , and machine learning , focusing on rigorous inference after model selection. Education: Ph.D., Mathematics, Imperial College London M.Sc., Econometrics and Mathematical Economics, London School of Economics M.Sc., Economics, London School of Economics B.A., Economics, University of Michigan Kuffner's work investigates the validity, accuracy, and power of statistical procedures, with a focus on reconciling Bayesian, frequentist, and neo-Fisherian paradigms. His publications address higher-order asymptotics , post-selection inference , and resampling techniques for small-sample and high-dimensional settings. Key trends in his recent articles include: Bootstrap methods for high-dimensional and dependent data Bayesian approaches to volatility estimation in financial models Connections between machine learning and frequentist post-selection inference Formal Edgeworth and von Mises expansions for robust regression Empirical likelihood and model selection uncertainty Scientific contributions : Organizer of the Workshop on Higher-Order Asymptotics and Post-Selection Inference (WHOA-PSI) (2016-2020) Editorial roles at Harvard Data Science Review , Sankhya Series A , and Journal of the American Statistical Association Recipient of NSF grants for collaborative research His advising and organizational efforts emphasize collaborative research and innovative conference design , including interactive forums and workshops.

Jonas Latzمشاهده پروفایل

مدرس

• Bayesian Inference
• Uncertainty Quantification
• Stochastic Processes
• +۵ مورد دیگر

Jonas Latz is a Lecturer in Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, uncertainty quantification, stochastic processes, and their applications in computational mathematics and inverse problems. He has contributed to areas such as physics-informed neural networks, stochastic gradient methods, and medical imaging modeling. Key research interests include developing robust algorithms for Bayesian inverse problems, analyzing stochastic dynamical systems, and advancing numerical methods for partial differential equations. His work bridges theoretical foundations with practical applications in fields like tumor growth modeling and medical image reconstruction. Recent Achievements : Recipient of the SIAM Activity Group Uncertainty Quantification Early Career Prize (2024) SIAM Student Paper Prize (2020) SIGEST Award (2023) Dr. Latz collaborates internationally on topics such as adversarial machine learning, deep learning methods for PDEs, and stochastic sampling techniques. His research emphasizes rigorous mathematical analysis alongside computational innovation.

• Statistics
• Experimental Design
• Bayesian Statistics
• +۴ مورد دیگر

Dr. Timothy Waite is a Lecturer in Statistics at the University of Manchester, specializing in experimental design and Bayesian statistics. His academic work focuses on developing innovative statistical methodologies for complex modeling scenarios with applications across various domains. Dr. Waite's research interests span multiple areas of statistics with a particular focus on: Bayesian statistics and inference Optimal experimental design Nonlinear and generalized linear models Design of experiments Monte Carlo methods and simulation Statistical modeling for prediction and inference Analysis of Dr. Waite's publication record reveals a strong focus on advancing experimental design methodologies, particularly in Bayesian frameworks. His work consistently addresses challenges in high-dimensional spaces and nonlinear models, with applications ranging from debt recovery modeling to general statistical inference. A notable trend in his research is the development of efficient computational methods for optimal design problems, with increasing emphasis on robustness and practical applicability in recent years. His publications in top statistical journals demonstrate both theoretical rigor and practical relevance. Dr. Waite serves as a Principal Investigator for the Statistical Advisory Unit (SAU), a collaborative research initiative at the University of Manchester involving multiple faculty members. While specific details about his supervised students are limited in the available information, the mention of 'Supervised Work (1)' suggests he has mentored at least one graduate student or research assistant. His research contributes to the UN Sustainable Development Goals through the University's Digital Futures initiative, indicating applications of his statistical methodologies to broader societal challenges.

Harlan Campbellمشاهده پروفایل

استاد مدعو

• Bayesian Statistics
• Meta-Analysis
• Causal Inference
• +۳ مورد دیگر

Harlan Campbell is an Adjunct Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on Bayesian statistics, causal inference, meta-analysis, and statistical methodology, with applications in medical and public health contexts. Campbell collaborates internationally, such as leading the Zika virus individual participant data consortium to study pregnancy outcomes. He has advised at least one student: Nathaniel Wu Dyrkton. His work integrates evidence synthesis, clinical trial analysis, and methodological innovation to address gaps in data reporting and analysis across disciplines. Education details are not explicitly provided, but his career reflects advanced training in statistical theory and applications. Research interests include developing robust statistical techniques for observational studies, addressing measurement error in meta-analyses, and improving methodologies for indirect treatment comparisons. He has contributed to frameworks for estimating infection fatality rates during the COVID-19 pandemic and advancing Bayesian approaches to evidence synthesis. Publications span topics like structural uncertainty in evidence synthesis, doubly robust estimators for clinical trials, and causal inference in longitudinal studies. His work emphasizes rigorous statistical methods to enhance reliability in medical and epidemiological research. Grants and collaborations are implied through his involvement in global consortia and methodological reviews but are not explicitly detailed in the provided text. Campbell maintains an active Google Scholar profile with extensive citation activity.