جستجوی استاد راهنمای دانشگاهی

Thomas Bonaldمشاهده پروفایل

استاد

• Data Analysis
• Machine Learning
• Graph Theory
• +۴ مورد دیگر

Thomas Bonald is a Professor at Telecom Paris, part of the Institut Polytechnique de Paris, and heads the DIG (Data, Intelligence and Graphs) team within the LTCI laboratory. His research focuses on data analysis, machine learning, graph theory, knowledge bases, and natural language processing. He leads projects like the YAGO knowledge base and develops open-source tools such as scikit-network and torch-kge for graph analysis and knowledge graph embedding. Key achievements include advancing link prediction, knowledge base refinement using LLMs, and contributions to fair resource allocation in networks. His work integrates theoretical insights with practical applications, including enhancing adaptive streaming performance in mobile networks. Scientific awards include recognition of his former students Céline Comte (Telecom Paris award) and Mathieu Feuillet (Gilles Kahn award). Bonald advises numerous PhD students and has supervised over 20 doctoral candidates. His research spans algorithmic fairness, network science, and large-scale data integration. Labs/Teams: DIG team at LTCI, contributing to projects like YAGO and NetSet datasets. His teaching includes probability, statistics, graph learning, and reinforcement learning, with lecture notes on topics like PageRank and spectral embedding.

Panos Parpasمشاهده پروفایل

دانشیار

• Mathematical Optimization
• Computational Mathematics
• Scientific Computing
• +۳ مورد دیگر

Panos Parpas is a Reader in Computational Mathematics at the Department of Computing, Imperial College London. His research focuses on mathematical optimization algorithms, computational mathematics, and scientific computing, with applications in science, engineering, and finance. He previously held roles as a postdoctoral fellow at MIT and a quant at Credit Suisse. Key research interests include dynamical systems, numerical methods for optimization, and stochastic processes. He teaches courses such as Computational Optimization and Computational Finance with C++. His work bridges theoretical optimization with practical applications in areas like water distribution networks, financial modeling, and machine learning. Panos has authored numerous publications on topics like multilevel optimization methods, stochastic mirror descent algorithms, and the analysis of saddle points using Witten Laplacians. His research emphasizes algorithmic efficiency and scalability, particularly in high-dimensional and distributed settings. Notable collaborations include projects on optimizing water distribution systems (e.g., pump scheduling and pressure management) and developing robust algorithms for financial and engineering systems. His contributions span journals like Automatica , SIAM Journal on Scientific Computing , and Journal of Optimization Theory and Applications .

Maryclare Griffinمشاهده پروفایل

استادیار

• Statistical Inference
• Time Series Analysis
• Variable Importance
• +۳ مورد دیگر

Maryclare Griffin is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Her research develops statistical methods for complex data analysis, with emphasis on time series modeling, variable importance measures, Bayesian computation, and interpretable machine learning. Current methodological work includes adaptive overdifferencing for non-stationary processes, mixture representations for Bayesian simulation, and structured shrinkage priors for high-dimensional inference. Applications span clinical genomics, environmental monitoring, and biophysical trend analysis. Recent publications demonstrate innovations in time series model identification, optimization algorithms for regularized regression, and variable importance frameworks for nonlinear models. Collaborative work extends to metastasis prediction in melanoma and lightning strike pattern modeling. Holds a PhD in Statistics from the University of Washington and completed postdoctoral training at Cornell's Center for Applied Mathematics. Maintains active research in computational statistics and model interpretability.

• Mathematical Analysis
• Applied Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Pierluigi Colli is a Professor in the Department of Mathematics at the University of Pavia. His research focuses on mathematical analysis of phase transition models, optimal control theory, and applications to tumor growth modeling. He leads the Mathematical Analysis and Applications research group and has contributed extensively to the study of Cahn-Hilliard equations, viscoelastic systems, and nonlinear PDEs. His work integrates advanced analytical techniques with computational approaches, addressing problems in material science, biological systems, and thermodynamics. Colli has published over 100 peer-reviewed articles, with recent emphasis on tumor growth models, fractional operators, and dynamic boundary conditions. His research interests include: Phase field systems and their applications Optimal control of PDEs Nonlinear parabolic equations Mathematical models in oncology Material deformation and viscoelasticity Nonlocal and fractional differential equations Recent studies analyze tumor growth dynamics with chemotaxis effects, sparsity-constrained control strategies, and the interplay between thermal memory and phase transitions. Colli’s contributions to well-posedness, asymptotic analysis, and numerical methods have advanced understanding of complex systems in applied mathematics.

