جستجوی استاد راهنمای دانشگاهی

Dehan Chenمشاهده پروفایل

دانشیار

• Inverse Problems in PDEs
• Regularization Theory
• Evolution Equations
• +۷ مورد دیگر

Dehan Chen is an Associate Professor at the School of Mathematics & Statistics, Central China Normal University, and currently a Researcher at the University of Duisburg-Essen. His research lies at the intersection of applied analysis and inverse problems, with a strong focus on partial differential equations and regularization theory. His research interests include: Inverse Problems in PDEs Regularization Theory Evolution Equations Ill-Posed Problems Mathematical Modeling Nonlinear Dynamics The analysis of his recent publications reveals a consistent and deep engagement with variational source conditions, Tikhonov regularization in Banach spaces, and inverse problems in both elliptic and parabolic systems. His work often bridges theoretical analysis with applications in physics and biology, such as electromagnetic modeling and population dynamics. A strong emphasis is placed on convergence rates and stability in ill-posed settings. His notable scientific recognition includes the prestigious Alexander von Humboldt Fellowship. This award highlights his international research impact and collaborative work in Germany. While no formal students are listed, his frequent collaborations with prominent mathematicians such as Jun Zou, Bernd Hofmann, and Irwin Yousept suggest active participation in research teams and potential advisory roles. He has not received mention of external grants, but his sustained publication record in top-tier journals indicates strong research productivity. He is affiliated with research groups focusing on optimal control and inverse problems at the University of Duisburg-Essen, particularly within the AG Optimal Control of Partial Differential Equations, contributing to a vibrant mathematical research environment.

• Linear Algebra
• Matrix Theory
• Graph Theory
• +۳ مورد دیگر

Louis A. Deaett is a Professor of Mathematics at the College of Arts & Sciences, Quinnipiac University. He holds a Ph.D. in Mathematics from the University of Wisconsin-Madison (2009) and previously served as a postdoctoral fellow at the University of Victoria. Education: BS, University of Rochester BA, University of Rochester PhD, University of Wisconsin-Madison Deaett's research focuses on the intersection of Linear Algebra , Graph Theory , and Discrete Mathematics , particularly in understanding matrix patterns and their sparsity structures. His work addresses computational and theoretical challenges in minimum rank problems. Recent publications highlight his contributions to matroid theory , spectrally arbitrary patterns , and graph-based matrix computations , reflecting interdisciplinary trends in mathematics and computer science.

Yongyi Guoمشاهده پروفایل

استادیار

• Statistical Learning
• Decision-Making Methods
• Digital Health Interventions
• +۳ مورد دیگر

Yongyi Guo serves as an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison, following a postdoctoral fellowship at Harvard University under Susan A. Murphy. Her academic foundation includes a Ph.D. in Statistics from Princeton University (2022) advised by Jianqing Fan and a B.S. in Mathematical Sciences from Peking University (2016). Dr. Guo's research integrates statistical learning with digital health applications, specializing in reinforcement learning for personalized mobile interventions and causal inference methodologies. Her work demonstrates particular expertise in micro-randomized trials for cannabis use reduction, where she developed the MiWaves and reBandit algorithms to optimize just-in-time adaptive interventions through online decision-making frameworks. Analysis of her publication trends reveals consistent focus on bridging theoretical statistics with health implementation challenges, featuring innovations in anytime-valid inference for N-of-1 trials, communication-efficient distributed estimation, and robust regression techniques for dependent data structures.

