جست‌وجوی استاد و پژوهشگر

• Scientific computing
• Finite element methods
• Inverse problems in mathematical physics
• +۲ مورد دیگر

Jingzhi Li is a Professor and Associate Chair of the Department of Mathematics at Southern University of Science and Technology (SUSTech), where he has been serving since January 2020. Previously, he was an Associate Professor at SUSTech from June 2012 to December 2019. His research focuses on scientific computing, finite element methods, inverse problems in mathematical physics, shape optimization in differential forms, and computational finance. Education: PhD in Applied and Computational Mathematics, Chinese University of Hong Kong, 2009 MS in Computer Science, Wuhan University, 2004 BS in Mathematics, Wuhan University, 2001 Professor Li's research spans multiple areas of computational mathematics with a particular emphasis on inverse problems and their applications. His work combines theoretical analysis with practical numerical methods to solve challenging problems in mathematical physics. He has made significant contributions to the development of globally convergent numerical methods for coefficient inverse problems, finite element methods for high-order PDEs, and optimization techniques using differential forms. His research has applications across various domains including electromagnetic scattering, wave propagation, and computational finance. Analysis of Professor Li's recent publications reveals a strong focus on inverse problems, particularly in the areas of coefficient identification, scattering theory, and phaseless data reconstruction. His work demonstrates consistent innovation in developing convexification methods for solving nonlinear inverse problems with guaranteed global convergence. The research spans multiple mathematical disciplines including partial differential equations, numerical analysis, and optimization theory, with applications in physics, engineering, and medical imaging. Scientific Awards: Career Award, Shenzhen, 2021 Excellent Mentor Award, Shude Residential College, SUSTech, 2020 Dual-excellence Award, Faculty of Science, SUSTech, 2020 Highlight Award, Faculty of Science, SUSTech, 2019 Excellent Research Award, SUSTech, 2016 Excellent Mentor Award, SUSTech, 2016 Peacock Award (Tier B), Shenzhen, 2013 Best Doctoral Dissertation Award, Mathematical Society of Hong Kong, 2011 Shenzhen Excellent Talent Project (Outstanding Young Scientists Project), 2021 National Key Talent Program Youth Project, Mathematics and Science, 2012 Professor Li has demonstrated strong commitment to student mentorship, evidenced by multiple Excellent Mentor Awards from SUSTech. His research has been supported by significant grants including the National Key Talent Program Youth Project and Shenzhen's Peacock Plan. His collaborative work spans multiple institutions, particularly with ETH Zurich and Chinese Academy of Sciences, reflecting a strong international research network. While specific grant details aren't provided in the text, his extensive publication record in top journals suggests substantial research funding. Professor Li's research activities are centered around computational mathematics with particular strength in inverse problems. His work connects theoretical mathematics with practical applications across physics and engineering domains. The consistent publication record in high-impact journals demonstrates an active and productive research program with significant contributions to the field of computational inverse problems.

James Legerمشاهده پروفایل

استاد

• Diffractive Optics
• Micro/Nano Optics
• Laser Resonators
• +۵ مورد دیگر

James Leger is a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota , holding the Cymer Professorship for Advanced Optical Systems, Metrology, and Lasers. He directs the Leger Research Group , focusing on micro/nano optics, diffractive optics, and computational imaging for electro-optic and biomedical applications. Education: BS in Applied Physics (Caltech, 1974), PhD in Electrical Engineering (UCSD, 1980) Key Research Areas: Coherent beam combining, laser resonator design, gradient-index metrology, and biomedical sensing via surface plasmon resonance Article Trends: Recent work emphasizes non-line-of-sight imaging using plenoptic data, THz radiation, and spectral unmixing, alongside gradient-index beam shapers and laser vortex generation . His group investigates how passive systems can extract 3D information from scattered light and optimize beam combining architectures. Awards: OSA Fellow (1995), IEEE Fellow (2002), SPIE Fellow (2002), Joseph Fraunhofer Award (1998), George Taylor Research Award (2000) Students: Mentored 17 PhD/MS graduates, including Todd Ballen, Qiwen Zhan, and Eric Shields, with ongoing advising of 7 current students Teaching & Service: Directed lower-division programs at the University of Minnesota, served as Applied Optics topical editor (1997-2003), and chaired conferences for OSA and SPIE . Holds 16 patents in diffractive optics and laser beam combining.

