جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Statistical Methods
• +۴ مورد دیگر

Tong Zhang is a Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC), part of the Grainger College of Engineering. He previously held positions at The Hong Kong University of Science and Technology, Rutgers University, and industry roles at IBM, Yahoo, Google, Baidu, and Tencent. His research focuses on machine learning algorithms, statistical methods for big data, and their applications in areas like reinforcement learning, generative AI, and optimization. Education: PhD in Computer Science (Stanford University, 1999), MS in Computer Science (Stanford University, 1996), BA in Mathematics and Computer Science (Cornell University, 1994). Research Interests: Machine learning theory, large language models, reinforcement learning, adversarial attacks, optimization algorithms, and ethical AI. His work emphasizes robustness, generalization, and scalable methods for complex systems. Awards: ASA Fellow, IEEE Fellow, IMS Fellow, and recipient of multiple top-tier conference awards (e.g., NAACL 2024 Outstanding Papers). Labs/Teams: Leads a research group at UIUC with active collaborations in areas like generative AI and embodied agents. Supervised over 20 PhD students and postdocs, many now in academia and industry leadership roles.

Matthias Heinkenschlossمشاهده پروفایل

استاد

• Nonlinear Optimization
• PDE-Constrained Optimization
• Optimal Control
• +۳ مورد دیگر

Matthias Heinkenschloss is the Noah G. Harding Chair and Professor of Computational Applied Mathematics and Operations Research at Rice University. He joined Rice in 1996 after serving as an assistant professor at Virginia Tech and earlier at the University of Trier in Germany. His research focuses on large-scale nonlinear optimization, PDE-constrained optimization, and optimal control, with applications in engineering and scientific domains such as flow control, reservoir management, and structural acoustics. He holds a Ph.D. (Dr. rer.nat.) and M.S. in Applied Mathematics from the University of Trier, Germany. His academic leadership includes six years as department chair at Rice and advisory roles on the Scientific Advisory Board of the Weierstrass Institute for Applied Analysis and Stochastics, among others. Research interests span optimization under uncertainty, model reduction, iterative solvers for KKT systems, and domain decomposition in optimization. His work emphasizes integrating optimization algorithms with underlying differential equations to enhance computational efficiency and solution fidelity. Notable contributions include methods for PDE-constrained optimization, reduced-order modeling, and parallel-in-time algorithms for optimal control. Key publications include advancements in nonlinear manifold reduced order models, domain decomposition techniques, and parallel-in-time solutions for linear-quadratic optimal control problems. His work has been recognized with awards such as the Mercator Fellowship (2016). He actively contributes to academic societies like the Society for Industrial and Applied Mathematics (SIAM) and teaches courses in numerical optimization and PDE-constrained optimization.

Lek-Heng Limمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Matrix Computation
• Computational Mathematics
• +۳ مورد دیگر

Lek-Heng Lim is a Professor in the Department of Statistics at the University of Chicago, with additional appointments in the Computational and Applied Mathematics Initiative (CCAM) and the College. He serves on the Executive Committee of CCAM and maintains an active research program in computational mathematics. His research focuses on Numerical Linear Algebra , Matrix Computation , and Optimization , with applications spanning scientific computing and applied mathematics. Professor Lim has developed expertise in matrix decompositions, iterative methods, eigenvalue problems, and sparse linear algebra, contributing significantly to both theoretical foundations and practical implementations in computational mathematics. He has maintained an extensive teaching record at the University of Chicago, offering advanced courses including Matrix Computation, Modern Applied Optimization, Matrix Calculus and Analysis, and Convex Optimization. His teaching spans multiple departments including Statistics, Computational and Applied Mathematics, Computer Science, and Financial Mathematics. Previously, he has taught at Stanford University, University of California Berkeley, University of Cambridge, and Cornell University across mathematics and computer science departments. Professor Lim and his student Zehua Lai were featured in Quanta Magazine in March 2023 for their work in computational and applied mathematics. His educational materials and lecture notes demonstrate a commitment to pedagogical excellence and making complex computational concepts accessible to students.

