جست‌وجوی استاد و پژوهشگر

• Cryptography
• Security
• Algorithms
• +۷ مورد دیگر

Paul Stankovski Wagner is an Associate Professor and Senior Lecturer at the Department of Electrical and Information Technology, Faculty of Engineering (LTH), Lund University. He serves as Project Manager for ELLIIT (Linköping-Lund initiative on IT and mobile communication) and contributes to the LTH Profile Area: AI and Digitalization as well as the Secure and Networked Systems initiative. Roles: Senior Lecturer, Associate Professor, Project Manager, Researcher, Assistant Supervisor Institutions: Lund University, Linköping University, Pufendorf IAS His research focuses on cryptography, security, and quantum-resistant algorithms. Key areas include Learning with Errors (LWE), post-quantum cryptographic implementations, and blockchain applications in cybersecurity and pharmaceutical R&D. He explores side-channel vulnerabilities, key management systems, and privacy-preserving protocols like PAPR for anonymous credentials. Recent publications (2021-2025) highlight his work on BKW-style LWE solvers, healthcare IT security architectures, and blockchain-based data-sharing frameworks. Articles span journals like ACM Transactions on Computing for Healthcare and International Review of Law, Computers & Technology , with keywords reflecting cryptography, algorithms, and data privacy. He supervises doctoral research (e.g., Amanda Nilsson’s work on post-quantum cryptographic side channels) and collaborates on multidisciplinary projects such as SMARTY (secure software updates for smart cities) and LUTAX (tax research with technological applications).

• Programming Languages
• Formal Verification
• Compilers
• +۶ مورد دیگر

Clément Pit-Claudel is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the SYSTEMF lab. Previously, he was a PhD candidate at MIT under Adam Chlipala and a Senior Applied Scientist at Amazon AWS. His research focuses on programming languages, compilers, and formal verification, with broader interests in systems engineering, hardware design languages, security, performance engineering, and type theory. Key research areas include extensible compilation, hardware design languages verification, and tooling for proof assistants. Recent publications span topics like JavaScript regular expression verification ( ICFP 2024 ), relational compilation ( PLDI 2024 ), and foundational integration verification for cryptographic servers ( PLDI 2024 ). Scientific Awards: Distinguished Artifact, ACM SIGPLAN International Conference on Software Language Engineering (2020) William A. Martin Memorial Thesis Award for Outstanding Thesis in Computer Science, MIT (2016) Frederick C. Hennie III Teaching Award, MIT (2016) As an educator, he teaches Software Construction (undergraduate) and Interactive Theorem Proving (graduate) at EPFL. His teaching philosophy emphasizes hands-on learning, oral examinations, and project-based assignments that lead to tangible artifacts. The SYSTEMF lab focuses on building small, fast, and completely verified systems components through machine-checked proofs, hardware-software co-design, low-level compiler engineering, and theorem prover tooling.

Joar Baggeمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Machine Learning
• High-Performance Computing
• +۳ مورد دیگر

Joar Bagge is a Postdoctoral Fellow at the Center for Numerical Analysis within the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin, where he joined in September 2023 to collaborate with Prof. George Biros and Prof. Gunnar Martinsson on computational engineering projects. He earned his PhD in Applied and Computational Mathematics from KTH Royal Institute of Technology, Stockholm, Sweden, in 2023 under the supervision of Prof. Anna-Karin Tornberg, focusing on special quadrature methods and fast summation techniques for boundary integral equations and Stokes flow. Bagge's research integrates numerical analysis, machine learning, and high-performance computing to develop efficient algorithms for microscale fluid simulations and elliptic PDEs. His work emphasizes special quadrature methods, fast direct solvers, and boundary integral equation formulations, with practical applications in scientific computing. His scientific recognition includes: Peter O'Donnell, Jr. Postdoctoral Fellowship in Computational Engineering and Sciences Bagge actively contributes to the Parallel Algorithms for Data Analysis and Simulation Group at the Center for Numerical Analysis, developing open-source software like the Spectral Ewald Unified package (SE_unified_v2) for fast summation methods in particle simulations.

