جستجوی استاد راهنمای دانشگاهی

• Applied Mathematics
• Computational Science
• Plasma Physics
• +۷ مورد دیگر

Giovanni Samaey is a Professor of Applied Mathematics and Mathematical Engineering at KU Leuven's Faculty of Engineering Science. He leads research in computational and multiscale methods, focusing on plasma edge modeling for nuclear fusion reactors, Bayesian inversion, and Monte Carlo algorithms. Appointed in 2011, he currently supervises ten PhD students and has held a five-year membership in the Young Academy. His work bridges academic research with societal impact, co-founding Platform Wiskunde Vlaanderen to strengthen mathematics in Flanders. Education: Graduated in Computer Science (specializing in applied mathematics) from KU Leuven (1996), completed a PhD in 2001 under Prof. Dirk Roose, supported by an NFWO fellowship. He transitioned from engineering studies due to a passion for mathematics' societal impact, initially avoiding academia but ultimately embracing teaching and research. Research Interests: Development of numerical methods for multiscale phenomena, including micro-macro acceleration algorithms, multilevel Monte Carlo techniques, and hybrid fluid-kinetic models. His work addresses challenges in plasma physics, fusion energy systems, and inverse problems. Key contributions include the X-Factor book (with Joos Vandewalle) promoting mathematics outreach and advancing computational tools for plasma edge simulations. Awards/Honors: Member of the Young Academy (2016-2021), NFWO Aspirant Fellowship (2001-2002). His efforts in STEM advocacy and mathematics promotion through Platform Wiskunde Vlaanderen highlight his dedication to education and public engagement. Advising & Leadership: Supervises a team of PhD students in applied mathematics and computational science. Active in curriculum development and interdisciplinary collaborations, particularly in fusion energy modeling. His research group contributes to codes like EMC3-EIRENE for plasma edge simulations. Labs/Teams: Leads projects in multiscale numerical methods and plasma simulation within KU Leuven's engineering faculty. Collaborates internationally on fusion reactor modeling and Monte Carlo algorithm design, emphasizing computational efficiency and scalability.

Constantine Caramanisمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• High-Dimensional Statistics
• +۲ مورد دیگر

Constantine Caramanis is a Professor and holder of the Chandra Family Endowed Distinguished Professorship in Electrical and Computer Engineering at the University of Texas at Austin. His research focuses on decision-making in large-scale complex systems, specializing in robust optimization, high-dimensional statistics, machine learning, and applications to networks including social, wireless, transportation, and energy systems. Caramanis directs a substantial research group and is affiliated with the NSF Institute for Foundations of Machine Learning and the UT Machine Learning Lab. He has made significant contributions to optimization theory, developing efficient algorithms for matrix sensing, non-convex problems, and combinatorial optimization. His recent work bridges machine learning and control theory through latent MDPs, diffusion models for inverse problems, and bandit algorithms with fairness considerations. Caramanis has received prestigious recognition including the NSF CAREER award and IEEE Fellowship for his theoretical advances in optimization and learning.

Noah Stephens-Davidowitzمشاهده پروفایل

استادیار

• Lattices
• Theoretical Computer Science
• Cryptography
• +۳ مورد دیگر

Noah Stephens-Davidowitz is an Assistant Professor in the Department of Computer Science at Cornell University, with affiliations to the Applied Mathematics and Mathematics fields. He holds a Ph.D. from New York University (2017), advised by Oded Regev and Yevgeniy Dodis. His research focuses on lattices, theoretical computer science, cryptography, and geometry. Previously, he was a Simons Institute fellow, a postdoc at MIT and Princeton, and a visiting researcher at the Institute for Advanced Study. Education: Ph.D. in Computer Science from New York University (2017). Affiliated colleges at Cornell include the Ann S. Bowers College of Computing and Information Science, College of Arts and Sciences, and College of Engineering. Research Interests: Lattice-based cryptography, algorithmic complexity, geometric algorithms, and cryptographic hardness assumptions. His work bridges theoretical foundations and practical applications, including secure messaging protocols, cryptographic protocol design, and lattice reduction techniques. Notable Contributions: Developed reverse Minkowski theorems, advanced lattice reduction algorithms, and analyzed computational hardness of lattice problems. His work on secure messaging with reverse firewalls and time-space tradeoffs for function inversion has been influential in cryptography. Advising: Supervises four PhD students: Yael Eisenberg, Kyle Fridberg, Surendra Ghentiyala, and Spencer Peters. Active in program committees for conferences like CRYPTO, FOCS, and STOC. Labs/Teams: Involved in the Simons Collaboration on Algorithms and Geometry, and contributes to lattice-based cryptography research initiatives at Cornell's College of Computing and Information Science.

• Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +۴ مورد دیگر

Colin Cotter is a Professor of Computational Mathematics in the Department of Mathematics at Imperial College London , part of the Faculty of Natural Sciences . His research focuses on numerical analysis, scientific computing, and geophysical fluid dynamics, with applications in weather forecasting, ocean modeling, and climate simulation. He is Head of the Applied Mathematics and Mathematical Physics Section and an Associate Editor for the SIAM Journal of Scientific Computing and Foundations of Data Science. Education : PhD in Mathematics, Imperial College London (2000–2004) BA and Part III in Mathematics, Cambridge University (1996–2000) Research Interests : Dr. Cotter develops compatible finite element methods and time-parallel algorithms for geoscientific models. His work includes: Design of geometric variational numerical methods for fluid equations Probabilistic forecasting and Bayesian data assimilation techniques Parallel-in-time algorithms using ParaDiag methods Awards & Roles : EPSRC Peer Review College member Co-author of "Probabilistic Forecasting and Bayesian Data Assimilation" Grants & Labs : Involved in projects funded by EPSRC, NERC, and ExCALIBUR. Active in the EPSRC Centre for Maths of Precision Healthcare and the Mathematics of Planet Earth initiative.

• High-performance Computing
• Parallel Algorithms
• Scientific Computing

Professor Tobias Weinzierl heads the Scientific Computing research group at Durham University's Department of Computer Science. He is Co-director of the Institute for Data Science (IDAS) with responsibility for Large-Scale Computing initiatives and serves as inaugural director of the Master in Scientific Computing and Data Analysis (MISCADA). Professor Weinzierl is a reviewer for the EuroHPC JU and principal investigator on multiple ExCALIBUR projects. His research focuses on parallel algorithms, high-performance computing, and scientific computing methodologies. Key areas include adaptive mesh refinement, task-based parallelism, and exascale computing frameworks. Professor Weinzierl develops the Peano software framework for parallel grid traversals and contributes to the ExaHyPE engine for hyperbolic PDEs. His publications span parallel computing paradigms, performance optimization, and applications in computational geophysics. Research trends include GPU offloading, fault tolerance in HPC, and SYCL programming models. Professor Weinzierl has authored books including Principles of Parallel Scientific Computing and edited volumes such as Advanced Computing . He regularly presents at major HPC conferences and workshops worldwide. He advises postgraduate students working on parallel algorithms, GPU acceleration, and computational science applications. Research groups include the Scientific Computing and Data Analysis initiatives at Durham.

• Stochastic Models
• Applied Probability
• Algorithms in Stochastic Models
• +۳ مورد دیگر

Professor Zhang Hanqin is a faculty member at the Department of Analytics & Operations, National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His research focuses on stochastic models, applied probability, algorithms in stochastic systems, supply chain management, and queueing theory. He has contributed extensively to optimization in inventory systems, manufacturing systems, and service operations. His work spans theoretical advancements and practical applications, including optimal staffing for queues, assemble-to-order systems, and quantity flexible contracts. Recent studies address time-varying queues, minimal phase-type distributions, and impulse control strategies. His research bridges stochastic processes, operations research, and applied mathematics with real-world systems. Key achievements include foundational contributions to diffusion approximations, Coxian representations, and policy design for complex systems. His articles often explore multi-source inventory systems, tandem queues, and expediting strategies, reflecting a deep engagement with both theoretical rigor and industrial relevance.

Justin Tatch Mooreمشاهده پروفایل

استاد

• Set Theory
• Mathematical Logic
• Infinite Combinatorics
• +۴ مورد دیگر

Justin Tatch Moore is a Professor in the Department of Mathematics at Cornell University's College of Arts and Sciences. He holds his office in Malott Hall and has been an active member of the mathematics faculty since completing his Ph.D. at the University of Toronto in 2000. Moore's research spans several areas of mathematical logic and its applications: Set theory and infinite combinatorics Applications to topology, functional analysis, and algebra Groups related to piecewise linear homeomorphisms Forcing axioms and the continuum hypothesis Ramsey theory of infinite sets His work has significantly contributed to solving long-standing problems in set theory, including the basis problem for uncountable linear orders and the L space problem from general topology. Moore's research often explores the connections between set theory and other mathematical disciplines, revealing deep structural relationships. Moore serves as an editor for the Archive of Mathematical Logic, handling papers specifically in set theory. His publications demonstrate a consistent focus on foundational questions in mathematics, with recent work exploring connections between large cardinals, Ramsey theory, and geometric group theory. As an educator, Moore teaches courses including Multivariable Calculus, Supervised Research, and a Seminar in Logic. He has supervised numerous graduate students through research and reading courses, contributing to the next generation of mathematical researchers.