Zhigen Zhaoمشاهده پروفایل

دانشیار

• Bayesian/empirical Bayesian statistics
• High-dimensional data analysis
• Multiple comparison
• +۵ مورد دیگر

Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.

• Machine Learning
• Federated Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Jie Ding is an Associate Professor in the Department of Statistics at the University of Minnesota, College of Science and Engineering. His research bridges statistical theory and artificial intelligence, with a focus on federated learning, continual learning, and AI applications in biomedicine and telecommunications. Research Interests: Jie Ding's work centers on developing robust and scalable machine learning systems. His interests include federated and collaborative learning frameworks, statistical modeling under data scarcity, and AI-driven solutions for biomedical engineering and 5G network optimization. He investigates foundational aspects of model selection, information fusion, and adversarial robustness. The recent publications highlight a strong trend in distributed and secure AI, particularly in federated learning with dynamic resource allocation, backdoor attack analysis, and AI applications in cardiac organoid maturation. His work integrates statistical rigor with real-world deployment challenges across heterogeneous systems. Scientific Awards: No awards explicitly listed in the provided text. Advising and Grants: Dr. Ding serves as Principal Investigator (PI) on multiple major research grants, including an NSF CAREER award on continual learning and projects funded by the U.S. Department of Defense and NIH. These projects involve collaborative research with Harvard University and focus on AI-driven flexible electronics, information fusion, and 5G measurement modeling. While specific student names are not listed, his leadership of large-scale research initiatives implies active graduate student mentoring. Labs and Teams: Dr. Ding leads a research group focused on AI and statistical learning, collaborating with interdisciplinary teams in engineering, computer science, and biomedical research. His projects involve partnerships with Harvard University and the National Institutes of Health, indicating a strong network in both academic and applied research domains.

Xiaotong T Shenمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Statistical Inference
• +۱۲ مورد دیگر

Xiaotong T Shen is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. He is actively involved in research and currently accepting PhD students. His work is supported by major grants from the National Science Foundation (NSF) and the National Institutes of Health (NIH), including projects on gene regulatory networks, Alzheimer’s disease genetics, and generative learning on unstructured data. Research Interests: Machine Learning and Deep Learning Statistical Inference and Modeling Biomedical Applications in Genomics and Neurodegenerative Diseases Natural Language Processing and Video Interpretation Generative Models and Synthetic Data Causal Inference and Graphical Models His recent publications reflect a strong trend in integrating deep learning with statistical rigor for causal and biomedical discovery, particularly in high-dimensional settings. Articles span applications in hepatology, transcriptomics, manufacturing, and AI-driven data generation, demonstrating interdisciplinary impact. Scientific Projects and Grants: PI, NSF: Generative Learning on Unstructured Data (2020–2024) PI, NIH: Estimation and Inference in Gene Regulatory Networks (2017–2024) Co-I, NIH: Causal Deep Learning for Alzheimer’s Disease Genetics (2021–2026) PI, NIH: Directed Acyclic Graphical Models for Biological Networks (2022–2026) He has advised numerous research projects and collaborated extensively with researchers in biostatistics and computer science. His work contributes to UN Sustainable Development Goals through data science for health and well-being. He leads a research group focused on foundational and applied statistical learning, with ongoing efforts in developing robust, interpretable models for complex scientific data.