• Numerical Methods
• Partial Differential Equations
• Computational Fluid Dynamics
• +۵ مورد دیگر

Keaton Burns is a Principal Research Scientist in the Department of Mathematics at the Massachusetts Institute of Technology (MIT), affiliated with the Computational Science and Engineering (CSE) program. His research focuses on numerical methods, partial differential equations, computational fluid dynamics, spectral methods, and their applications in geophysical and astrophysical systems. He leads the development of the open-source Dedalus project, a flexible framework for solving partial differential equations using spectral methods. His work addresses challenges in fluid dynamics, turbulence, and magnetohydrodynamics, with applications ranging from ocean modeling to stellar convection and planetary magnetospheres. Research interests include advanced numerical techniques for simulating complex flows, parameterization of microscale ocean mixing (e.g., CATKE model), and exploring nonlinear instabilities and multiscale phenomena in rotating systems. His contributions to software development, such as Oceananigans and Dedalus, emphasize high-resolution, scalable simulations across diverse scales. Publications highlight advancements in automated adjoints for geophysical flows, stability analysis of viscoelastic fluids, and the role of convection in solar dynamics. His work bridges computational innovation with fundamental scientific questions in astrophysics and geophysics, often leveraging spectral methods for high-precision solutions. No scientific awards or advising roles are explicitly mentioned in the provided materials. The Dedalus project underscores his commitment to open-source tools for advancing computational research in academia and industry.

Robert M. Freundمشاهده پروفایل

استاد

• Nonlinear optimization theory
• Computational complexity
• Interior-point methods
• +۵ مورد دیگر

Robert M. Freund is the Theresa Seley Professor in Management Science at the MIT Sloan School of Management, specializing in Operations Research. His research focuses on nonlinear optimization, computational complexity, and first-order methods with applications in management and engineering. He holds a PhD in Operations Research from Stanford University and has authored influential textbooks like Data, Models, and Decisions: The Fundamentals of Management Science . Education: B.A. in Mathematics (Princeton, 1975), M.S. and Ph.D. in Operations Research (Stanford, 1979–1980). Roles: Faculty Director of MIT Sloan MBA Program, Deputy Dean for Faculty (2008–2011), Co-Director of MIT Program in Computation for Design and Optimization (2004–2008). His research interests span optimization theory, computational science, and applications in machine learning. Notable contributions include advancements in Frank-Wolfe algorithms, interior-point methods, and photonic crystal design. He has received the Longuet-Higgins Prize (2007) and multiple teaching awards. Recent work includes the development of restarted PDHG algorithms, Taylor-approximated gradients for empirical risk minimization, and analysis of first-order methods' computational guarantees. His collaborative efforts with students and researchers like Zikai Xiong and Haihao Lu have produced impactful publications in Mathematical Programming and SIAM Journal on Optimization . Freund has also contributed to educational initiatives, including MIT's Professional Certificate in Data Science and Analytics. His work bridges theoretical optimization advancements with practical applications in engineering and data-driven decision-making.

Klaus-Jürgen Batheمشاهده پروفایل

استاد

• Computational Mechanics
• Finite Element Analysis
• Fluid-Structure Interaction
• +۵ مورد دیگر

Professor Klaus-Jürgen Bathe is a distinguished faculty member at the Massachusetts Institute of Technology in the Department of Mechanical Engineering . As a pioneer in computational mechanics , his work has shaped the development of the finite element method through innovations like the MITC shell elements , Bathe time integration , and subspace iteration method . He founded ADINA R&D, Inc. (acquired by Bentley Systems in 2022) and established the Klaus-Jürgen Bathe Leadership Program at the University of Cape Town to cultivate leadership in South African graduates.

Sarang Kulkarniمشاهده پروفایل

پژوهشگر ارشد

• Logistics and Supply Chain Management
• Operations Research
• Optimization
• +۳ مورد دیگر

Sarang Kulkarni is a Senior Research Fellow at Curtin University's School of Elec Eng, Comp and Math Sci (EECMS), within the Faculty of Science and Engineering. He holds a PhD and works at Curtin's Perth campus in the Centre for Optimisation and Decision Science. His research focuses on real-life large-scale optimisation problems, including scheduling, mathematical programming, and mixed-integer programming. Education: PhD, MEng (Industrial Engineering and Management), BEng (Production) Research Interests: Logistics and Supply Chain Management, Operations Research, Optimisation techniques, and heuristic development Specialty: Decompositions and heuristics for complex optimisation challenges in transportation, logistics, and supply chain management His publications span vehicle scheduling, blast design optimisation, and hybrid neural network approaches. Notable works include benchmark datasets for multi-depot vehicle scheduling (2019) and autonomous drill-based blast optimisation (2024). He collaborates with industry partners in India and across Curtin's global campuses (Australia, Dubai, Malaysia). Labs/Teams: Active member of the Centre for Optimisation and Decision Science, contributing to both academic and industrial applications of optimisation technologies.

Alexander Mundeyمشاهده پروفایل

پژوهشگر ارشد

• Operator Algebras
• Dynamical Systems
• Noncommutative Geometry
• +۳ مورد دیگر

Alexander Mundey is an Associate Research Fellow at the School of Mathematics and Applied Statistics, University of Wollongong. His roles include organizing the Wollongong OANCG Seminar and contributing to research on operator algebras and dynamical systems. He previously held positions as Associate Lecturer and held a PhD in Mathematics (2020) from the University of Wollongong, complemented by degrees in Mathematics and Physics (2015). His research focuses on C*-algebraic approaches to self-similar dynamical systems, including iterated function systems, graph dynamics, and group actions. He has secured grants such as the AEGis CONNECT (2023-2024) and RevITAlising (2021-2022). Mundey teaches advanced courses like Complex Analysis and Linear Algebra and has supervised multiple graduate students, including Angus Alexander and Thomas Pedersen. His work integrates topological dynamics, noncommutative geometry, and algebraic structures.