• Analysis
• Applied Mathematics
• Probability
• +۴ مورد دیگر

Nicholas F. Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University's College of Science. His academic journey includes a Ph.D. in Applied Mathematics from Yale University (2019) and a B.S. in Mathematics from Clarkson University (2014), with additional research experience at Princeton University as an NSF Postdoc. Ph.D. in Applied Mathematics, Yale University, 2019 B.S. in Mathematics, Clarkson University, 2014 His research focuses on the interplay between analysis, geometry, and probability, particularly as applied to data science challenges. Current investigations include harmonic analysis on geometric domains, randomized algorithms for linear systems, and mathematical frameworks for cryo-electron microscopy. His work bridges pure mathematical theory with computational applications in imaging and machine learning. Analysis of his recent publications reveals strong trends in computational harmonic analysis, with significant contributions to fast algorithms for spherical and disk harmonics, randomized linear solvers with momentum acceleration, and geometric approaches to hyperdimensional computing. His work consistently connects abstract mathematical concepts to practical computational problems in imaging and data science. Dr. Marshall actively mentors graduate students including Wyatt Whiting, Peter Cowal, and Heather Fogarty, and has supervised notable undergraduate research projects leading to publications in SIAM journals. His current teaching portfolio includes advanced courses in probability theory, numerical linear algebra, and data science mathematics. He maintains active research collaborations with institutions including Princeton University and Yale, focusing on applications in cryo-EM imaging and computational geometry. Personal interests include skiing (learned in Vermont) and kayaking along the Oregon Coast.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +۱۵ مورد دیگر

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.

• Tropical Geometry
• Convex Optimization
• Algorithmic Game Theory
• +۷ مورد دیگر

Mateusz Skomra is a CNRS researcher currently based at the Laboratoire d'analyse et d'architecture des systèmes (LAAS) in Toulouse, France, where he is a member of the POP research team. Previously, he held postdoctoral positions at École normale supérieure de Lyon and CNRS within the Laboratoire de l'Informatique du Parallélisme and the MC2 team. He earned his PhD from École polytechnique, Centre de Mathématiques Appliquées, under the supervision of Xavier Allamigeon and Stéphane Gaubert, as part of the INRIA Tropical team and Université Paris-Saclay. His research lies at the intersection of tropical geometry, convex optimization, algorithmic game theory, and algebraic complexity. He has made significant contributions to the understanding of tropical convexity, semidefinite programming over nonarchimedean fields, mean payoff and stochastic games, and algebraic reconstruction problems. His work often bridges theoretical mathematics with computational aspects, particularly in optimization and complexity theory. The analysis of his recent publications reveals a consistent focus on tropical methods in optimization and game theory, with increasing attention to algorithmic and computational implications. His work connects deep algebraic structures with practical algorithm design, especially in deriving complexity bounds and developing solution methods for games and polynomial systems. Key recurring themes include value iteration, derandomization, Minkowski sums, and the interplay between geometry and algebra in computational settings. Prix Dodu for best presentation by a young researcher Mateusz Skomra has advised doctoral research during his PhD under leading experts and continues to mentor students through master’s internships, such as one on analyzing mean payoff games using sums-of-squares techniques. While specific grant details are not listed, his affiliations with CNRS, INRIA, and participation in numerous national and international workshops suggest active involvement in collaborative research projects. He frequently contributes to academic discourse through seminars and conference presentations across Europe. He is actively involved in research teams including the POP team at LAAS-CNRS, previously the MC2 team at ENS Lyon and the Tropical team at INRIA. These teams focus on mathematical computing, optimization, and theoretical computer science, providing a rich interdisciplinary environment for his work.

