جستجوی استاد راهنمای دانشگاهی

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.

Remco van der Hofstadمشاهده پروفایل

استاد

• Probability Theory
• Statistics
• Complex Networks
• +۶ مورد دیگر

Remco van der Hofstad is a Full Professor and chair of probability at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He also serves as scientific director of EURANDOM and co-applicant of the Gravitation program NETWORKS. His research spans probability theory, statistics, and their applications in complex networks, statistical physics, and medical imaging. PhD from University of Utrecht (1997) Positions at McMaster University, Delft TU/e, and TU/e His research focuses on probability questions related to complex networks, including percolation, random graphs, self-interacting processes, and interdisciplinary applications in electrical engineering, computer science, and theoretical physics. Recent work includes medical imaging for glioma patients and theoretical advancements in PageRank analysis, sparse matrices, and dynamic intersection graphs. Key awards include the Prix Henri Poincaré (2003), Rollo Davidson Prize (2007), and Netherlands Organisation for Scientific Research (NWO) VIDI (2003) and VICI (2008) grants. He is editor-in-chief of the Network Pages and acts as spokesman for Platform Wiskunde Nederland. Teaches courses: Probability theory, Probability and Modeling, Probability and Statistics Ancillary activities: Chair of Applied Probability Trust, member of Dutch Chamber of NetSci, KNAW councils

• 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.

Felix Krahmerمشاهده پروفایل

دانشیار

• Optimization and Data Analysis
• Compressed Sensing
• Unlimited Sampling
• +۷ مورد دیگر

Prof. Dr. Felix Krahmer is an Associate Professor for Optimization and Data Analysis at the Department of Mathematics, Technical University of Munich (TUM) . His research focuses on mathematical foundations of data science, with expertise in Compressed Sensing Unlimited Sampling Quantization of Signals and Images Neural Networks Uncertainty Quantification Low-Rank Recovery He leads the Data Science research group within TUM's School of Computation, Information and Technology, collaborating with institutions like TU Berlin, RWTH Aachen, and Technion. His recent publications span topics like Robust blind deconvolution under adversarial noise Kronecker-structured dimensionality reduction Hysteresis in unlimited sampling systems Efficient sparse FFT algorithms Notable scientific awards include two August-Wilhelm-Scheer Visiting Professorships (2016, 2019) for hosting international collaborators. Former and current students include Olga Graf Dominik Stöger Claudio Verdun Felipe Pagginelli Patricio Juana Kostin He has co-organized workshops at SAMPTA, Oberwolfach, and ISIT, serving as associate editor for journals including SIAM Journal on Matrix Analysis and Frontiers in Signal Processing.

David Gamarnikمشاهده پروفایل

استاد

• Discrete Probability
• Optimization
• Algorithms
• +۴ مورد دیگر

David Gamarnik is a Professor of Operations Research at the Operations Research and Statistics Group within the Sloan School of Management at the Massachusetts Institute of Technology (MIT). He holds a PhD in operations research from MIT (1998) and a BA in mathematics from New York University (1993). Prior to joining MIT in 2005, he was a research staff member at IBM T.J. Watson Research Center. Research Interests : Discrete probability, optimization and algorithms, quantum computing, statistics, machine learning, and stochastic processes. His recent work focuses on algorithmic obstructions in random structures, quantum optimization, and high-dimensional statistical models. He has contributed to understanding the overlap gap property in combinatorial problems and the performance of gradient descent in neural networks. Scientific Awards : Fellow of the Institute for Mathematical Statistics (IMS) Fellow of the Institute for Operations Research and Management Science (INFORMS) Fellow of the American Mathematical Society (AMS) Erlang Prize Best Publication Award from the Applied Probability Society of INFORMS Franz Edelman Prize competition finalist He currently serves as an area editor for Mathematics of Operations Research and has previously held editorial roles in Operations Research , Annals of Applied Probability , Queueing Systems , and Stochastic Systems journals.