Amos Golanمشاهده پروفایل

استاد

• Info-Metrics
• Information Theory
• Entropy Econometrics
• +۳ مورد دیگر

Amos Golan is a Professor at American University, directing the Info-Metrics Institute. He holds a PhD from the University of California, Berkeley, and advanced degrees from the Hebrew University of Jerusalem. A Fellow of the AAAS and recipient of the 2008 American University Outstanding Scholarship Award, Golan specializes in info-metrics, information theory, and entropy econometrics. His research bridges disciplines, addressing optimal decision-making under imperfect information. He has held visiting roles at institutions like Oxford and the Santa Fe Institute. His work spans econometrics, policy analysis, and complex systems modeling. Notable contributions include foundational texts on info-metrics and pioneering methods for interval estimation and nonlinear dynamics. Golan serves on editorial boards for journals like Journal of Econometrics and Entropy , and teaches advanced courses in econometrics and empirical microeconomics at AU. Awards: AAAS Fellowship, 2008 AU Scholarship Award, 2021 IJF Outstanding Paper Roles: Director of Info-Metrics Institute, Editorial Leadership, Visiting Scholarships

Habib Ammariمشاهده پروفایل

استاد

• Applied Mathematics
• Mathematical Physics
• Medical Imaging
• +۶ مورد دیگر

Habib Ammari is a Professor of Applied Mathematics at ETH Zurich, leading research in wave propagation, mathematical imaging, and metamaterials. He previously served as Director of Research at École Normale Supérieure in Paris. His academic journey includes a Bachelor's, Master's, and PhD from École Polytechnique (1992–1995), followed by a Habilitation from Université Pierre & Marie Curie (1998). Research Interests: Mathematics of subwavelength physics and metamaterials Bio-inspired imaging and neural natural intelligence Multiscale biomedical imaging and super-resolution techniques Publications: His work spans over 300 papers and 9 books, including seminal contributions to inverse problems, plasmonics, and imaging modalities. Recent research focuses on non-Hermitian skin effects, quantum integrable systems, and Anderson localization in subwavelength regimes. Awards and Fellowships: European Research Council Advanced Grant (2010) Kuwait Prize in Basic Sciences (2013) Khwarizmi International Award (2015) Fellowships: American Mathematical Society (2022), Academia Europaea (2021), European Academy of Sciences (2018) Advising: Supervised 40+ PhD students and 27 postdocs, many recipients of prestigious awards like the ETH Medal and ECCOMAS PhD Award. Labs and Collaborations: Active in international initiatives including CISM courses, NSF-CBMS conferences, and collaborations with institutions like Hong Kong Institute for Advanced Study and MSRI.

Matthias Katzfussمشاهده پروفایل

استاد

• Computational Spatial Statistics
• Spatio-Temporal Statistics
• Gaussian Processes
• +۵ مورد دیگر

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

Vivak Patelمشاهده پروفایل

استادیار

• Numerical Optimization
• Statistical Estimation
• Dynamical Systems
• +۳ مورد دیگر

Vivak Patel is an Assistant Professor in the Department of Statistics at the University of Wisconsin–Madison, part of the School of Computer, Data & Information Sciences. His research focuses on the intersection of numerical optimization and statistical estimation, with applications to dynamical systems, machine learning, and power systems load modeling. He develops scalable algorithms that integrate uncertainty considerations to address computational challenges in data science and energy systems. Research interests include foundational mathematics, statistical methodologies, and algorithm development for problems involving uncertainty and computing. His work emphasizes mathematical guarantees under realistic conditions, particularly in optimization, machine learning, and numerical linear algebra. Collaborative efforts extend to energy and climate modeling, leveraging computational efficiency and robustness. Key research trends from his publications include advancements in stochastic gradient methods, randomized iterative solvers, and convergence analyses for optimization algorithms in complex systems. These contributions address scalability, computational efficiency, and theoretical robustness in data-driven applications. He advises prospective students through dedicated mentoring programs and maintains an active research group focused on the Uncertainty & Computing initiative. Contact details include vivak.patel@wisc.edu and the Medical Sciences Center address. No scientific awards are explicitly listed in available materials.