• Macroeconomic Forecasting
• Machine Learning
• Econometrics
• +۶ مورد دیگر

Jad Beyhum is an Associate Professor in Economics at KU Leuven, affiliated with the Faculty of Economics and Business and its Economics Research Group. He specializes in econometrics and statistics, focusing on high-dimensional data analysis, instrumental variable methods, and survival analysis techniques. Current research themes include high-dimensional econometrics (e.g., LASSO-type procedures, factor models) and instrumental variables techniques (e.g., dynamic treatments, censored data). His methodological contributions target econometric challenges in nonlinear panel data, competing risks models, and duration outcomes. Key applications span corporate bankruptcy prediction, program evaluation, and macroeconomic forecasting. Recent publications highlight innovations in instrumental variable estimation under censoring, testing treatment effect homogeneity, and combining machine learning with traditional econometric frameworks. He actively supervises projects on topics like "Beyond Instrumental Variables" and "New methods to control unobserved heterogeneity" . Selected project leadership roles: "Predicting bankruptcy with machine learning methods" (Promotor, 2023-2027) "Partial identification methods for survival analysis" (Co-promotor, 2023-2027)

Yunjie Yangمشاهده پروفایل

مدرس ارشد

• AI-powered sensing
• Machine Learning
• Digital Twins
• +۳ مورد دیگر

Roles & Affiliations Dr. Yunjie Yang is a Senior Lecturer (equivalent to Associate Professor) at the School of Engineering , University of Edinburgh. He is affiliated with the Edinburgh Futures Institute (EFI), Edinburgh Generative AI Laboratory (GAIL), and Edinburgh Centre for Robotics. He previously held the Chancellor’s Fellow in Data Driven Innovation (2018–2023) and Bayes Innovation Fellow (2023–2024). Education PhD in Engineering Electronics (2018), University of Edinburgh MSc in Control Science & Engineering (2013), Tsinghua University BEng in Measurement & Control Engineering (2010), Anhui University Research Interests His research focuses on AI-driven sensing and imaging, machine learning, digital twins, and soft robotics. Specific areas include: Electrical impedance tomography (EIT) for medical imaging Soft robotic perception and tactile sensing Medical devices and wearable technologies Data-driven inverse problems and reconstruction algorithms Awards & Fellowships 2024 IEEE J. Barry Oakes Advancement Award ERC Starting Grant Fellowships: Young Academy of Europe (FYAE), International Society for Industrial Process Tomography (FISIPT), and Higher Education Academy (FHEA) 2015 IEEE I&M Society Graduate Fellowship Award Advising & Grants Dr. Yang supervises PhD students in robotics, sensing, and medical imaging (e.g., Huazhi Dong). He leads projects such as the SELECT project on soft robot perception and Real-time Bedside Medical Imaging. His research is funded by grants from the ERC, Medical Research Council, and the European Commission. Research Group He heads the SMART Group , focusing on Sensing, Machine Learning, and Robotics. The group develops technologies like modular soft wearable gloves and digital twin frameworks for multiphase flow imaging.

• Statistical Learning
• Machine Learning
• High-Dimensional Data Analysis
• +۲ مورد دیگر

Peng Zhao is a Quantitative Researcher affiliated with the University of California, Berkeley's Department of Statistics within the College of Letters and Science. He completed his PhD in Statistics in 2006 under the advisement of Bin Yu, focusing on regularization techniques in high-dimensional data analysis. His research interests center on statistical learning , with emphasis on sparsity, structured regularization, and computational methods for modern data analysis. These areas align with broader trends in machine learning and high-dimensional statistics.

Jia Liuمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Linear Algebra
• Sparse Matrix Computations
• +۹ مورد دیگر

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.

• Multi-compartment modeling
• Cerebral arterial circulation
• Electrical conductivity
• +۵ مورد دیگر

Maryamolsadat Samavaki is a Researcher affiliated with the Computing Sciences Mathematics Research Centre , focusing on computational modeling of cerebrovascular dynamics and neuroelectromagnetism. Her work bridges biomedical engineering and applied mathematics through advanced numerical methods. Key research areas include EEG source localization , transcranial stimulation , and multi-compartment head modeling . Develops spatiotemporal hemodynamic models to analyze microcirculation and blood volume fractions. Applies L1-norm optimization and metaheuristic algorithms for neurostimulation montage design. Recent publications explore cerebral circulation's impact on electrical conductivity and in silico imaging techniques using anatomical atlases . Collaborations emphasize boundary-fitted meshing for subcortical structures in EEG modeling.