Weiping Peiمشاهده پروفایل

استادیار

• Security and Privacy
• Crowdsourcing
• Machine Learning
• +۳ مورد دیگر

Weiping Pei is an Assistant Professor in the School of Cyber Studies at The University of Tulsa, specializing in security and privacy, crowdsourcing, machine learning, and human-computer interaction. Her work bridges technical security mechanisms with human behavioral aspects to develop practical privacy-preserving systems. Education: Ph.D. in Computer Science, Colorado School of Mines (2022) M.Eng. in Software Engineering, Sun Yat-sen University (2017) B.Eng. in Microelectronics, Sun Yat-sen University (2015) Dr. Pei's research centers on three interconnected pillars: secure and privacy-preserving crowdsourcing (with focus on receipt transcription systems and third-party privacy), robust machine learning (particularly adversarial attacks against vulnerability detection and content-preserving text manipulation), and usable security (including GDPR policy analysis and online grooming detection). Her approach uniquely integrates human behavioral modeling with cryptographic and machine learning techniques to address real-world security gaps in crowd-powered systems. Analysis of her 2023-2025 publications reveals a strategic shift toward AI-driven security solutions, particularly using LLMs for behavioral analysis in digital forensics and adversarial machine learning for vulnerability assessment. Her work consistently targets high-impact venues like IEEE S&P and ACM CCS, demonstrating both theoretical rigor and practical applicability in privacy policy compliance and crowdworker security. Scientific Awards: NSF CAREER Award (2025) for foundational cybersecurity research NSF SaTC 2.0 RES Award (2025) for privacy-preserving technologies NSF CRII Grant recipient (2023) Distinguished Rath Award nominee for societal-impact PhD thesis (2022) Outstanding CS Ph.D. Student Award at Mines (2021) Grace Hopper Celebration Scholarship (2018) Dr. Pei actively mentors four students across degree levels while leading multiple NSF-funded projects totaling $1.3+ million. Her research group develops tools for privacy policy analysis (PolicyChecker), adversarial vulnerability testing (AdVul), and online safety monitoring. She teaches core cybersecurity courses including Data Security and Cryptography, with future work focusing on AI security foundations through her NSF CAREER grant.

• Matrix Theory
• Combinatorics
• Linear Algebra
• +۲ مورد دیگر

Louis A. Deaett is a Professor of Mathematics at Quinnipiac University , affiliated with the College of Arts & Sciences and the Department of Mathematics and Statistics . He holds a PhD in Mathematics from the University of Wisconsin-Madison (2009), preceded by dual bachelor's degrees (BS and BA) from the University of Rochester. His career spans postdoctoral work at the University of Victoria and teaching at Quinnipiac since 2011. Education BS, University of Rochester BA, University of Rochester PhD, University of Wisconsin-Madison Deaett's research focuses on the interplay between matrix theory , combinatorics , and graph theory . His work addresses problems like the minimum rank of graphs , spectrally arbitrary patterns , and the application of matroid theory to matrix pattern analysis. He emphasizes pedagogical innovation, seeking to make mathematical truths accessible through discovery-based learning. Recent publications highlight his exploration of computational challenges in graph theory, algebraic conditions for sparse matrices, and connections between matroids and matrix patterns. His academic contributions are documented in journals such as the INFORMS Journal on Computing , Special Matrices , and The American Mathematical Monthly .