Eytan Barouchمشاهده پروفایل

استاد

• Electromagnetic Applications
• Numerical Analysis
• Algorithm Development
• +۵ مورد دیگر

Eytan Barouch is a Professor in the Department of Mechanical Engineering at Boston University. His primary research focuses on electromagnetic applications, numerical analysis, and algorithm development for industrial processes. He specializes in EUV lithography, inverse scattering techniques, and material property identification. His work often addresses challenges in microchip fabrication, pellicle deformation effects, and high-NA optical systems. Education: PhD from Stony Brook University. His research integrates advanced computational methods with practical engineering problems, such as optimizing EUV sources and mitigating defects in lithographic systems. Notable contributions include developing fast simulators for large-area EUV lithography and analyzing waveguide effects in high-NA systems to extend 4-nm node capabilities. His publications span over three decades, emphasizing rigorous electromagnetic simulations, pellicle impact on critical dimension uniformity, and non-linear material responses. While no specific awards are listed, his extensive body of work reflects sustained contributions to computational electromagnetics and semiconductor manufacturing. Prof. Barouch’s advising and grants focus on advancing lithography technologies and material characterization, though specific grant details are not provided. His research group likely collaborates with semiconductor industry partners, given the applied nature of his work.

Luca Maestriniمشاهده پروفایل

مدرس

• Methodological and Computational Statistics
• Statistical Theory
• Variational Approximations
• +۴ مورد دیگر

Dr. Luca Maestrini is a Lecturer of Statistics at the Research School of Finance, Actuarial Studies & Statistics at the Australian National University (ANU), Canberra. His research focuses on methodological and computational statistics, with expertise in variational approximations, generalized linear mixed models (GLMMs), and statistical theory. He previously held postdoctoral positions at ANU (2022–2024) under Prof. Francis Hui and Prof. Alan Welsh, and at the University of Technology Sydney (2018–2022) under Prof. Matt Wand. He completed his PhD in Statistics at the University of Padova, Italy (2015–2019), studying under Prof. Nicola Sartori and co-supervisors Prof. Alessandra Salvan and Prof. Matt Wand. He also served as a Research Fellow at the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research interests span computational statistics, Bayesian inference, and applications in forensic science, ecology, and finance. Notable collaborations include work on inverse problems, multivariate abundance data analysis, and stochastic variational inference for GARCH models. He currently chairs the Australian Capital Territory branch of the Statistical Society of Australia. Key research trends in his articles include advancements in variational inference frameworks, asymptotic improvements to GLMMs, and statistical methods for postmortem interval estimation using lipid degradation biomarkers. His work bridges theoretical developments with practical applications in diverse fields. He has advised students through ANU’s research programs and contributed to ACEMS initiatives. His lab focuses on computational methods and applied statistical modeling, emphasizing interdisciplinary collaboration.

Eric Darveمشاهده پروفایل

استاد

• Machine Learning
• Numerical Linear Algebra
• High-Performance Computing
• +۵ مورد دیگر

Eric Darve is a Professor of Mechanical Engineering and Director of the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning for science/engineering, numerical linear algebra, high-performance computing, and GPU-accelerated algorithms. He holds a PhD in Applied Mathematics from Pierre et Marie Curie University (2009), and completed postdoctoral work at Stanford/NASA Ames before joining Stanford’s faculty in 2001. Education: PhD, Applied Mathematics, Paris VI University (1999) MS, Applied Mathematics, Paris IX University (1994) BS, Mathematics & Physics, Paris VI University (1993) Research interests include: Machine learning for scientific applications Surrogate/Reduced Order Modeling Anomaly detection in engineering systems Parallel computing and numerical algorithms Physics-informed learning machines Awards include: H. Julian Allen Award (NASA, 2010) Habilitation à Diriger des Recherches (France, 2007) Leslie Fox Prize in Numerical Analysis (2001) James H. Clark Faculty Scholar (2001) Led development of scalable linear solvers and hybrid physics-data approaches. Active in computational engineering education and interdisciplinary research initiatives through ICME.

Lexing Yingمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Science
• Quantum Computing
• +۷ مورد دیگر

Lexing Ying is a Professor of Mathematics at Stanford University. His research focuses on applied mathematics, computational science, and interdisciplinary areas such as quantum computing, machine learning, and numerical analysis. He has made significant contributions to high-dimensional problems, inverse problems, and the development of efficient algorithms for complex systems. His work spans theoretical advancements and practical applications, including quantum signal processing, tensor-based numerical methods, and probabilistic modeling. Key themes in his research include the analysis of diffusion models, stochastic systems, and optimization algorithms. Recent publications highlight innovations in solving high-dimensional PDEs, improving sampling techniques, and advancing quantum algorithms. Notably, Ying has developed methods for operator learning, functional hierarchical tensors, and robust phase estimation. His research often bridges mathematical theory and computational practice, addressing challenges in areas like statistical inference and numerical simulation.