جست‌وجوی استاد و پژوهشگر

Andreas Artemiouمشاهده پروفایل

استاد

• Statistical Methods
• High-Dimensional Data Analysis
• Dimension Reduction
• +۳ مورد دیگر

Dr. Andreas Artemiou serves as Professor, Vice Rector for Academic Affairs and Quality Assurance, and Dean of the Technology and Innovation School at the University of Larnaca's Department of Information Technologies. His leadership spans academic administration and cutting-edge statistical research with global collaborations. His academic foundation includes: BSc in Mathematics and Statistics from University of Cyprus (2005) MSc and PhD in Statistics from Pennsylvania State University (2008, 2010) Artemiou's research pioneers statistical methods for high-dimensional datasets, specializing in dimension reduction, kernel techniques, and machine learning applications. His work bridges theoretical innovation with practical implementations across engineering, computer science, and medical sciences, particularly evident in pandemic-related mortality analysis and cytometry data processing. Recent publications (2021-2024) reveal accelerating focus on SVM-based dimension reduction, sparse modeling, and medical applications. The trajectory shows increasing interdisciplinary impact, with 2024 works emphasizing matrix data analysis and time-series dimension reduction for real-world health crises. His professional recognition includes: New Researcher Fellow at Statistics and Applied Mathematical Sciences Institute Artemiou actively contributes to major collaborative initiatives without explicit grant details. His editorial role at Computational Statistics and Data Analytics journal and board membership in the European Statistical Computing Association highlight academic leadership. Current projects drive innovation in cytometry analysis and pandemic mortality modeling. He directs CytoPy - an autonomous cytometry analysis framework - and leads pandemic mortality research through the international CMOR consortium, demonstrating commitment to translating statistical theory into public health solutions.

• Non-linear dimension reduction methods
• Statistical methodology
• Data visualization
• +۳ مورد دیگر

Sjoerd Huisman is an Assistant Professor in the Methodology & Statistics unit at Leiden University's Faculty of Social and Behavioural Sciences. He specializes in teaching statistics to psychology and artificial intelligence students and conducts research on non-linear dimension reduction methods. Huisman is also actively involved in promoting open science practices and contributes to the Grafiekpolitie , a visualization-focused initiative within Leiden University's fact-checking group. Education: BA in Archaeology, BSc in Biology, MSc in Statistics (Leiden University), PhD in Bioinformatics (Delft University of Technology) Teaching: Courses in statistics, data science, and multivariate analysis across Psychology and Statistical Science programs Research: Focus on statistical methodology, data visualization, and interdisciplinary applications in psychology and life sciences Affiliations: Open Science Community Leiden, Institute of Psychology

Matey Neykovمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Nonparametric Estimation
• Variable Selection
• +۴ مورد دیگر

Matey Neykov is an Assistant Professor of Statistics and Data Science at Northwestern University, serving as Director of Graduate Studies. He holds a Ph.D. in Biostatistics from Harvard University (2015), with postdoctoral research at Princeton University and prior faculty positions at Carnegie Mellon University. His research focuses on high-dimensional statistics, nonparametric estimation, variable selection, and statistical applications in medical contexts such as electronic health records and personalized medicine. Education: Ph.D. in Biostatistics, Harvard University (2015) B.S. in Applied Mathematics, Sofia University Postdoctoral Research: Princeton University (Department of Operations Research and Financial Engineering) Research Interests: High-dimensional inference and dimension reduction Nonparametric methods under shape constraints Statistical machine learning applications in healthcare Graph property testing and conditional independence testing Publications Trends: Recent work emphasizes theoretical advancements in nonparametric regression, robust estimation under constraints, and optimal transport-based hypothesis testing. Key themes include minimax rate characterizations, adversarial noise resilience, and applications in biomedical data analysis. Labs/Teams: Engages with interdisciplinary teams in statistics, machine learning, and biomedical informatics, focusing on translating theoretical insights into practical methodologies for healthcare analytics.

Per Dannemand Andersenمشاهده پروفایل

استاد

• Technology Foresight
• Research and Innovation Strategies for Industrial Sectors
• Stakeholder Inclusion
• +۷ مورد دیگر

Per Dannemand Andersen is a Professor at DTU's Department of Technology, Management and Economics. He holds leadership roles as Head of the PhD School and Study Leader for the BSc in Data Science and Management. His career includes heading research groups in Technology Foresight (1999–2010), Technology and Innovation Management (2012–2021), and serving as Deputy Head of Management Engineering (2016–2019). His research focuses on Technology Foresight and Research & Innovation Strategies , emphasizing stakeholder inclusion and priority-setting in STI. Projects span sectors like wind energy, hydrogen technology, and agri-food, with international collaborations in Europe, Asia, and Africa. He has supervised numerous doctoral students and teaches foresight methodologies globally. Research Interests : Stakeholder-driven foresight processes Cultural dimensions in futures studies Energy systems modeling Innovation strategies for industrial sectors Science and technology parks Recent Work Trends : Recent articles address cultural diversity in scenario planning, participatory energy systems modeling, and smart specialization in technology parks. His work bridges theory and practice, emphasizing inclusive methodologies to address global challenges like climate change and sustainable development. Labs/Teams : Active in DTU's Technology and Business Studies unit, leading interdisciplinary projects on innovation systems and foresight. Engaged in collaborative networks across academia, industry, and policy.