Zhan Gaoمشاهده پروفایل

استادیار

• Econometrics
• Machine Learning
• High-Dimensional Time Series
• +۴ مورد دیگر

Dr. Zhan Gao is an Assistant Professor of Economics at Southern Methodist University, where he has been a faculty member since 2025. His core expertise lies in econometric theory and its intersection with machine learning, particularly in high-dimensional time-series and panel-data settings. Education: Ph.D. in Economics, University of Southern California Research Interests: Dr. Gao’s research spans econometrics , machine learning , biostatistics , and health economics . Methodologically, he focuses on robust estimation under endogeneity, high-dimensional inference, and convex optimization techniques that scale to large datasets. His recent publications advance robust regression methods that withstand outlier contamination, develop new identification strategies for categorical random-coefficient models, and create survival-analysis tools for dependent censoring in oncology trials. Collectively, this body of work reflects a commitment to rigorous theory paired with reproducible computational tools. Software & Reproducibility: All major papers are accompanied by open-source R and Python packages hosted on GitHub, ensuring full reproducibility. Notable repositories include implementations of penalized least squares, copula-graphic estimators, and high-dimensional GMM routines. Teaching: In Fall 2025 Dr. Gao will teach ECO 4370 / 6370 Computing for Economics , introducing graduate and advanced undergraduate students to modern computational methods and convex optimization in econometrics.

• Machine Learning
• Contact/Symplectic Geometry
• Computational Geometry
• +۶ مورد دیگر

Maia Fraser is an Associate Professor at the University of Ottawa , cross-appointed to the School of Electrical Engineering and Computer Science and affiliated with the Brain and Mind Research Institute . Her research bridges Machine Learning , Symplectic Geometry , and Computational Geometry , with interdisciplinary collaborations in Neuroscience and Mathematical Biology . PhD in Computer Science (University of Chicago, 2013) PhD in Mathematics (Stanford University, 1994) Postdoctoral work: University of Toronto Industry experience: Supercomputing Systems AG (Zurich) Her recent work explores AI safety through the lens of living systems, combining mathematical principles with societal implications. She examines temporal abstraction in continual learning, reinforcement learning limits, and geometric constraints in AI. Publications span topology , biological navigation , and neural time scales . Scientific contributions include: Co-organizing Fields Medal Symposium (2022) Co-moderating New Paradigms session at Fields Institute (2024) Leading INTER-MATH-AI (IMA) NSERC-CREATE program (2022) She investigates AI's impact on mathematical research and is writing a book on mathematical frameworks for AI safety .

Rong Chenمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Machine Learning
• Neuroimaging
• +۵ مورد دیگر

Rong Chen is an Associate Professor in the Department of Diagnostic Radiology and Nuclear Medicine at the University of Maryland School of Medicine. He serves as Associate Vice Chair of AI and leads the Biomedical Data Mining Laboratory, focusing on integrating machine learning, computational neuroscience, and neuroimaging to decode brain-behavior relationships. His work spans clinical and translational research for disorders like Alzheimer’s, Parkinson’s, autism, and HIV, and he develops open-source software (GAMMA suite, Advanced Connectivity Analysis) for neuroimaging data analysis. Education: BS in Biomedical Engineering, Southeast University, China (1996) MS in Electrical Engineering, The Graduate School of Chinese Academy of Sciences (1999) PhD in Electrical and Computer Engineering, Washington State University (2003) Postdoctoral Researcher in Radiology, University of Pennsylvania (2005) MTR in Translational Research, University of Pennsylvania (2012) Research Interests: Computational modeling of neural activity and behavior Development of machine learning frameworks for neuroimaging Brain-inspired AI and therapeutic concepts Longitudinal analysis of brain disorders Distributed data mining for heterogeneous databases Software tools for biomarker detection and functional connectivity Scientific Contributions: 20+ years of advanced modeling and algorithm development Two open-source neuroimaging software packages (GAMMA suite, ACA) NIH and BRAIN initiative-funded research Editorial roles in journals like Frontiers in Computational Neuroscience Honors: Senior Member of IEEE Labs & Collaborations: Dr. Chen collaborates with institutions like NIH and Oracle, and his lab has developed tools used in studies on sickle cell disease, autism, and traumatic brain injury.

• Computational Methods
• Numerical Analysis
• Control Theory
• +۹ مورد دیگر

Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.