George Birosمشاهده پروفایل

استاد

• Computational Engineering
• Parallel Algorithms
• Numerical Methods
• +۷ مورد دیگر

George Biros is a Professor of Mechanical Engineering and holds the W.A. 'Tex' Moncrief Jr. Endowed Chair in Simulation-Based Engineering Sciences at the University of Texas at Austin. He leads the Parallel Algorithms for Data Analysis and Simulation Group within the Institute for Computational Engineering and Sciences (ICES). His research focuses on computational mathematics, parallel algorithms for physics-based simulations, and large-scale data analysis, with applications in biomedical imaging, tumor growth modeling, and high-performance computing. Biros earned his Ph.D. in Computational Science and Engineering from Carnegie Mellon University (CMU), followed by a postdoctoral fellowship at NYU's Courant Institute. He previously held faculty positions at the University of Pennsylvania and Georgia Tech. His work emphasizes scalable algorithms for exascale computing, including fast multipole methods and hierarchical matrix techniques. Research interests span parallel algorithms, numerical methods for PDEs, inverse problems, and biophysical modeling. Notable contributions include tumor growth models with mass effect, image registration algorithms (e.g., CLAIRE), and GPU-accelerated solvers for fluid dynamics and plasma simulations. His group develops open-source libraries like PyKokkos for performance-portable Python kernels. Recent publications highlight advancements in grain microstructure prediction via graph neural networks, single-scan MRI tumor calibration, and tau protein dynamics modeling in Alzheimer's disease. He has pioneered GPU-based diffeomorphic image registration for large biomedical datasets, achieving real-time performance on multi-GPU systems. Awards: Two-time ACM Gordon Bell Prize (2010, 2016), DOE Early Career Award Grants: Leads projects in exascale computing, numerical methods, and medical image analysis Labs: ICES Parallel Algorithms Group, ExaNIML (Exascale Numerically Inspired Machine Learning)

Wilbert IJzermanمشاهده پروفایل

استاد

• Illumination Optics
• Inverse Optics
• Optical Simulation
• +۳ مورد دیگر

Wilbert IJzerman is a Full Professor (Part-time) of Illumination Optics at Eindhoven University of Technology (TU/e), leading the Scientific Computing and Applications (CASA) group with Dr. Jan ten Thije Boonkkamp. He holds roles as Chair of Illumination Optics, Head of Lighting Technology at Signify Research, and Member of the European Optical Society's Industrial Advisory Committee. Education: MSc in Applied Physics (1995) and MSc in Applied Mathematics (1996) from University of Twente, PhD in Applied Mathematics (2000), and Executive MBA from Rotterdam School of Management (2011). Research focuses on inverse optical problems, numerical simulation of illumination systems, and freeform optical design. Innovations include a versatile mass transport algorithm and ultra-fast ray tracing methods. He co-leads projects like the TTW Perspective Program 'Free-Shape Scattering Optics' and holds over 50 US patents in lighting technology. His recent work emphasizes neural network solutions for optical equations and Lie algebraic analysis of gradient-index media. Advising PhD students on optical component characterization and scattering materials design, he bridges academic research with industrial applications through Holland High Tech and Optics Netherlands collaborations.

Daniel Sjöbergمشاهده پروفایل

استاد

• Electromagnetics
• Nanoelectronics
• Antenna Engineering
• +۵ مورد دیگر

Daniel Sjöberg is a Professor at the Department of Electrical and Information Technology at Lund University, specializing in Electromagnetics and Nanoelectronics . He also serves as the Director of Third Cycle Studies and contributes to the LTH Profile Area: AI and Digitalization and the ELLIIT initiative . Research Focus: Electromagnetic field interactions with materials, antennas, microwave applications, homogenization, periodic structures, and inverse scattering problems. Collaborations: Active partnerships with industry (aerospace sector) and institutions like Linköping University through ELLIIT. Projects: Leads initiatives such as EKAS-3D (3D-printed electromagnetic characterization) and FE2MS (fast electromagnetic solvers). His work aligns with UN Sustainable Development Goals (SDGs) in technology and communications. Recently, he has published on topics including 2D periodic dielectrics, water-based reconfigurable devices, and nonreciprocal systems, with technical reports and peer-reviewed articles.

