جست‌وجوی استاد و پژوهشگر

Jose Israel Rodriguezمشاهده پروفایل

دانشیار

• Applied Algebraic Geometry
• Algebraic Statistics
• Nonlinear Algebra
• +۷ مورد دیگر

Jose Israel Rodriguez is an Associate Professor in the Department of Mathematics at the University of Wisconsin-Madison. His research bridges applied algebraic geometry and algebraic statistics, focusing on nonlinear algebra, maximum likelihood estimation, monodromy, and polynomial systems in engineering and science applications. Primary Affiliation: Department of Mathematics , UW-Madison Additional Affiliations: Department of Electrical & Computer Engineering , Institute for Foundations of Data Science Research Interests : Applied algebraic geometry for nonlinear eigenvalue problems and kinematics Algebraic statistics in nearest point problems and likelihood geometry Numerical methods for monodromy, Galois groups, and polynomial optimization Teaching and Mentorship : Co-organized the Collaborative Undergraduate Research Laboratory (CURL) for Spring 2020 Advises PhD students Julia Lindberg and Zinan Wang , with Bernd Sturmfels as his own PhD advisor Developed software tools like Multiregeneration and Decomposable Sparse Polynomial Systems Academic Contributions : Authored over 20 peer-reviewed publications in journals like SIAM Journal on Applied Algebra and Geometry, Foundations of Computational Mathematics, and Journal of Symbolic Computation Organized international conferences including Monodromy and Galois Groups in Enumerative Geometry and SIAM AG19 Active member of the SIAM community and developer of the Matroids Day seminar

• Generative AI
• Information Theory
• Machine Learning
• +۴ مورد دیگر

Alexandros G. Dimakis is a Professor at the University of California, Berkeley's Department of Electrical Engineering and Computer Sciences (EECS), College of Engineering. He is also Co-Director of the National AI Institute for Foundations of Machine Learning and Co-Founder of BespokeLabs.ai. PhD (2008) and Diploma (2003) in Electrical Engineering His research focuses on Generative AI , Information Theory , and Machine Learning . Recent work includes advancements in diffusion models, compressed sensing, and causal inference. His publications (150+) emphasize inverse problems, neural network verification, and generative model optimization. Recent publications highlight trends in Diffusion Models for inverse problems, Language Model Scaling , and 3D-Aware Generative Systems . Collaborative projects span biomedical applications, large-scale dataset curation (Datacomp-LM), and parameter-efficient model fine-tuning. Scientific Awards : IEEE Fellow (2022) James Massey Award (2018) NSF CAREER Award (2011) Google Research Faculty Award Best Paper awards at UAI workshops Eli Jury Dissertation Award (UC Berkeley) He advises PhD students in generative modeling, compressed sensing, and information theory. His research group collaborates with institutions like MIT, NYU, and IBM Research. Former students hold positions at Google, Amazon, and academic institutions like Purdue University.

• Higher-order finite element methods
• Numerical linear algebra
• Multigrid methods
• +۳ مورد دیگر

Prof. Dr. Martin Kronbichler is a faculty member at the Faculty of Mathematics , Ruhr University Bochum , leading the Numerics group. His research focuses on higher-order finite element methods, multigrid techniques, and high-performance computing for complex fluid and solid mechanics problems. Key Research Areas: Higher-order finite element methods, iterative solvers, multigrid algorithms, exascale mathematical software, and computational fluid dynamics. Notable Projects: EU-funded dealii-X (exascale digital twins), BMBF PDExa (optimized PDE solvers for exascale), and DFG grants for cut-discontinuous Galerkin methods and geometric multigrid. Publications Trends: Recent works emphasize matrix-free operators for hyperelasticity, diffuse-interface models for additive manufacturing, and multigrid smoothers for higher-order elements. Scientific Awards: Recipient of the Humboldt Research Award for his contributions to numerical methods and HPC. Team: Collaborates with researchers like Dr. Shubham Kumar Goswami, Dr. Richard Schussnig, and Natalia Nebulishvili.

Stephen A. Vavasisمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science
• First-Order Methods
• +۴ مورد دیگر

Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.