Seungchul Baekمشاهده پروفایل

دانشیار

• Machine Learning
• High-dimensional data analysis
• Multivariate analysis
• +۳ مورد دیگر

Seungchul Baek is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on statistical methodologies, including machine learning, high-dimensional data analysis, and functional data analysis. He has contributed to interdisciplinary research, particularly in biomedical applications and computational statistics. Baek holds a Ph.D. in Statistics from the University of South Carolina (2018) and advanced degrees from Yonsei University in Seoul, Korea. **Education:** Ph.D. in Statistics, University of South Carolina, 2018 M.A. in Statistics, Yonsei University, 2005 B.A. in Statistics (minor in Mathematics), Yonsei University, 2001 **Research Interests:** Machine learning and high-dimensional data techniques for classification and variable selection Development of semiparametric and nonparametric statistical models Applications in functional data analysis and biomedical research **Teaching and Service:** Active instructor for courses like Statistical Computing, Probability Theory, and Survey Sampling Associate Editor for *Statistical Analysis and Data Mining* since 2023 **Collaborations:** Interdisciplinary projects in oncology and genomics, such as kinase profiling for acute myeloid leukemia Development of computational tools for statistical inference and classification

• Machine Learning
• High-Dimensional Statistics
• Network Models
• +۳ مورد دیگر

Yang Feng is a Professor and Ph.D. Program Director in the Department of Biostatistics at New York University's School of Global Public Health. He holds affiliate faculty positions at the Center for Data Science and PRIISM. His research focuses on Machine Learning, High-Dimensional Statistics, Network Models, and Applications in Biostatistics and Public Health. Key contributions include advancements in Neyman-Pearson classification, federated learning, transfer learning, and high-dimensional statistical methods. He has authored influential papers in journals such as the Annals of Statistics, JASA, and Journal of Machine Learning Research. Recipient of prestigious honors: ASA Fellow, IMS Fellow, and ISI Elected Member. Leading an NSF-funded project on high-dimensional multi-task learning inference (Grant DMS-2324489). Editorial roles at top journals including JASA and AOAS. Research emphasizes theoretical foundations and practical applications, spanning statistical methodology, algorithm development, and interdisciplinary collaborations in health and data science.

Kean Ming Tanمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• High-Dimensional Data Analysis
• Quantile Regression
• +۷ مورد دیگر

Kean Ming Tan is an Associate Professor of Statistics at the University of Michigan, Department of Statistics (College of Literature, Science, and the Arts). His research focuses on statistical machine learning methods for analyzing complex datasets, including multivariate statistical techniques such as probabilistic graphical models, cluster analysis, discriminant analysis, and dimension reduction. He also develops robust statistical methods, quantile regression approaches, non-convex optimization algorithms, and data integration strategies. Recently, his work has involved applying instrumental variables to address unmeasured confounders in models. Education background and academic training details are not explicitly listed in the provided texts. His research interests span both theoretical and applied domains, with contributions to high-dimensional data analysis, computational statistics, and interdisciplinary applications in neuroscience and econometrics. He co-organized the 2022 'Modern Statistical and Machine Learning Methods for Big Data Workshop' in Ann Arbor, Michigan. His publications emphasize methodological advancements in quantile regression, robust estimation, and scalable inference techniques. Key themes include handling large-scale datasets, improving model interpretability, and addressing challenges posed by high-dimensionality and latent confounding variables. While specific advising/grant details are not detailed here, his work reflects active engagement in mentoring students and collaborative research initiatives.

Abhishek Chakraborttyمشاهده پروفایل

استادیار

• Concentration inequalities
• Empirical processes
• Debiasing methods
• +۴ مورد دیگر

Abhishek Chakrabortty is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on advanced statistical methodologies, including concentration inequalities, empirical processes, and debiasing techniques. He specializes in semi-supervised learning, causal inference, and high-dimensional statistical analysis with applications in biomedical informatics and electronic medical records. Key research interests include developing robust frameworks for handling missing data, model misspecification, and non-parametric regression. His work emphasizes practical applications such as phenome-wide association studies and improving treatment effect estimation in complex datasets. His recent publications highlight contributions to semi-supervised inference methods, doubly robust causal estimation, and moving beyond classical sub-Gaussian assumptions in high-dimensional problems. This reflects a strong focus on bridging theoretical advancements with real-world data challenges. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research areas suggest active involvement in mentoring graduate researchers.

