جست‌وجوی استاد و پژوهشگر

• Statistical Inference on Random Sets
• Random Fields
• fMRI Data Analysis
• +۳ مورد دیگر

Dr. Khalil Shafie is a Professor and Department Chair in the Department of Applied Statistics and Research Methods at the University of Northern Colorado (UNC), part of the College of Education and Behavioral Sciences. He holds a Ph.D. in Statistics from McGill University (1998) and prior academic credentials from Iranian institutions. His research focuses on advanced statistical methodologies including random fields, functional data analysis, and applications in fMRI and longitudinal data. He has contributed to interdisciplinary fields like telecommunications and signal processing through analytical modeling. Education: Ph.D. in Statistics, McGill University (1998) Sc. in Statistics, Shahid Chamran University (1988) Sc. in Statistics, Shahid Beheshti University (1991) Research Themes: Dr. Shafie's work bridges theoretical statistics and applied problems, emphasizing statistical inference on random sets , signal detection in complex systems , and computational methods for high-dimensional data . Recent publications address energy-efficient electronic systems and stochastic process modeling. Professional Experience: Department Chair, UNC (2016–Present) Associate Professor, UNC (2013–2016) International Experience: Held academic roles at Kashan University (Iran) and Shahid Beheshti University (Iran). Contact: Office 507 McKee Hall | Greeley, CO 80639 | +1 970-351-2591

Hang Zhouمشاهده پروفایل

پژوهشگر ارشد

• Functional and high-dimensional data analysis
• Complex-structured data analysis
• ODE/PDE modeling
• +۳ مورد دیگر

Hang Zhou is a Post-Doctoral Scholar at the University of California, Davis, specializing in advanced statistical and computational methodologies. His research focuses on functional and high-dimensional data analysis, complex-structured data including manifold and Wasserstein types, and ODE/PDE modeling. He is affiliated with the Mathematical Sciences Building at UC Davis. His research interests encompass functional data analysis, high-dimensional statistics, and the integration of differential equations into modeling frameworks. He explores complex-structured data such as manifold and Wasserstein geometries, advancing methodologies for analyzing distribution-valued processes and error detection in numerical data. Zhou’s recent work bridges machine learning and statistical theory, exemplified by studies on deep regression for repeated measurements and event-attended graph ODE frameworks. His research also emphasizes conformal inference and optimal transport representations, reflecting a focus on robust statistical methods and interdisciplinary applications in data science. As a postdoctoral researcher, Zhou has not yet listed advisees or grants in the provided information. His website ( https://hg-zh.github.io/ ) provides additional details on his ongoing projects and collaborations.

Thomas Leeمشاهده پروفایل

استاد

• Nonparametric and Semiparametric Modeling
• Statistical Learning
• Statistical Image and Signal Processing
• +۳ مورد دیگر

Thomas Lee, Ph.D., is a Professor of Statistics at the University of California, Davis, affiliated with the Department of Statistics within the College of Letters and Science. His research focuses on nonparametric and semiparametric modeling, statistical learning, signal processing, and applications across scientific disciplines. He holds a Ph.D. from Macquarie University and CSIRO, Australia. Recent work emphasizes time series analysis, survey methodology, and computational statistics. His publications span journals like Signal Processing , Journal of Survey Statistics and Methodology , and Journal of the Royal Statistical Society Series B . Research interests include developing statistical methodologies for complex data structures, such as spatial and spherical data, and addressing challenges in model calibration and robust matrix completion. His work integrates frequentist approaches with modern computational techniques to solve real-world problems in fields like geophysics and environmental science. Publications highlight advancements in statistical signal processing for count data, survey regression with Lasso regularization, and multi-resolution modeling of non-Gaussian random fields. While no awards are explicitly listed, his prolific output reflects significant contributions to statistical theory and applications.

• Statistical Hypothesis Testing
• Permutation Methods
• Group Invariance Testing
• +۳ مورد دیگر

Dr. Nick Koning is an Assistant Professor at the Erasmus School of Economics within the Department of Econometrics at Erasmus University Rotterdam. His research focuses on statistical methodologies with a strong emphasis on permutation tests and group invariance principles. He maintains an ORCID profile: 0000-0002-3784-4480 . His research interests span statistical hypothesis testing , computational efficiency , and applications in econometrics . Key areas include optimizing permutation-based tests, addressing challenges in group invariance testing, and advancing real-time evaluation frameworks. He explores confidence interval estimation, multiple testing validity, and the integration of statistical rigor with practical computational constraints. Recent publications (2021–2025) reflect his contributions to statistical inference, particularly in reducing computational costs for permutation tests and developing anytime-valid testing strategies. His work bridges theoretical statistics with applied econometric challenges, such as risk-averse stochastic programming and high-dimensional data analysis. No scientific awards or specific grants are mentioned in the provided information. While he advises students in econometrics and statistics, no formal advisee names are listed. Collaborations involve international networks, though specific countries/affiliations remain unspecified.