• Artificial Intelligence
• Multi-Agent Systems
• Deep Learning
• +۷ مورد دیگر

Assoc Prof Kaile Su is an Associate Professor at the School of Information and Communication Technology, Griffith University, with expertise in artificial intelligence, multi-agent systems, and deep learning. They hold an ORCID identifier (0000-0001-6741-9699) and have been affiliated with Griffith University since 2004. PhD in Computer Science from Nanjing University (1995) Postdoctoral work at Changsha Institute of Technology Their research spans combinatorial optimization, temporal logic verification, speech enhancement, and medical imaging applications. Recent work focuses on edge AI, federated learning, and neural network regularization techniques. Key funded projects include ARC Discovery Grants (DP150101618, DP120102489) and an ARC Future Fellowship (FT0991785). Awards include the NSFC Award for Distinguished Young Scholar (2007). Supervised 9 doctoral students at Griffith University Contributions to multi-agent coordination, CT reconstruction, and dialogue systems Active in software verification and sparse graph optimization

Yu Yueمشاهده پروفایل

استاد

• Bayesian statistics
• Nonparametric Regression
• Spatial Statistics
• +۲ مورد دیگر

Yu Yue is a Professor at the Zicklin School of Business, Baruch College, The City University of New York, in the Paul H. Chook Department of Information Systems and Statistics. He holds a PhD and MA in Statistics from the University of Missouri, Columbia, and a BS in Statistics from Shanghai University of Finance and Economics. His research focuses on Bayesian statistics, nonparametric regression, spatial statistics, quantile regression, and functional data analysis. His recent publications include innovations in Bayesian trend filtering for neuroimaging data spatio-temporal modeling of muscle activity adaptive smoothing techniques for fMRI analysis with applications in neuroscience, public health, and computational statistics. Scientific Awards Teaching Excellence Award (Zicklin School of Business, 2011) Eugene M. Lang Junior Faculty Research Fellowship (Baruch College, 2012) SAMSI workshop travel awards (2009, 2014, 2017) R. L. Anderson Award (Southern Regional Council on Statistics, 2007) G. Ellsworth Huggins Fellowship (University of Missouri, 2003) He has advised PhD students Bin Ma (2016-2017) and Zhu Zhu (2015-2016) and received grants from PSC-CUNY and Indiana University for Bayesian modeling of neuroimaging data and statistical methods development.

• Machine Learning
• Computational Biology
• Applied Mathematics
• +۴ مورد دیگر

George Linderman is an Assistant Professor in the Department of Applied Mathematics at Yale University's College of Arts and Sciences. He completed his MD-PhD training at Yale, where he focused on high-dimensional data analysis. Currently, he is a General Surgery Resident at Massachusetts General Hospital, applying his computational expertise to clinical training. University: Yale University Current Role: General Surgery Resident at Massachusetts General Hospital Email: george.linderman@yale.edu, glinderman@mgh.harvard.edu His research spans computational methods for data analysis, including t-SNE acceleration, low-rank matrix approximation, and concentration inequalities. He has developed software packages like FIt-SNE and ALRA , which are widely used in scRNA-seq data visualization and imputation. The articles listed reflect his contributions to machine learning, computational biology, and clinical research. Notably, his work on t-SNE initialization and scRNA-seq dimensionality reduction has advanced data visualization techniques, while clinical studies on REBOA and laparoscopic surgery highlight his interdisciplinary impact. He has mentored students through Yale's Directed Reading Program for High Dimensional Probability and served as a Teaching Fellow for Linear Algebra. His academic journey combines rigorous theoretical work with practical applications in both computational biology and surgery.

Xiaobai Sunمشاهده پروفایل

استاد

• Numerical Analysis
• Matrix Theory
• High-Performance Scientific Computing
• +۸ مورد دیگر

Xiaobai Sun is a full-time Professor of Computer Science at Duke University's Trinity College of Arts & Sciences, with a career spanning from 1994 to present. His research focuses on Numerical Analysis, Matrix Theory, High-Performance Scientific Computing, Parallel Computing, and Graph Clustering . He received his PhD from the University of Maryland, College Park in 1991. Education : Ph.D., University of Maryland, College Park (1991) Research Interests encompass: Numerical Linear Algebra Graph Algorithms Parallel Computing Scientific Simulation Medical Imaging Optical Engineering Publication Trends show a consistent focus on graph algorithms, numerical methods, and high-performance computing applications across biomedical imaging, optical systems, and physical simulations. Grants include: NIH funding (2019-2021) for Hybrid virtual-MRI/CBCT in liver SBRT NSF funding (2009-2015) for Integral Equation-Based Fast Algorithms NIH funding (2014-2020) for LIVE System in SBRT Treatments ARO funding (2013-2014) for computing platform development Professional Activities include Bass Connections Faculty Team Memberships (2013-2016) focusing on energy systems analysis and scientific document analysis.