David Ruppertمشاهده پروفایل

استاد

• Functional data analysis
• Nonparametric regression
• Statistical machine learning
• +۷ مورد دیگر

David Ruppert is the Andrew Schultz Jr. Professor of Engineering at Cornell University's School of Operations Research and Information Engineering, and Professor of Statistics and Data Science. He holds dual appointments and has been a faculty member since 1987. His education includes a B.A. in Mathematics from Cornell University (1970), M.A. in Mathematics from the University of Vermont (1973), and Ph.D. in Statistics and Probability from Michigan State University (1977). Research Interests: His work spans functional data analysis, astrostatistics, neuroimaging (fMRI/ICA), environmental statistics, and semiparametric regression. He has pioneered methods in measurement error models, splines, and Bayesian statistics. His research has been continuously funded by NSF, NIH, and EPA since 1978. Publications: Over 130 refereed articles and 5 books, including foundational texts like Measurement Error in Nonlinear Models and Statistics and Data Analysis for Financial Engineering . Recent work includes astrostatistical modeling of galaxy spectral energy distributions and neuroimaging analysis. Awards/Honors: Wilcoxon Prize (1986), ASA/IMS Fellowships, Highly Cited Researcher (ISI), and Distinguished Alumni Award (2014). Teaching: Courses include Financial Engineering, Bayesian Statistics, and Functional Data Analysis. He co-developed four graduate/undergraduate courses at Cornell. Service: Editor of Journal of the American Statistical Association , Director of the MPS Program in Data Science and Statistics (DSS). Impact: 29 PhD students trained, many now leading researchers in academia and industry.

• Computational Mechanics
• Machine Learning
• Inverse Problems
• +۷ مورد دیگر

Assad Anshuman Oberai is a Professor at the University of Southern California's Viterbi School of Engineering . His research spans computational mechanics, inverse problems, and machine learning applications in nuclear engineering and biomedical imaging. Key Research Areas: Multi-fidelity modeling, Bayesian inference in mechanics, conditional GANs for posterior estimation, ultrasonic sensing of nuclear fuel canisters, mechano-microscopy for tumor elasticity imaging Methodological Contributions: Novel operator network architectures, dimension-reduced Bayesian frameworks, active noise cancellation techniques, and error estimation in variational multiscale methods Recent work focuses on non-invasive nuclear fuel inspection using ultrasonic sensing and machine learning, achieving significant signal-to-noise ratio improvements with active noise cancellation. In biomedical domains, his team develops cGAN-enhanced elasticity imaging for cancer differentiation, outperforming traditional algebraic methods. Methodologically, he pioneered VarMiON (Variationally Mimetic Operator Networks) with rigorous error analysis, demonstrating superior performance over DeepONet in partial differential equation approximations. His publications address high-dimensional inverse problems through generative models, with applications in epidemic modeling (capturing algebraic decay via behavioral feedback), 3D traction microscopy accounting for cell-induced matrix degradation, and compressible phase change simulations using discontinuous finite element methods. Co-authors include researchers from mechanical engineering, biomedical informatics, and nuclear safety domains.

Ninoslav Truharمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Systems and Control Theory
• Applied Mathematics
• +۴ مورد دیگر

Ninoslav Truhar is Full Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek , Croatia, where he also heads the Department of Applied Mathematics and Computer Science . Education B.S. in Mathematics and Physics, 1987 – University of Osijek M.S. in Mathematics, 1995 – University of Zagreb Ph.D. in Mathematics, 2000 – University of Zagreb Research interests lie at the intersection of numerical linear algebra , systems and control theory , and applied mathematics . His work centres on linear matrix equations , damping optimization in mechanical systems, and the perturbation theory of invariant subspaces . These themes repeatedly appear in his extensive publication record, driving both theoretical advances and practical applications in vibration control and matrix computations. Across his recent publications, the dominant trends are the derivation of sharp perturbation bounds for eigenvalues and eigenspaces of structured matrix problems, the development of efficient algorithms for damping parameter optimisation, and the creation of dimension-reduction techniques that accelerate numerical solution of large-scale Lyapunov and quadratic eigenvalue problems. Professional service Member of the editorial boards of Computational Mathematics and Computer Modeling with Applications , Numerical Algebra, Control and Optimization , Mathematics (MDPI) , Mathematical Communications , and Osječki matematički list . Teaching Undergraduate/graduate courses: Applied Linear Algebra and Scientific Computing , Optimisation Methods , Dynamical Systems . Supervises B.Sc. and M.Sc. theses on topics aligned with his research expertise.