• Numerical Analysis
• Partial Differential Equations
• Spectral Methods
• +۴ مورد دیگر

Einar Malvin Rønquist is a Professor and has served as Head of the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) since August 2013. He has been a faculty member at the same department since 1999 and is affiliated with the differential equations and numerical analysis (DNA) research group, which he led from 2010-2011. His extensive publication record demonstrates a sustained research career spanning over three decades in computational mathematics. Professor Rønquist's research focuses primarily on numerical methods for partial differential equations, with special emphasis on spectral methods, reduced basis methods, and computational fluid dynamics. His work addresses challenges in high-order approximation, model reduction, and efficient computation for complex physical systems. He has made significant contributions to the development of tensor-product solvers for deformed domains, interface tracking algorithms, and the theoretical foundations of reduced basis methods. His research bridges theoretical mathematics with practical computational applications across engineering and physical sciences, with particular relevance to fluid dynamics, geometric problems, and multi-physics systems. Analysis of Rønquist's publication trajectory reveals a consistent evolution from foundational spectral element methods to advanced model reduction techniques. His work shows increasing sophistication in handling parametric problems and uncertainty quantification while maintaining rigorous mathematical foundations. The publications demonstrate strong contributions to high-order numerical methods, with applications spanning fluid dynamics, geometric problems, and multi-physics systems. Recent work shows particular emphasis on practical computational implementation, including parallel algorithms and efficient solvers for real-world applications that require solving problems on complex or deformed domains. Rønquist has maintained active international collaborations throughout his career, particularly with researchers at MIT (notably Anthony Patera), Université Pierre et Marie Curie (Yvon Maday), and other leading institutions across Europe and North America. His work has appeared consistently in top journals in computational mathematics and scientific computing, including Journal of Computational Physics, SIAM Journal on Scientific Computing, and Computer Methods in Applied Mechanics and Engineering. As department head since 2013, Rønquist has played a significant administrative role in shaping mathematics education and research at NTNU. His leadership extends beyond pure administration, as evidenced by his 2016 commentary in Universitetsavisa regarding the organization of technical cybernetics and electrical power engineering at NTNU, and his 2021 opinion piece in Aftenposten about mathematics examinations. He continues to actively contribute to both the academic and public discourse on mathematics education and research.

• Electromagnetics
• Bio-electromagnetics
• Computational Analysis
• +۴ مورد دیگر

Luis Gomez is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on computational electromagnetics, electromagnetic brain stimulation, and uncertainty quantification. He specializes in developing advanced algorithms for transcranial magnetic stimulation (TMS) optimization, including real-time electric field modeling, coil design, and neuronavigation systems. His work integrates fast multipole methods (FMM), boundary element methods (BEM), and deep learning for biomedical applications. Key areas of expertise include: Electromagnetic dosimetry and safety in TMS Pseudo-realistic cell and neuron modeling Optimization of TMS coil placement and focality Uncertainty quantification in biomedical simulations His recent publications emphasize real-time computation of electric fields for neuronavigation, fast solvers for population-level TMS targeting, and integration of machine learning with bioelectromagnetic analysis. Gomez collaborates on projects involving nonhuman primates and mouse models to advance neurostimulation technologies. Notable research contributions include novel volume integral equation approaches for low-frequency dosimetry and the design of high-focality TMS coils. His work bridges computational methods with clinical applications in neurology and medical engineering.

Indre Jödickeمشاهده پروفایل

پژوهشگر

• Mechanical Engineering
• Materials Science
• Computational Mechanics
• +۳ مورد دیگر

Indre Jödicke serves as an Associated Doctoral Researcher in the Department of Microsystems Engineering at the Faculty of Engineering, University of Freiburg. Her work focuses on topology optimization of nonlinear mechanical metamaterials using Fast-Fourier-Transformation-based solvers to engineer materials with tailored nonlinear responses for long-lived, trainable systems. Her research spans Mechanical Engineering, Materials Science, and Computational Mechanics, with specific expertise in topology optimization, metamaterials, and nonlinear mechanical behavior. She investigates how material anisotropy and computational homogenization techniques can overcome manufacturing defects and numerical instabilities in architected materials. Analysis of her publications reveals a strong emphasis on computational material science. Her 2024 work addresses defect mitigation in architected materials through anisotropy, while her 2022 contribution solves ringing artifacts in FFT-based homogenization via finite-element projection. These studies advance structural optimization and computational homogenization methodologies. No scientific awards or fellowships are documented. She is supervised by Prof. Dr. Lars Pastewka within the livMatS cluster of excellence and has no listed advisees or additional grant information beyond her doctoral project.