Danfeng Zhangمشاهده پروفایل

دانشیار

• Computer Security
• Differential Privacy
• Information Flow Analysis
• +۴ مورد دیگر

Danfeng Zhang is an Associate Professor of Computer Science at Duke University's Trinity College of Arts & Sciences since 2024. He received his Ph.D. from Cornell University in 2015 . Specializes in computer security and privacy Focuses on timing channels, differential privacy, and formal verification Develops static analysis tools for security guarantees His recent research includes: LLM-assisted verification (HOTOS 2025) Per-user differential privacy in online advertising (IEEE Security & Privacy 2025) GPU uncore vulnerabilities (MICRO 2024) Cache timing analysis (POPL 2024) Key funded projects: NSF SaTC Frontiers (2023-2027): Center for Distributed Confidential Computing CAREER Grant (2023-2026): Non-static information flow policies NSF-Penn State Collaboration (2024-2028): Differentially Private SQL systems Teaching: COMPSCI 390 (System Software) - Spring 2025, 2024 COMPSCI 590 (Advanced Topics) - Fall 2024

• Fractional Calculus
• Numerical Analysis
• Partial Differential Equations
• +۴ مورد دیگر

Professor Suzan Cival Buranay is a faculty member at the Faculty of Arts and Sciences, Eastern Mediterranean University, serving as Department Head in Mathematics. She earned her PhD in Mathematics (2007), MS in Applied Mathematics (1995), and BS in Applied Mathematics (1993) from the same university. Research Interests: Her work focuses on Fractional calculus and its applications to differential equations Numerical analysis of finite difference schemes and hexagonal grids Approximation theory with modified Bernstein-Kantorovich operators Preconditioning techniques for linear systems and ill-posed problems Computational methods for heat equations and Volterra integral equations Recent Contributions: She has developed high-accuracy numerical methods for fractional boundary value problems, Caputo derivatives, and singular integral equations. Her work on hexagonal grid approximations for heat equation derivatives has advanced computational efficiency. Thesis Supervision: She has advised 5 PhD students and 6 MS students, with ongoing supervision of two PhD/MS theses. Her supervised works include collocation methods for fractional equations and implicit schemes for heat equations. Projects: Currently leading a project on collocation methods for generalized Bagley-Torvik equations (2023-2024), and previously worked on block-grid methods for Laplace equations with singularities (2010-2011).

Marc Lelargeمشاهده پروفایل

استاد

• Generative AI
• Interactive Theorem Proving
• Deep Learning
• +۴ مورد دیگر

Marc Lelarge is a Researcher at INRIA and a part-time Professor in the Department of Computer Science at École Normale Supérieure. He received his PhD in Applied Mathematics from École Polytechnique in 2005, following engineering qualifications from École Nationale Supérieure des Télécommunications and undergraduate studies at École Polytechnique. His research focuses on generative AI for coding and interactive theorem proving, deep learning for graphs and structured data, machine learning, and programming languages. Recent publications emphasize graph neural networks, theorem proving, optimization algorithms, and statistical learning theory. He has received multiple awards including the NetGCoop Best Paper Award (2011), SIGMETRICS Rising Star Award (2012), and Best Publication in Applied Probability Award (2015). He developed the 'Deep Learning: Do It Yourself' course and maintains active educational resources on GitHub. Current affiliations include collaborations with NVIDIA (hardware donations) and Google (cloud computing grants). He advises PhD students including E. Coupechoux and maintains research teams focused on deep learning implementations.