• Uncertainty Quantification
• Machine Learning
• Environmental Fluid Mechanics
• +۱۲ مورد دیگر

David Crevillén García is a Professor specializing in the intersection of machine learning and mathematical models for environmental fluid mechanics. He earned his PhD in Applied Mathematics and Statistics from the University of Nottingham (2015) and holds a Licenciado in Mathematics from the University of Murcia (2002). Research Interests: Uncertainty quantification in stochastic models Linear/non-linear dimension reduction methods (PCA, manifold learning) Gaussian process emulators for computationally expensive models Monte Carlo methods in porous media transport Machine learning applications for urban crowd/traffic motion prediction Finite Element and fluid dynamic modeling Academic Roles: Postdoctoral researcher at University of Warwick (Statistics Department) Researcher at Lucideon Simulation and Modelling Department Teaching Applied Statistics to multiple engineering disciplines Teaching Activities: Bachelor's in Industrial Electronics and Automation Engineering Bachelor's in Agri-food and Biological Systems Engineering Bachelor's in Civil Engineering Bachelor's in Energy and Mineral Resource Engineering Office Hours: Location: Hospital de Marina, Floor 0, Office B033 Monday 09:00–11:00 Tuesday 10:00–12:00 Thursday 11:00–13:00 Appointments required via email

Constantine Caramanisمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• High-Dimensional Statistics
• +۲ مورد دیگر

Constantine Caramanis is a Professor and holder of the Chandra Family Endowed Distinguished Professorship in Electrical and Computer Engineering at the University of Texas at Austin. His research focuses on decision-making in large-scale complex systems, specializing in robust optimization, high-dimensional statistics, machine learning, and applications to networks including social, wireless, transportation, and energy systems. Caramanis directs a substantial research group and is affiliated with the NSF Institute for Foundations of Machine Learning and the UT Machine Learning Lab. He has made significant contributions to optimization theory, developing efficient algorithms for matrix sensing, non-convex problems, and combinatorial optimization. His recent work bridges machine learning and control theory through latent MDPs, diffusion models for inverse problems, and bandit algorithms with fairness considerations. Caramanis has received prestigious recognition including the NSF CAREER award and IEEE Fellowship for his theoretical advances in optimization and learning.

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.

Xinyi (Cindy) Zhangمشاهده پروفایل

پژوهشگر ارشد

• Causal Inference
• High-Dimensional Statistics
• Post-Selection Inference
• +۸ مورد دیگر

Xinyi (Cindy) Zhang is a Postdoctoral Researcher in Biostatistics at Johns Hopkins University, mentored by Professors Brian Caffo and Zheyu Wang. She earned her Ph.D. in Statistics from the University of Toronto in 2023 under Professors Dehan Kong, Linbo Wang, and Stanislav Volgushev, following a Master's degree from UC Berkeley and dual Bachelor's degrees from the University of Toronto in Statistics and Mathematical Application in Economics and Finance. Ph.D. in Statistics, University of Toronto, 2018–2023 M.S. in Statistics, University of California, Berkeley, 2017–2018 B.Sc. in Mathematical Application in Economics and Finance, University of Toronto, 2014–2017 B.Sc. in Statistics, University of Toronto, 2014–2017 Her research develops statistical and machine learning methods for high-dimensional, complex data structures with applications in causal discovery, neuroimaging, and personalized healthcare. Key challenges addressed include unmeasured confounders, incomplete data, and massive-volume datasets, with recent expansion into deep learning for brain imaging in Alzheimer's disease detection. Methodological innovations focus on causal inference frameworks, latent variable modeling, and multi-view data integration. Her 13 publications (2022-2024) reveal a cohesive trajectory in biostatistical methodology, emphasizing causal inference techniques for observational studies and neuroimaging applications. Notable themes include instrumental variable methods for invalid instruments, fMRI multiple testing procedures, and Alzheimer's disease biomarker modeling using MRI and deep learning. The work bridges theoretical statistics with real-world healthcare challenges, particularly in neurodegenerative disease progression. Dr. Zhang has received significant recognition during her graduate training: Ontario Trillium Scholarship (2018–2022) SSC Annual Meeting Student Travel Grant (2022) SGS Conference Grant, University of Toronto (2020) Department Citation Award, UC Berkeley (2018) ASA Nonparametric Statistics Section Student Paper Award Finalist (2018) Dean’s List Scholar, University of Toronto (2015–2017) She has extensive teaching experience as a TA for 12+ statistics courses at the University of Toronto and UC Berkeley, covering mathematical statistics, probability, and data analysis. Her service includes journal reviewing for JASA and Scandinavian Journal of Statistics, conference session chairing at JSM and ICSA symposia, and peer review for UAI and IEEE conferences. No independent student advising or grant leadership is documented.