جستجوی استاد راهنمای دانشگاهی

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.

Indranil Mukhopadhyayمشاهده پروفایل

استاد

• Biostatistics
• Statistical Genetics
• Genomics
• +۳ مورد دیگر

Indranil Mukhopadhyay serves as a Professor in the Department of Statistics at the University of Nebraska-Lincoln, where he conducts cutting-edge research at the intersection of statistics, genomics, and public health. His work bridges theoretical statistical methodology with urgent real-world health challenges, particularly in infectious disease modeling and genomic data analysis. His primary research interests encompass biostatistical methodology development for multi-omics integration, epidemiological modeling of infectious diseases (notably HIV/AIDS and COVID-19), and statistical genetics for complex trait analysis. He has pioneered robust computational frameworks for handling high-dimensional biological data, with emphasis on isoform quantification, gene expression clustering, and genetic association studies in diverse populations. Analysis of his recent publications reveals a strong trajectory toward public health applications , where statistical models directly inform pandemic response strategies and disease control policies. His work consistently demonstrates innovation in adapting mathematical frameworks (e.g., critical community size theory) to contemporary health crises, while maintaining rigorous methodological foundations in sequential estimation and robust statistics. No scientific awards were documented in the provided materials. Information regarding student advising, research grants, or laboratory infrastructure was not available in the source text. His publication record suggests active collaboration with medical researchers, particularly in studies involving Indian population cohorts and rare genetic disorders. No details about specific research laboratories or collaborative teams were provided in the available documentation.

Jaeyoung Parkمشاهده پروفایل

استادیار

• Healthcare Analytics
• Machine Learning Applications in Healthcare
• Patient Outcome Prediction
• +۵ مورد دیگر

Jaeyoung Park is an Assistant Professor in the School of Global Health Management and Informatics at the University of Central Florida's College of Community Innovation and Education. His work focuses on healthcare analytics, machine learning applications in clinical settings, and patient-reported outcome measurement. Key research interests include predictive modeling for surgical outcomes, critical care delivery optimization, and health information technology's impact on patient engagement. He holds an office in DPAC 402O and can be reached at jaeyoung.park@ucf.edu. Research Themes: His studies bridge clinical data analysis with healthcare operations, emphasizing translational research to improve patient care through digital tools and predictive systems. Recent work addresses challenges in ICU care team dynamics, heart failure readmission prediction, and the operational impact of patient portal usage. Publications: His 2021-2025 articles reveal a focus on machine learning applications for clinical decision support, patient outcome prediction, and healthcare system optimization. He explores topics ranging from digital twin models in critical care to mitigating silent abandonment in healthcare contact centers.

Nicolas Jouvinمشاهده پروفایل

پژوهشگر

• Model based clustering
• Dimension reduction
• Variational inference
• +۴ مورد دیگر

Nicolas Jouvin is an INRAE researcher at the MIA-Paris laboratory specializing in applied mathematics and statistics. He is based at AgroParisTech Saclay (Bureau E4.512, Bâtiment E, 22 place de l'agronomie, 91120 Palaiseau) and works within the MIA-Paris-Saclay research unit. His educational background includes a PhD in Applied Mathematics (2017-2020) from Université Paris 1 Panthéon-Sorbonne and an MVA degree (2016) from ENS Paris Saclay. His doctoral research focused on high-dimensional data and graph clustering with discrete latent variable models under the supervision of Pr. Pierre Latouche, Pr. Charles Bouveyron, and Dr. Alain Livartowski, with collaboration at Institut Curie. Jouvin's research centers on probabilistic models for unsupervised learning, particularly model-based clustering and dimension reduction techniques. His work incorporates variational inference, sparse regularisation, and high-dimensional statistics with applications to medical data analysis. He has developed methodologies for hierarchical clustering using discrete latent variable models and the integrated classification likelihood criterion. His publications demonstrate expertise in developing algorithms for clustering count data through multinomial PCA mixtures and discriminative Gaussian subspace clustering using Bayesian approaches. The Greed R package, which implements some of his methodological contributions, was developed in collaboration with Etienne Côme. Charged de cours for Master 2 Data Science (Evry) - Unsupervised learning Master 2 data science (Evry) - Introduction à Python Master 1 data science (Evry) - Analyse des données TD de M1 MAEF Python TD de L3 MIAGE Technique de calcul TD de L1 MIASHS

Aldo SOLARIمشاهده پروفایل

استاد

• Statistics
• Statistical Inference
• Biostatistics
• +۳ مورد دیگر

Aldo Solari is a Full Professor at the Department of Economics of Ca' Foscari University of Venice. His research focuses on statistical inference, conformal prediction, and biostatistics, with applications in genomics, medical decision-making, and neuroscience. He teaches courses in statistics for economics, probability, and data science. Recent publications include advancements in false discovery rate control and hybrid human-AI collaboration in medical diagnostics. Solari co-edits the Proceedings of Machine Learning Research (PMLR) and serves as a referee for the US-Israel Binational Science Foundation. His work integrates theoretical developments with practical software tools for statistical analysis. Teaching: STATISTICA, INTRODUCTION TO PROBABILITY FOR ECONOMICS, DATA ANALYTICS FOR BUSINESS AND SOCIETY Research Themes: Multiple Testing, Conformal Prediction, Biomedical Applications, Neuroimaging Analysis Publications span peer-reviewed journals like Biometrika and Journal of the Royal Statistical Society , emphasizing methodological contributions with real-world impact. Solari’s projects include funding from Italy-Israel collaborations on high-dimensional inference in neuroscience and genomics.