Mengchu Liمشاهده پروفایل

استادیار

• Change Point Analysis
• High-Dimensional Statistics
• Robust Statistics
• +۳ مورد دیگر

Roles & Affiliations: Dr. Mengchu Li is an Assistant Professor in the School of Mathematics at the University of Birmingham. Previously, he served as a Postdoctoral Research Fellow in the Department of Statistics at the University of Warwick (Feb 2023–Sept 2024), and completed his PhD in Statistics at Warwick under Prof. Yi Yu. Education: PhD in Statistics, University of Warwick (2023) MASt in Mathematical Statistics, University of Cambridge (2019) BSc in Mathematics and Economics, Durham University (2018) Research Interests: Focuses on statistical theory and methodology for heterogeneous data analysis, particularly in high-dimensional settings and under privacy constraints. Key areas include change point analysis, robust statistics, differential privacy, and transfer learning. His work emphasizes minimax optimality and explores fundamental trade-offs between data contamination, privacy, and statistical performance. Research Trends in Articles: Recent work spans federated learning with privacy guarantees, robust change point testing in high-dimensional settings, and privacy-preserving network analysis. His papers often bridge statistical theory with applications in machine learning and distributed systems. Awards & Honors: Harrison Award (2023) for highly commended PhD thesis NeurIPS 2022 Scholar Award Teaching & Service: Taught graduate courses on statistical inference and computation at Birmingham. Previously led tutorials in mathematical statistics and linear modeling at Warwick. Active reviewer for Annals of Statistics , Biometrika , NeurIPS , and other top venues. Contributed to the R package changepoints for change-point detection methods. Labs & Collaborations: Engaged in interdisciplinary research through workshops on heterogeneous data (2024) and change point analysis (2023). Collaborates with institutions like the Alan Turing Institute and Warwick’s Statistics department.

Tomasz J. Kozubowskiمشاهده پروفایل

استاد

• Stochastic Modeling
• Distribution Theory
• Laplace Distribution
• +۷ مورد دیگر

Dr. Tomasz J. Kozubowski is a Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno. He holds a Ph.D. in Statistics and Applied Probability from the University of California, Santa Barbara, and an M.S. in Statistics from the University of Texas, El Paso. His research focuses on stochastic modeling of natural phenomena across climate science, geosciences, finance, and economics. Key research areas include distribution theory (particularly Laplace and heavy-tailed distributions), limit theory, mathematical statistics, financial mathematics, and stochastic models for hydro-climatic events. He has authored/co-authored over 120 publications, including two monographs, and serves on editorial boards of multiple journals. In 2016, he received the Sentinel of Science Award from Publons for peer review contributions. Education: M.S. in Statistics, University of Texas, El Paso Ph.D. in Statistics and Applied Probability, University of California, Santa Barbara His work emphasizes interdisciplinary applications, such as modeling extreme weather events, financial risk, and environmental variability. Publications span journals like Annals of the Institute of Statistical Mathematics , Scandinavian Journal of Statistics , and Journal of Computational and Applied Mathematics . He is actively involved in academic service, including editorial roles and peer review for over 100 journals. Research tools include advanced statistical methodologies and computational frameworks for high-dimensional data analysis.

• Computational Neuroscience
• Systems Neuroscience
• Neural Engineering
• +۷ مورد دیگر

Matthew A. Smith is a Professor in the Department of Biomedical Engineering and the Carnegie Mellon Neuroscience Institute, where he serves as Co-Director of the Center for the Neural Basis of Cognition. His research bridges computational and experimental neuroscience to understand visual perception, cognition, and motor control. Dr. Smith's research focuses on neural engineering, visual perception, cognition, eye movements, and neural circuits . His laboratory investigates how groups of neurons interact to construct visual perception and translate it into cognitive processes and motor outputs. The lab employs a multi-scale approach combining single-neuron electrophysiology with global signals like EEG and near-infrared imaging, examining both normal and disease states of the brain. Analysis of his recent publications reveals strong trends in brain stimulation optimization (e.g., MiSO/OMiSO frameworks), neural population dynamics during cognitive tasks, visual cortex plasticity , and non-invasive neurotechnology development. His work spans fundamental neuroscience questions about working memory and perception while developing practical tools for brain monitoring and intervention. NIH K99/R00 Pathway to Independence Award Research to Prevent Blindness Career Development Award Dr. Smith has secured substantial funding from NIH, NSF, Research to Prevent Blindness, Schaffer Foundation for Glaucoma Research, and Hillman Foundation to support his research program. His laboratory actively develops novel methodologies for neural recording and stimulation while investigating fundamental mechanisms of visual processing and cognition. The lab maintains strong collaborative ties within Carnegie Mellon's neuroscience ecosystem through the Center for the Neural Basis of Cognition.