James Brannickمشاهده پروفایل

استاد

• Lattice Field Theory
• Numerical Solutions to PDEs
• Multigrid Methods
• +۳ مورد دیگر

James Brannick is a Professor in the Department of Mathematics at Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on numerical solutions to partial differential equations (PDEs), multigrid methods, and lattice field theory, with applications in computational physics and quantum chromodynamics (QCD). He holds a Ph.D. from the University of Colorado Boulder (2005). Research interests include developing advanced multigrid algorithms for complex systems, such as coupled PDEs and spacetime discretizations, with a particular emphasis on algebraic multigrid (AMG) methods. His work bridges theoretical foundations (e.g., convergence analysis) and practical implementations (e.g., parallel solvers for large-scale problems). Recent publications emphasize AMG optimization, bootstrap techniques, and applications to fluid dynamics, quantum field theory, and materials science. Collaborative projects include NSF-funded initiatives for parallel space-time solvers and multigrid methods in lattice QCD. Brannick’s contributions address challenges like critical slowing down in lattice simulations and efficient handling of anisotropic or indefinite problems. Grants include subcontract B629176 (2019) and prior NSF awards for multigrid solvers in coupled PDE systems. His research often intersects with high-performance computing and interdisciplinary applications, such as neutron star dynamics and phase-field modeling.

Alexei Novikovمشاهده پروفایل

استاد

• Applied Analysis
• Probability
• Stochastic Processes
• +۴ مورد دیگر

Alexei Novikov is a Professor in the Department of Mathematics at Pennsylvania State University, affiliated with the Eberly College of Science. He holds a Ph.D. from Stanford University (1999). His research focuses on applied analysis, probability, stochastic processes, and their applications in fluid dynamics, imaging science, and materials modeling. Key areas include wave propagation in complex media, sparse signal recovery, and homogenization techniques for heterogeneous systems. His teaching includes advanced courses such as Functional Analysis (Math 503), Stochastic Calculus (Math 519), and Differential Equations (Math 251). He has led NSF-funded research on transport phenomena in heterogeneous media and contributed to algorithm development for imaging in complex environments. Notable works include studies on eddy viscosity in cellular flows, network approximation methods, and fractional kinetic processes. Novikov’s recent work emphasizes interdisciplinary applications, including neural network approaches for super-resolution imaging and stochastic models for knowledge diffusion. His publications span journals like Communications on Pure and Applied Mathematics , SIAM Journal on Mathematical Analysis , and Inverse Problems .

• Coding Theory
• Post-Quantum Cryptography
• Quantum Error Correction
• +۲ مورد دیگر

Felice Manganiello is an Associate Professor and Associate Director for Mathematics and Statistics Education in Clemson University's College of Science, Department of Mathematical and Statistical Sciences. His research specializes in coding theory with applications to quantum computing, cryptography, and secure communications. His work bridges theoretical mathematics and practical implementations in information security and distributed systems. Research interests include the development of quantum-resistant cryptographic systems, error-correcting codes for quantum computing, and secure multiparty computation protocols. Recent publications focus on constructing specialized codes for quantum fault tolerance, developing zero-knowledge proof systems, creating efficient batch coding schemes for distributed storage, and designing secure computation methods for matrix operations. His work demonstrates consistent innovation in applying algebraic structures to solve contemporary challenges in information security and network communications.

• Combinatorics
• Number Theory
• Graph Theory
• +۴ مورد دیگر

Neil Calkin is a Professor in the Department of Mathematical and Statistical Sciences at Clemson University. His research focuses on combinatorics, number theory, and experimental mathematics. He has contributed to areas such as fractal eigenvectors, Bohemian matrices, and computational discovery using tools like Jupyter. His work bridges theoretical mathematics with practical applications in education and pandemic logistics. Research interests include combinatorial structures, graph theory, and algorithmic approaches to classical mathematical problems. Notable contributions involve non-attacking kings configurations in 3D, Newton fractals, and Mandelbrot polynomials analysis. He actively explores the intersection of historical mathematical methods (e.g., Schröder’s algorithm) with modern computational techniques. Publications from 2020-2024 highlight experimental mathematics in education, pandemic modeling, and advanced combinatorial configurations. No awards are explicitly listed in provided texts. Advising details are not specified, but his research collaborations include work with graduate students on topics like Bernoulli convolutions and elliptic curves.