Haizhao Yangمشاهده پروفایل

دانشیار

• AI for Science
• Machine Learning
• High-Performance Computing
• +۴ مورد دیگر

Haizhao Yang is an Associate Professor of Mathematics and Computer Science at the University of Maryland College Park (UMCP). He holds affiliate appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Applied Mathematics and Scientific Computation (AMSC) program. Previously, he served as an Assistant Professor at Purdue University and the National University of Singapore, and as a Visiting Assistant Professor at Duke University (2015–2017). His education includes a B.Sc. from Shanghai Jiao Tong University (2010), M.Sc. from The University of Texas at Austin (2012), and Ph.D. from Stanford University (2015). Yang's research focuses on machine learning theory, scientific computing, and applied mathematics. Key areas include AI for scientific discovery, high-performance computing, and algorithm development for differential equations. His work bridges mathematical rigor with practical applications, such as developing neural network-based solvers for high-dimensional PDEs and quantum computing integration for uncertainty quantification. He leads a dynamic research group with over 30 students and postdocs across PhD, master's, and undergraduate levels. Notable achievements include the NSF CAREER Award (2020), ONR Young Investigator Award (2022), and DARPA Young Faculty Award (2024). His laboratory collaborates with institutions like Lawrence Berkeley National Lab and Duke University, emphasizing interdisciplinary projects in data science and computational physics. Recent publications highlight innovations in operator learning for PDEs, quantum algorithm integration, and adversarial reinforcement learning. His group actively explores AI-driven approaches to overcome computational bottlenecks in scientific simulations, with techniques like the Finite Expression Method and OptimAI framework for automated problem-solving.

Jiahui Chenمشاهده پروفایل

استادیار

• Topological Data Analysis
• Machine Learning
• Computational Biophysics
• +۴ مورد دیگر

Jiahui Chen is an Assistant Professor in the Department of Mathematical Sciences at the University of Arkansas, Fayetteville. He holds a PhD in Computational and Applied Mathematics from Southern Methodist University (2019). Prior to his current position, he was a Visiting Assistant Professor at Michigan State University under Prof. Guo-Wei Wei's group. His research focuses on topological and geometrical data analysis, machine learning algorithms, and their applications in biomolecular modeling. Key areas include protein-protein interaction analysis, mutation impact prediction, and fast numerical methods like the Poisson-Boltzmann equation, Treecode, and Fast Multipole Method. He has contributed to studies on SARS-CoV-2 variants, vaccine-breakthrough mutations, and antibody resistance using topological methods and deep learning. His work combines numerical algorithms with parallel computing, emphasizing GPU acceleration and algorithm optimization. Chen has collaborated on projects involving persistent homology, Hodge Laplacians, and algebraic topology for biomolecular analysis. He maintains an active GitHub profile with repositories like persistent_homology_visualizer and TABI_solver , showcasing his computational tools.

Carlos Borgesمشاهده پروفایل

دانشیار

• Inverse Scattering Problems
• Numerical Analysis
• Domain Decomposition
• +۴ مورد دیگر