Stanislav Volgushevمشاهده پروفایل

دانشیار

• Data Science
• Probability
• Theoretical Statistics
• +۷ مورد دیگر

Stanislav Volgushev is an Associate Professor in the Department of Statistical Sciences at the University of Toronto, with a cross-appointment in the Department of Mathematical & Computational Sciences at the University of Toronto Mississauga (UTM). His work bridges theoretical statistics with practical applications in big data analysis, with a focus on developing robust statistical methods for complex data structures. His educational background includes: Ph.D. in Mathematics from Ruhr University Bochum (2010), advised by Prof. Holger Dette Diploma in Mathematics from Ruhr University Bochum (2007) Volgushev's research spans multiple areas of statistical theory and methodology. His primary interests include quantile regression, empirical process theory, extreme value theory, time series analysis, copulas, and bootstrap methods. He is particularly focused on addressing statistical and computational challenges presented by big data, developing methods that maintain theoretical rigor while being practically applicable to modern data analysis problems. His work often involves the development of nonparametric and semiparametric methods that can handle complex dependence structures. Analysis of his recent publications reveals a strong focus on high-dimensional statistics, with particular attention to change-point detection, extremal graphical models, and quantile-based methods. His work bridges theoretical statistics with machine learning, showing increasing interest in optimization methods for statistical learning and causal inference. There's a clear trajectory toward addressing computational challenges in modern statistics, with many papers developing methods specifically designed for big data contexts. Volgushev serves as an associate editor for several prestigious journals: Electronic Journal of Statistics (since 2022) Canadian Journal of Statistics (since 2022) Scandinavian Journal of Statistics (since 2021) Statistical Inference for Stochastic Processes (since 2014) Previously served at Computational Statistics and Data Analysis (2018-2023) and Bernoulli (2016-2018) He has taught various courses including STA107 (Introduction to Probability and Modelling), STA2211 (Probability Theory II), and STA314 (Introduction to Statistical Learning) at both the St. George and Mississauga campuses. His teaching reflects his research expertise, covering both theoretical foundations and modern applications of statistical methods. Administratively, he serves as Associate Chair, Graduate Studies in his department, demonstrating leadership in academic governance.

• Applied Mathematics
• Probability
• Statistics
• +۵ مورد دیگر

Marie-Eliette DURY is an Associate Professor in Applied Mathematics at the School of Economics, University of Clermont Auvergne (UCA). She serves as Head of the Economics and Modern Languages Degree program and is an IPPA Trainer and EE Ambassador at the IPPA Center. Her research bridges probability, statistics, and quantitative finance, with a focus on financial market efficiency, volatility modeling, and climate-related risk assessment in developing economies. Key Affiliations: Centre d'Études et de Recherches sur le Développement International (CERDI), Laboratoire de Mathématiques de Clermont Research Interests: Her work spans extreme value theory , stable distributions , multifractional Brownian motion , and index insurance mechanisms . She specializes in volatility forecasting for financial and commodity markets, particularly gold and nickel, and develops spatially consistent drought hazard models for West Africa. Recent Article Trends: DURY’s publications (2015–2024) emphasize stochastic processes (GARCH/ARCH, stable models), climate risk quantification (droughts, extreme precipitation), and financial market anomalies (Shanghai vs. Shenzhen stock markets). Her methodologies integrate wavelet decomposition, p-variations, and mixture models. Grants & Projects: Involved in ANR projects CAP 20-25 and Design of Well Being Monitoring Systems , alongside CNRS-funded collaborations. Collaborations: Frequently co-authors with Bing Xiao and Nourddine Azzaoui on cross-disciplinary topics.

Xavier Dupuisمشاهده پروفایل

مدرس

• Optimization
• Numerical Methods
• Machine Learning
• +۲ مورد دیگر

Xavier Dupuis is a Lecturer (Maître de conférences) at the Institut de Mathématiques de Bourgogne, Université de Bourgogne, France. His research focuses on optimization, numerical methods, and machine learning with applications in economics, biology, and medicine. Education: PhD in Applied Mathematics, École Polytechnique (2013) Master's degree in Mathematics, École Normale Supérieure de Lyon (2010) Agrégation de Mathématiques (2009) Research Focus: His work spans optimal control theory (finite/infinite dimensions), sparse regularization (SLOPE), and numerical schemes for partial differential equations, with cross-disciplinary applications in biomedical modeling and economics. Publication Trends: Recent articles (2022-2024) primarily address SLOPE regularization theory and computational methods, while earlier work (2013-2017) established foundations in optimal control theory and variational problems. Affiliations: Member of the Statistics, Probability, Optimization and Control research team at Institut de Mathématiques de Bourgogne.