Nikola Simidjievskiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Biomedical Data Integration
• Neuroscience
• +۴ مورد دیگر

Dr. Nikola Simidjievski is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology. His research focuses on machine learning applications in natural sciences, including oncology, neuroscience, and biomedical informatics. He specializes in multimodal data integration, dynamic systems modeling, and explainable AI techniques. Simidjievski contributes to interdisciplinary projects such as AI-driven space research and Earth observation systems (e.g., AIAtlas). His work emphasizes computational methods for small-sample biomedical data and interpretable neural networks. Education background not explicitly stated, but his research spans computational biology, aerospace telemetry analysis (e.g., Mars Express spacecraft), and environmental modeling. He collaborates on projects like GalaxAI for spacecraft data analysis and PATHS for medical imaging. Key tools developed include Healnet for biomedical data fusion and RO-FIGS for tabular ensemble methods. No scientific awards listed. Advising no listed students, but contributes to academic initiatives like the Human Brain Project's neuroscience training programs. Engages in open-source projects (e.g., AITLAS toolbox for Earth observation). Current research trends include tabular data augmentation methods (TabEBM, TabMDA) and efficient transformer-based models for medical imaging (PATHS).

Matey Neykovمشاهده پروفایل

استادیار

• High-Dimensional Inference
• Statistical Machine Learning
• Nonparametric Estimation
• +۴ مورد دیگر

Matey Neykov is an Assistant Professor in the Department of Statistics and Data Science at Northwestern University . Previously, he was on the faculty at Carnegie Mellon University and held a postdoctoral position at Princeton University under Han Liu. He earned his Ph.D. in Biostatistics from Harvard University, advised by Jun S. Liu and Tianxi Cai, and a B.S. in Applied Mathematics from Sofia University, Bulgaria. Research Interests: Neykov focuses on high-dimensional nonparametric estimation, variable selection, graph property testing, and statistical applications in medicine such as electronic medical records (EMRs) and personalized medicine. His work bridges theoretical advancements in machine learning and practical medical data analysis. Education: B.S., Applied Mathematics, Sofia University Ph.D., Biostatistics, Harvard University (2015) Professional History: Postdoctoral Researcher, Princeton University (Department of Operations Research and Financial Engineering) Faculty, Carnegie Mellon University (2017–202X) Current Position: Assistant Professor, Northwestern University No specific scientific awards or grants are explicitly listed in the provided texts. His research emphasizes both foundational statistical theory and medical applications, though no lab affiliations or team details are mentioned beyond his academic roles.

Maggie Makarمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Healthcare Applications
• +۳ مورد دیگر

Maggie Makar is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of Michigan. Her research focuses on the intersection of machine learning and causal inference, particularly in developing robust and data-efficient methods for healthcare and resource-constrained settings. She received her PhD from MIT's CSAIL and a bachelor's degree in math and economics from UMass Amherst. Her work emphasizes causal reasoning to improve predictive models' reliability, with applications in healthcare decision-making and policy evaluation. Key areas include offline reinforcement learning for medical interventions, confounding variable handling, and mitigating biases in AI systems. Dr. Makar teaches courses such as CSE 598 (Causality and Machine Learning) and mentors PhD students in causal ML and healthcare AI. She actively seeks to expand her research group and collaborate on projects blending causal theory with real-world applications. Education: PhD in Computer Science, MIT (CSAIL), 2018 Bachelor's in Mathematics and Economics, University of Massachusetts Amherst, 2013 Awards: CAREER Award (NSF, 2024) CRII: SCH Grant (NSF, 2022) Research Themes: Causal inference in machine learning Data-efficient methods Healthcare applications Algorithmic fairness

Fabian Scheiplمشاهده پروفایل

پژوهشگر

• Functional Data Analysis
• Statistical Modeling
• Machine Learning
• +۳ مورد دیگر