Andrew Valentineمشاهده پروفایل

دانشیار

• Geophysical inverse theory
• Global seismology
• Machine learning
• +۴ مورد دیگر

Dr Andrew Valentine is currently an Associate Professor in the Department of Earth Sciences at Durham University, a position he has held since 2023. Previously, he served as Assistant Professor at Durham (2021-2023), Fellow at the Research School of Earth Sciences at The Australian National University (2016-2021), and Postdoctoral Researcher at Utrecht University (2011-2016). He holds significant leadership roles including Director of Education for the Department of Earth Sciences (2025-present) and Secretary of the IUGG Commission on Mathematical Geophysics (2023-present). DPhil in Earth Sciences, University of Oxford (2006-2010), supervised by Prof. J.H. Woodhouse BA/MSci in Natural Sciences (Physics), University of Cambridge (2002-2006) Valentine's research focuses on mathematical and statistical tools for extracting information from observational data, with expertise in geophysical inverse theory, global seismology, and machine learning applications in Earth Sciences. His work bridges theoretical mathematics with practical geophysical problems, developing novel approaches to data analysis and interpretation. He has made significant contributions to probabilistic inversion methods, seismic tomography, and the application of deep learning to geoscience problems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional geophysical methods. His work spans from theoretical developments in Bayesian inference and optimal transport to practical applications in seismic modeling, mantle dynamics, and volcanic processes. A notable pattern is his focus on uncertainty quantification in geophysical models and the development of efficient computational frameworks for complex inverse problems. His research increasingly incorporates neural networks and other AI techniques to address longstanding challenges in Earth Sciences. Harold Jeffreys Lectureship, Royal Astronomical Society (2025) ARC DECRA Fellowship (2018-2021) Multiple Geophysical Journal International Outstanding Reviewer citations Geophysical Journal International Student Author Award (2010) Valentine has supervised numerous PhD students including Buse Turunçtur, Matthias Scheiter, Suzanne Atkins, Paul Käufl, and Ralph de Wit, with thesis topics spanning sparsity-constrained inversion, Monte Carlo methods, mantle convection patterns, and probabilistic source inversion. His supervision extends to current students Arijit Chakraborty, Charlotte Aulton, Hanna-Riia Allas, Isaac Abbott, and Ugurcan Cetiner. He has served on various editorial boards including as Editor for Geophysical Journal International (2020-2025) and has been involved in major collaborative projects through organizations like CIG. As Director of InLab since 2018, Valentine leads a research group focused on developing innovative computational approaches to geophysical problems. His team has produced influential open-source software like pyprop8 for seismic modeling and has pioneered applications of deep learning to problems ranging from glacial isostatic adjustment to volcanic glass properties. The group maintains strong international collaborations, particularly with researchers at The Australian National University where Valentine holds an Honorary Senior Lecturer position (2021-2025).

• Data Mining
• Bioinformatics
• Neural Networks
• +۵ مورد دیگر

Dr. Ray R. Hashemi is a Professor in the Department of Computer Science within Georgia Southern University's College of Engineering and Computing. His academic career spans over 14 years of continuous research output from 2003-2017, with significant contributions as co-editor for four International Conferences on Information Technology and Knowledge Engineering (2005, 2010, 2014, 2017). His research focuses on innovative applications of data mining across diverse domains: Bioinformatics: DNA sequence analysis, organ toxicity prediction, and liver cancer predictive systems Medical Informatics: Bone mineral density analysis using DEXA data and dendrograms Financial Systems: Extraction of essential constituents from S&P500 index Environmental Science: Climate prediction using algae sedimentation patterns Computer Vision: Video mining for theatrical analysis and Android-based OCR for non-flat documents Methodologically, Dr. Hashemi specializes in neighborhood systems analysis, association rule mining, and grid-based approaches for sparse data. His work consistently bridges theoretical data mining concepts with practical applications, developing tools for signature-based prediction, record layout discovery, and intent analysis through web behavior. Recent publications (2015-2017) show increased focus on domain-specific applications in finance and toxicology while maintaining core data mining expertise. His collaborative work includes partnerships with international researchers across multiple continents, demonstrated through conference editorial roles and co-authored publications. Dr. Hashemi's research demonstrates sustained scholarly activity with practical implementations in medical diagnostics, financial analysis, and environmental prediction systems.