Robert Kirbyمشاهده پروفایل

استاد

• Finite elements
• Partial differential equations
• Preconditioners
• +۲ مورد دیگر

Robert Kirby is a Professor of Mathematics and Undergraduate Advisor at Baylor University, affiliated with the College of Arts & Sciences and the Department of Mathematics. He holds a Ph.D. from the University of Texas at Austin (2000) and has held academic positions at Texas Tech University, the University of Chicago, and as a Dickson Instructor. His research focuses on automating numerical methods for partial differential equations (PDEs), including finite elements, preconditioners for multiphysics problems, and high-performance computing. Kirby's research interests bridge mathematics and computer science, emphasizing the development of efficient algorithms for PDE simulation using domain-specific languages, Bernstein polynomials, and multicore architectures. His work integrates theoretical analysis with practical software implementation in projects like FEniCS and Firedrake. His publications consistently address finite element methods, numerical stability, and computational efficiency. Recent articles emphasize automated time-stepping, domain truncation, and preconditioning techniques, reflecting a trend toward scalable and user-friendly tools for scientific computing. Grants and Advising: Kirby has secured significant funding, including NSF awards for automated algorithms and Sandia National Laboratory contracts. He advises doctoral students in mathematics and computing, with alumni at Amazon, NSA, and academia. Grants include: NSF CCF award 1117794: Metanumerical computing ($500k) NSF CCF award 0830655: Automated intrusive algorithms ($270k) DOE Early Career Award: Automatic parallel finite elements ($300k) He leads collaborations on open-source projects (FEniCS, Firedrake) and develops tools like FIAT and Irksome to streamline finite element workflows.

Umberto Grandiمشاهده پروفایل

استاد

• Computational Social Choice
• Artificial Intelligence
• Digital Democracy
• +۴ مورد دیگر

Umberto Grandi is a Professor of Computer Science at Toulouse Capitole University and a member of the Toulouse Institute for Research in Computer Science (IRIT), affiliated with the LILaC team. He holds a PhD from the University of Amsterdam (2012) and an HDR (2021) from Toulouse Capitole University. His research bridges artificial intelligence and economic theory, focusing on computational social choice, judgment aggregation, voting models, and digital democracy. He leads the ERC Synergy-funded ADDI project (€9.8M), exploring digital tools to enhance democratic practices. Collaborators include experts from the University of Amsterdam, Toulouse School of Economics, and University of Turku. His work emphasizes open-source solutions for participatory democracy and ethical AI. Education: PhD (University of Amsterdam, 2012), HDR (Toulouse Capitole, 2021) Affiliations: IRIT, LILaC team, Université Toulouse Capitole Research interests include computational social choice, digital democracy, and AI ethics. Recent projects integrate social sciences to develop algorithms for equitable decision-making and moral planning agents. Key Award: ERC Synergy Grant (2024) Publications: Over 50 articles in AI and social choice theory Supervises PhD and master’s students in computational social choice and digital democracy. Active in teaching and international master programs like the MIAGE program.

• Magnetic Particle Imaging
• Tomographic Imaging
• Image Reconstruction
• +۳ مورد دیگر

Prof. Tobias Knopp holds dual professorships in experimental Biomedical Imaging at the University Medical Center Hamburg-Eppendorf (UKE) and Hamburg University of Technology (TUHH). He leads the Institute for Biomedical Imaging and serves as Editor-in-Chief of the International Journal on Magnetic Particle Imaging (IJMPI). His expertise spans tomographic imaging, signal processing, and magnetic particle imaging (MPI), with a focus on advancing imaging technologies for clinical applications. Education: Tobias Knopp earned a Diplom in Computer Science (2007) and a PhD (2010) from the University of Lübeck, both with highest distinction. His doctoral work on MPI earned the Klee Award (2011). Career highlights include leading the MAPIT project, publishing the first MPI textbook, and developing the first commercial MPI system with Bruker Biospin (2011–2014). Research Interests: His work revolves around MPI innovation, including image reconstruction algorithms, system matrix optimization, and biomedical applications. Recent studies focus on reducing power consumption in MPI systems, improving motion correction in imaging, and applying deep learning for enhanced reconstruction. Publications reflect advancements in MPI fundamentals, such as field calibration using ellipsoidal harmonics, GPU-accelerated algorithms, and neural network-driven solutions. Awards include the Klee Award acknowledging his foundational contributions to MPI technology. As a leader in interdisciplinary research, Knopp collaborates across institutions and industries to bridge gaps between biomedical imaging theory and clinical practices. His lab develops cutting-edge tools like TrainingPhantoms.jl and explores MPI applications in perfusion imaging and catheter guidance.