PD Dr. Fabian Scheipl is the Head of the Department of Statistics at Ludwig-Maximilians-Universität München (LMU). His work focuses on cutting-edge statistical methodologies, particularly in Functional Data Analysis, and he leads the Functional Data Analysis research group. He specializes in developing advanced statistical models and open-source software tools for data science applications. His research bridges theoretical statistics with practical implementations, emphasizing reproducible research and educational resources. Dr. Scheipl’s research interests include functional additive mixed models, cluster analysis, and high-dimensional data techniques. He has contributed significantly to R package development, including refund , gamm4 , and lme4 , which are widely used in statistical computing. His work spans diverse fields such as healthcare (e.g., analyzing critical care outcomes), environmental science (e.g., earthquake dynamics), and computational biology (e.g., epigenetic studies). His publications highlight innovations in statistical methodology, such as improving cluster validation metrics and advancing topological approaches to outlier detection. While no awards are explicitly listed, his impactful contributions to statistical software and theory are widely recognized in academic circles. He actively engages in advising doctoral candidates and contributes to teaching and mentorship in statistical education. Labs/Teams: Leads the Functional Data Analysis group within LMU’s Department of Statistics, fostering interdisciplinary collaborations across computational, biomedical, and environmental research domains.

Lihua Chenمشاهده پروفایل

استاد

• Statistics
• Bayesian Methods
• Categorical Data Analysis
• +۳ مورد دیگر

Lihua Chen is a Professor of Statistics at the Department of Mathematics & Statistics, James Madison University, since 2009. His research focuses on model combining methodologies, categorical data analysis, Bayesian approaches, and statistical procedure integration. He teaches courses such as StatAssess in Spring 2025. Education: PhD in Statistics, Iowa State University (2005) MS in Finance, Beijing University (1993) BS in Economics, Beijing University (1990) His research interests emphasize advancing statistical frameworks for complex data structures and their applications. Notable contributions include work on factorial data analysis and econometric problem-solving techniques. His publications span theoretical advancements and applied methodologies across statistics and economics. Dr. Chen maintains a professional website at educ.jmu.edu/~chen3lx , which includes teaching materials and datasets like the 'StatAssess' resources.

• Statistics
• Statistical Inference
• Time-Series Analysis
• +۳ مورد دیگر

Emanuele Taufer is a Full Professor of Statistics at the Department of Economics and Management of the University of Trento. His academic career includes roles as Vice Director of the Department of Computer Science and Business Studies and Faculty Delegate for International Relations. He holds a Ph.D. in Statistics from Cardiff University, an M.Sc. in Mathematical Statistics from George Washington University, and a Laurea in Economics from the University of Trento. His research focuses on statistical inference, stochastic processes, goodness-of-fit tests, and applications in ESG analysis. Notable contributions include work on exponentiality testing, graphical models, and financial dependence modeling. He has been recognized for his 2002 paper on mean residual life characterization at the SIS2002 conference. Recent research trends emphasize methodological advancements in ESG performance measurement, sparse network estimation for heavy-tailed data, and generalized precision matrices for financial risk modeling. His work spans theoretical statistics, applied econometrics, and interdisciplinary topics like environmental governance. Education: Ph.D. in Statistics, Cardiff University (UK) M.Sc. in Mathematical Statistics, George Washington University (USA) Laurea in Economics, University of Trento (Italy) Professional Roles: Full Professor of Statistics at University of Trento (2003–present) Associate Professor (2003–2003), Assistant Professor (1996–2002) Awards: 2002 SIS2002 Recognition for innovative statistical testing methodology Key Research Themes: Stochastic processes and estimation ESG methodology and financial reporting High-dimensional data analysis Goodness-of-fit tests and tail index estimation

François Bachocمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Mathematical Statistics
• +۳ مورد دیگر

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

• Statistical Methodology
• Latent Variable Models
• Tensor Decompositions
• +۳ مورد دیگر

Zhongyuan Lyu is a Research Fellow (Postdoctoral Research Scientist) at Columbia University's Data Science Institute, mentored by Professors Yuqi Gu and Kaizheng Wang. His research focuses on statistical methodology for latent structures in mixture models, graphical models, and tensor decompositions, with applications to heterogeneous data analysis. Prior to Columbia, he earned his PhD in Mathematics from the Hong Kong University of Science and Technology under Professor Dong Xia's supervision. His academic background includes advanced work in high-dimensional data analysis, latent variable modeling, and computational statistics. Research interests emphasize developing theoretically grounded algorithms for complex data types, particularly in network science and multilayer data frameworks. Recent publications highlight contributions to spectral clustering optimization, adaptive transfer learning frameworks, and tensor-based methodologies for higher-order networks. His work bridges statistical theory and practical applications, addressing computational limits and optimal estimation challenges in modern data science problems. No scientific awards or grants are explicitly mentioned in the provided data. His current position is full-time within the Data Science Institute's research team.