Carlos Borges is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. He holds a Ph.D. in Mathematics from Worcester Polytechnic Institute (2013) and has held postdoctoral positions at the Oden Institute (University of Texas-Austin) and the Courant Institute (NYU). His research focuses on inverse scattering problems, numerical analysis, and neural networks, with applications in computational engineering and mathematical physics. Education: PhD in Mathematics (Worcester Polytechnic Institute, 2013), MS in Mathematics (Instituto de Matematica Pura e Aplicada, 2007), BEng in Computer Engineering (Instituto Militar de Engenharia, 2002). Research interests include inverse scattering (acoustic, electromagnetic), domain decomposition methods, fast solvers, and machine learning integration in numerical analysis. His work emphasizes high-resolution parameter reconstruction and multi-frequency data utilization for obstacle identification. He advises four students: PhD candidates Isabela Vasconcellos Viana and Nick Arustamyan, and undergraduates Tina Tran and Eli Rivera-Sanchez. His recent publications (2023–2025) highlight advances in neural network-driven inverse scattering and robust algorithms for irregular geometries. He organizes the Scientific Computing Seminar at UCF, focusing on numerical analysis and computational methods. His software contributions include solvers for inverse scattering problems, available on GitHub. He is active in professional societies like SIAM and IEEE, and regularly presents at conferences such as SIAM CSE 2025.

Muhammad Mohebujjamanمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Stochastic Partial Differential Equations
• Reduced Order Modeling
• +۳ مورد دیگر

Muhammad Mohebujjaman is an Assistant Professor at the University of Alabama at Birmingham (UAB) . His research focuses on Uncertainty Quantification of stochastic PDEs (including Navier-Stokes and Magnetohydrodynamics equations), Reduced Order Modeling , Population Dynamics , and Applied Analysis , with applications to multiphysics problems and fast algorithms. He teaches courses in Numerical Analysis , Scientific Computing , and Partial Differential Equations . Education : B.S. in Mathematics, University of Dhaka M.S. in Applied Mathematics, University of Dhaka M.S. and Ph.D. in Applied and Computational Mathematics, Clemson University He previously held postdoctoral positions at Virginia Tech (joint appointment in Mathematics and Biomedical Engineering & Mechanics) and MIT's Plasma Science and Fusion Center, where he contributed to data-driven reduced order modeling and Krylov solvers for fusion reactor simulations. His office hours are Monday/Wednesday 11:10 am–12:10 pm or by appointment.

Daochen Wangمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Algorithms
• Quantum Cryptography
• +۳ مورد دیگر

Daochen Wang is an Assistant Professor of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on quantum computation, quantum algorithms, and quantum cryptography, with an emphasis on understanding quantum speed-ups and their applications. Education: Ph.D. in Computer Science from the University of Maryland (advisors: Andrew Childs and Carl Miller), BA and MMath in Mathematics from the University of Cambridge. Research interests include quantum algorithm design, post-quantum cryptography, and the theoretical foundations of quantum computing. His work spans quantum graph algorithms, lattice-based security, and variational quantum eigensolvers. Teaching includes undergraduate (CPSC 436Q) and graduate (CPSC 536W) courses on quantum computation. He advises PhD student Xingyu Zhou and undergrad Rain Zimin Yang. Key research contributions include quantum algorithms for reinforcement learning, cryptographic security analysis of post-quantum schemes (e.g., CRYSTALS-Dilithium), and foundational studies on quantum speedups via symmetry analysis.

Yongtao Zhangمشاهده پروفایل

استاد

• Computational Physics
• Mathematical and Computational Biology
• Numerical Solution of Differential Equations
• +۳ مورد دیگر

Yongtao Zhang is a Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a PhD from Brown University (2003) and an M.S. from Nankai University (1999). His research focuses on developing high-order numerical methods for partial differential equations (PDEs), including weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) methods, and implicit integration factor (IIF) methods. These methods are applied to computational physics (e.g., shock wave dynamics, fluid flows) and computational biology (e.g., morphogenesis, tissue patterning). Dr. Zhang's work emphasizes numerical solutions for hyperbolic conservation laws, stiff advection-reaction-diffusion equations, and Hamilton-Jacobi equations. He has contributed to sparse-grid implementations, fast sweeping methods, and exponential time differencing schemes to enhance computational efficiency and accuracy. His research bridges theoretical advancements with practical applications in high-dimensional systems and complex geometries. He is an editorial board member for Journal of Scientific Computing and Communications on Applied Mathematics and Computation , reflecting his leadership in the field. Teaching responsibilities include numerical analysis, scientific computing, and applied mathematics courses. His work has been published in leading journals such as Journal of Computational Physics and SIAM Journal on Scientific Computing .