Henry Cohnمشاهده پروفایل

استاد

• Discrete Mathematics
• Sphere Packing
• Conformal Field Theory
• +۸ مورد دیگر

Henry Cohn is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), specializing in discrete mathematics, sphere packing, conformal field theory, and fast matrix multiplication. His work bridges pure and applied mathematics, emphasizing symmetry in exceptional structures and applications to physics, cryptography, and information theory. He supervises graduate students and research interns, particularly at Microsoft Research and MIT. Education: Ph.D., Mathematics, Harvard University (2000) B.S., Mathematics, MIT (1995) Professional Background: Postdoc to long-term member, Microsoft Research Redmond (2000–2007) Head, Cryptography Group, Microsoft Research (2007–2008) Founding member, Microsoft Research New England (2008–2024) Professor, MIT Department of Mathematics (2025–present) Research Interests: Cohn explores symmetry in mathematics and physics, focusing on sphere packing, potential energy minimization, and applications of harmonic analysis and representation theory. His work spans discrete geometry, coding theory, cryptography, and theoretical computer science. Publications & Grants: He has published extensively on sphere packings, error-correcting codes, and matrix multiplication, often leveraging modular forms and semidefinite programming. His research is supported by institutions like the Simons Foundation. Teaching & Mentorship: Cohn teaches courses in number theory, discrete geometry, and mathematical logic at MIT and programs like PROMYS. He has advised numerous Ph.D. students and research interns, including Yichi Zhang, James Hirst, and Alex Cohen.

• Numerical Analysis
• Computational Mathematics
• Sparse Signal Processing
• +۴ مورد دیگر

Dr. Toni Volkmer is a researcher affiliated with the Faculty of Mathematics at Chemnitz University of Technology. His work focuses on computational mathematics and signal processing, particularly in high-dimensional data analysis and sparse Fourier transforms. His research intersects numerical analysis, approximation theory, and algorithm design for efficient data processing. Key research areas include Sparse spectral estimation Rank-1 lattice sampling Multivariate function approximation High-dimensional data analysis Fast matrix-vector operations Graph Laplacian computations His publications demonstrate expertise in developing sublinear-time algorithms for harmonic analysis and creating efficient numerical methods applicable to modern data science challenges. While no formal awards are listed, his contributions to scientific computing have been documented in multiple peer-reviewed publications since 2012.

François Chartonمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Mathematics
• Theoretical Physics
• +۳ مورد دیگر

François Charton is a Researcher at Meta (FAIR) and affiliated with École des Ponts ParisTech / CERMICS . His work bridges Artificial Intelligence and Mathematics/Physics , focusing on transformers for symbolic mathematics, cryptanalysis, and theoretical physics applications. Education: École Polytechnique (1987), ENSEEIHT (1990), majoring in statistics Research Interests include symbolic transformers, mathematical modeling, AI for theoretical physics, and cryptanalysis. He has pioneered transformer-based approaches for symbolic integration , Lyapunov function discovery , and lattice cryptography attacks . Recent publications show a trend toward using transformers for generalization in mathematical tasks , scattering amplitude computation , and model collapse in scaling laws . Key conferences include NeurIPS, ICML, and ICLR. Scientific Awards: Debunking Challenge (NeurIPS 2024 workshop) Notable Collaborations: Albert Alfarano, Geordie Williamson, Julia Kempe, Kristin Lauter Charton co-organized workshops at Harvard CMSA and IHES , and has delivered invited talks at Princeton IAS , CERN , and Collège de France .

• Quantum Information Theory
• Quantum Computing
• Quantum Error Mitigation
• +۳ مورد دیگر

Dr. Balint Koczor is an Associate Professor in the Mathematical Institute at the University of Oxford, leading the Quantum Information, Computation and Cryptography Group. He holds a Future Leaders Fellowship, serves as Associate Editor for npj Quantum Information , and organizes the SEEQA conference series. His work bridges theoretical quantum computing with industry applications through AppliedQC.org. His academic credentials include an MSc and Dr rer. nat. from Technical University of Munich (2019). Dr. Koczor's research centers on making quantum computers practical for real-world use, specializing in quantum error mitigation techniques for near-term devices. He develops hybrid quantum-classical algorithms to maximize hardware performance, with emphasis on error correction, efficient circuit design, and quantum simulation. His work directly addresses hardware limitations through close collaboration with experimental teams and quantum technology companies. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on practical quantum computing solutions. Key themes include classical shadows for efficient state estimation, virtual distillation for error suppression, and CoVaR-based optimization methods. His work consistently targets near-term device constraints while advancing foundational quantum algorithms across simulation, spectroscopy, and cryptography applications. His scientific recognition includes: Future Leaders Fellowship As a Future Leaders Fellow, Dr. Koczor directs significant research funding toward quantum error mitigation and algorithm development. He actively collaborates with quantum hardware companies to optimize application-hardware integration, with research supported through his fellowship and industry partnerships. His group develops tailored solutions for current quantum platforms while mentoring junior researchers. He leads the Quantum Information, Computation and Cryptography Group at Oxford's Mathematical Institute, which operates in close coordination with AppliedQC.org. This team focuses on translating theoretical advances into practical quantum computing frameworks, emphasizing error-resilient algorithms and hardware-aware implementations for near-term quantum devices.