• Mathematical Statistics
• High-Dimensional Statistics
• Statistical Learning
• +۴ مورد دیگر

Professor Angelika Rohde is a faculty member at the University of Freiburg, holding a professorship in the Department of Mathematical Stochastics within the Mathematical Institute. She actively leads multiple German Research Foundation (DFG) projects including Research Group FOR 5381 and Collaborative Research Center sub-projects. Her research specializes in mathematical statistics for high-dimensional data environments, with core expertise in: Computationally feasible bootstrap methodologies Structural inference for covariance matrices under sparsity Discriminant analysis and classification frameworks Statistical inference under randomization constraints Clinical predictive modeling applications Forensic genetics classification systems Current DFG funding supports her leadership in: FOR 5381: Mathematical Statistics in the Information Age (as spokesperson) Integration of multi-site data for clinical predictive models (sub-project manager) Uncertainty quantification in forensic classification (sub-project manager) Prior projects include high-dimensional stochastic differential equations (Priority Program), covariance matrix inference (Research Group), and Graduate School GRK 2131 on high-dimensional stochastics phenomena. She directs the "Mathematical Statistics in the Information Age" research group, fostering interdisciplinary methodological development for modern statistical challenges.

Maura Johnمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Genome-wide Association Studies
• Machine Learning in Biology
• +۴ مورد دیگر

Maura John serves as a Research Associate at the Chair of Bioinformatics at Hochschule Weihenstephan-Triesdorf's Straubing Campus for Sustainable Resource Use. Her research focuses on developing advanced computational methods for biological data analysis, with particular expertise in genome-wide association studies and protein structure prediction. Her primary research interests include: Genome-wide association studies with permutation-based significance thresholds that preserve population structure Development of bioinformatics tools like permGWAS2 and easyPheno Protein thermostability prediction using machine learning approaches Genomic selection methodologies for crop breeding applications Dr. John's recent publications demonstrate a strong focus on methodological improvements in computational biology, particularly addressing limitations of traditional approaches in handling skewed phenotype distributions and population structure. Her work bridges theoretical statistical methods with practical biological applications across plant genomics and protein science. Notable contributions include: permGWAS2: An improved method that maintains population structure during permutations ProLaTherm: A protein language model-based thermophilicity predictor outperforming existing methods easyPheno: A comprehensive Python framework for phenotype prediction model comparison Her research program demonstrates strong collaborative efforts with Dominik Grimm's group and other bioinformatics researchers, focusing on developing open-source tools that address critical challenges in genomic data analysis. The work has practical applications in plant breeding, protein engineering, and understanding genotype-phenotype relationships.

Huibin Zhouمشاهده پروفایل

استاد

• Statistics
• Data Science
• High-dimensional Statistics
• +۵ مورد دیگر

Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University, where he has held faculty positions since 2004. He served as Chair of the Department of Statistics and Data Science (2017-2021) and earlier as Chair of the Department of Statistics (2012-2017). Professor Zhou earned his Ph.D. from Cornell University in 2004, progressing through academic ranks from Assistant Professor to full Professor at Yale. His research spans high-dimensional statistics, nonparametric estimation, machine learning, and statistical decision theory. Key focus areas include minimax optimality in estimation, Bayesian methods, network analysis, and functional data. Zhou's work frequently bridges theoretical foundations with applications in social networks, signal processing, and computational biology. Analysis of his publications reveals consistent emphasis on theoretical guarantees for statistical methods, with recent work exploring spectral clustering, Gaussian mixtures, variational inference, and privacy-preserving estimation. His research demonstrates deep engagement with minimax optimality, high-dimensional challenges, and computational-statistical tradeoffs. Honors & Awards: Henry Ford II Professorship at Yale University Professor Zhou has taught graduate and undergraduate courses including Probability Theory, Statistical Decision Theory, Nonparametric Estimation, Data Mining, and Functional Data Analysis. He organized several NSF workshops including the 2012 Workshop on High-Dimensional Data and the 2015 Workshop on Empirical Processes.