• Automata Theory
• Logic in Computer Science
• Infinite-State Systems
• +۷ مورد دیگر

Wojciech Czerwiński is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw. His research lies at the intersection of automata theory, logic, and computational complexity, with a focus on infinite-state systems such as vector addition systems (VASS) and Petri nets. His research interests include: Automata and logic Infinite-state systems Reachability and separability problems Complexity of computational models Formal verification and concurrency theory His recent work has significantly advanced the understanding of the complexity of reachability in VASS, proving it to be Ackermann-complete. This line of research has been published in top venues including FOCS, STOC, LICS, and CONCUR, where several papers received Best Paper Awards. His publications reflect a strong trend toward resolving long-standing open problems in decidability, complexity, and logical definability in infinite-state models. Scientific awards include: Best Paper Award, CONCUR 2022 Best Paper Award, STOC 2019 He leads an ERC-funded project and is actively recruiting PhD students and post-docs. He organizes the Friday Afternoon Seminar and co-organized the Infinite Automata Workshop (2024). He has also contributed to public understanding of science as an editor and author for the journal Delta from 2013 to 2022. His work involves collaboration with leading researchers such as Sławomir Lasota, Jérôme Leroux, Georg Zetzsche, and others. He has made foundational contributions to problems like bisimilarity, language equivalence, and regular separability in various automata models.

Miju Ahnمشاهده پروفایل

استادیار

• Mathematical Optimization
• Statistical Learning
• Nonconvex Programming
• +۳ مورد دیگر

Miju Ahn is an Assistant Professor in the Department of Operations Research & Engineering Management at the Lyle School of Engineering, Southern Methodist University. She holds a Ph.D. in Industrial and Systems Engineering from the University of Southern California (2018) and a B.A. in Applied Mathematics from UC Berkeley (2008). Her research focuses on mathematical optimization methods with applications in statistical learning, power systems, finance, and healthcare. Education: Ph.D., Industrial and Systems Engineering, University of Southern California (2018) B.A., Applied Mathematics, UC Berkeley (2008) Research Interests: Designing computational algorithms for large-scale optimization problems Nonconvex programming and its applications Mathematical modeling for decision-making systems Scientific Awards: NSF CRII grant recipient for research in optimization and decision-making

Jan Glaubitzمشاهده پروفایل

استادیار

• Scientific Computing
• Bayesian Scientific Computing
• Numerical Analysis
• +۷ مورد دیگر

Jan Glaubitz is an Assistant Professor in Scientific Computing at the Department of Mathematics, Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) within the Faculty of Science and Engineering. His research lies at the intersection of numerical analysis, inverse problems, uncertainty quantification, and computational fluid dynamics, with a focus on developing robust and provably convergent computational methods. His research interests center on advancing Bayesian Scientific Computing , particularly for hyperbolic conservation laws and inverse problems under uncertainty. He works on establishing rigorous theoretical foundations—such as approximation, convergence, and stability—for numerical methods while quantifying confidence in computational predictions. His work integrates high-order numerical schemes, shock-capturing techniques, data assimilation, and uncertainty quantification to solve complex problems in science and engineering. The recent publications highlight a strong trend in developing mathematically rigorous and computationally efficient methods for partial differential equations and Bayesian inference. His work bridges theoretical numerical analysis with practical computational frameworks, focusing on stability-preserving discretizations and sparse reconstructions in inverse problems. Jan Glaubitz has held postdoctoral positions at the Massachusetts Institute of Technology (MIT) under Youssef Marzouk and at Dartmouth College under Anne Gelb, reflecting a strong international research trajectory. Although no specific grants or students are listed, his active publication record and leadership in method development suggest ongoing research funding and academic mentorship. He is associated with the Division of Applied Mathematics at Linköping University, which conducts research in computational mathematics, mathematical statistics, and optimization, providing a collaborative environment for interdisciplinary scientific computing.