• Control Engineering
• Systems Identification
• Machine Learning
• +۳ مورد دیگر

Håkan Hjalmarsson is a full Professor at the KTH Royal Institute of Technology , affiliated with the Division of Decision and Control Systems within the School of Electrical Engineering and Computer Science . He serves as examiner for advanced-level degree projects in Computer Science , Electrical Engineering , and Systems Engineering , while also contributing as assistant, course manager, and teacher for Control Engineering courses. Research focus on Control Theory , System Identification , and Data-Driven Control Expertise in Optimal Experiment Design , Sparse Estimation , and Bayesian Methods Active in Industrial Applications including bioprocess optimization and network control His recent publications (2024-2025) emphasize finite-time regret minimization , sparse system identification , dynamic programming exploration , and Bayesian approaches to control problems. Key subfields include linear quadratic control , Markov parameter estimation , Wiener-Hammerstein models , and metabolic network identification . He has no listed scientific awards in the provided data.

Randal Burnsمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Cloud Storage Systems
• +۴ مورد دیگر

Randal Burns is a Professor and the Bill and Lisa Stromberg Head of the Department of Computer Science in the Whiting School of Engineering at Johns Hopkins University. He is also affiliated with the Data Science and AI Institute and co-founder of NeuroData. His research focuses on scalable data systems for scientific applications, spanning storage technologies, cloud infrastructure, and graph/sparse-matrix engines for machine learning. Key themes include data-intensive science, neuroscience imaging analysis, and adaptive performance management. Recent publications highlight trends in edge-parallel graph encoders masked matrix operations for sparsity decentralized foundation model training storage optimization for evolving AI models batch-parallel data structures turbulence data analysis These reflect his work on scalable systems for machine learning and scientific computing. Scientific awards include NSF CAREER Award DOE Early Career Principal Investigator Kavli Fellowship IBM Outstanding Innovation Award Advising: Mentors PhD students in scalable data systems and machine learning. Grants: Supported by NSF, DOE, and DARPA initiatives. Labs: Core member of the Johns Hopkins Turbulence Database Group and NeuroData team, developing open platforms for neuroscience and turbulence research.

Irini Moustakiمشاهده پروفایل

استاد

• Latent Variable Models
• Structural Equation Models
• Categorical Data Analysis
• +۵ مورد دیگر

Irini Moustaki is a full-time Professor in Social Statistics at the Department of Statistics , London School of Economics and Political Science . Her work focuses on latent variable models, structural equation models, and categorical data analysis with applications in education, psychology, and social sciences. She has co-authored influential books such as Latent Variable Models and Factor Analysis: A Unified Approach (2011) and Analysis of Multivariate Social Science Data (2008). Research Interests : Latent variable models, structural equation modeling, categorical data analysis, missing values, outliers, longitudinal data, composite likelihood estimation, and applications in social sciences. Recent research trends include: Composite likelihood and stochastic approximation methods Bayesian structural equation modeling Outlier detection in latent variable frameworks Network meta-analysis for intervention hierarchies Advances in DIF analysis and sparse factor modeling Scientific Awards : Honorary Doctorate, University of Uppsala Honorary Professor, The Education University of Hong Kong LSE Excellence in Education Award Advising & Grants : Supervised 8 PhD students and contributed to major grants from ESRC, MRC, and British Academy. Key roles in editorial boards of Psychometrika , Computational Statistics and Data Analysis , and Structural Equation Modeling .