Vadim Lozinمشاهده پروفایل

استاد

• Graph theory
• Combinatorics
• Discrete Mathematics
• +۴ مورد دیگر

Vadim Lozin is a Professor of Mathematics at the University of Warwick, affiliated with the Department of Mathematics within the School of Mathematics. His research interests span graph theory, combinatorics, and discrete mathematics, focusing on areas such as clique-width, Ramsey numbers, and structural graph theory. He has held visiting positions at institutions including the Université Paris-Dauphine, EPFL, and KAUST. Lozin has received several accolades, including the Best Paper Award for 'Linear Ramsey numbers' in 2018 and a 2024 award at the International Symposium on Algorithms and Computation. His work involves collaborations with global researchers and contributions to conferences like IWOCA and WG. Lozin serves on editorial boards for journals such as Discrete Applied Mathematics and Electronic Notes in Discrete Mathematics . His research explores foundational problems in graph theory, with applications in algorithm design and complexity analysis. Lozin’s publications include studies on union-closed sets, functional graph properties, and algorithmic approaches to graph parameters. He has also contributed to books like Words and Graphs , bridging formal language theory with graph structures. His grants focus on clique-width and stability in graphs, reflecting his commitment to advancing theoretical and applied discrete mathematics.

Jee Choiمشاهده پروفایل

استادیار

• High Performance Computing
• Parallel and Scalable Algorithm Design
• Performance Analysis Optimization
• +۲ مورد دیگر

Jee Choi is an Assistant Professor in the Department of Computer and Information Science within the College of Arts and Sciences at the University of Oregon. His research focuses on developing high-performance algorithms for big data analytics, particularly in tensor decomposition and parallel computing systems. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology, 2015 MS in Electrical and Computer Engineering, Georgia Institute of Technology, 2004 BS in Electrical and Computer Engineering, Georgia Institute of Technology, 2000 Research Interests: Dr. Choi specializes in High Performance Computing with expertise in parallel algorithm design, performance modeling, and energy efficiency. His work targets Tensor Decomposition & Data Mining for big data, developing scalable solutions for sparse and dense tensor computations. He advocates leveraging HPC systems to transform massive datasets into societal benefits through efficient data mining techniques. Publication Trends: Choi's recent publications (2021-2025) demonstrate consistent innovation in tensor decomposition methodologies, with increasing focus on adaptive storage (Alto), streaming data factorization, and energy-aware autotuning. His work spans GPU clusters, distributed systems, and emerging architectures, maintaining strong connections to real-world big data challenges while advancing theoretical algorithm design. Professional Background: After completing his PhD under Richard Vuduc at Georgia Tech (notable for SpMV GPU autotuning and Energy Roofline Model), Choi conducted DoD-funded research on tensor decomposition at IBM Watson Research Center (2015-2018) before joining the University of Oregon faculty.

Christian Bargetzمشاهده پروفایل

استاد

• Nonlinear Functional Analysis
• Banach Spaces
• Locally Convex Spaces
• +۴ مورد دیگر

Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

• Quantum Computation
• Quantum Algorithms
• Computational Complexity
• +۳ مورد دیگر

Ashley M R Montanaro is a Professor of Quantum Computation at the School of Mathematics, University of Bristol . Active in quantum computing research since at least 2014, they lead projects at the intersection of quantum algorithms , computational complexity , and quantum information theory , affiliated with the Bristol Quantum Information Institute. Research interests focus on quantum algorithm design , computational complexity analysis , and quantum simulation . Key work includes developing variational quantum algorithms for phase transition detection, Hamiltonian simulation techniques, and quantum-classical hybrid methods for solving complex problems in physics and optimization. Recent publications demonstrate expertise in: Quantum phase diagram simulation with low-depth circuits Quantum speedups for constraint satisfaction problems Quantum communication complexity of machine learning tasks Quantum-enhanced optimization heuristics Hamiltonian simulation with time-dependent product formulas Quantum algorithm complexity analysis Scientific awards include: EPSRC Fellowship (2014-2019) - "New insights in quantum algorithms and complexity" Active in quantum software development through projects like: "Quantum Algorithms from Foundations to Applications" (ERC-2018-COG) "Quantum Computing and Simulation Hub" (2019-2024) "Prosperity Partnership in Quantum Software" (2019-2023)

• Numerical Analysis
• Partial Differential Equations
• Wave Propagation
• +۷ مورد دیگر

Olof Runborg is a Professor in Numerical Analysis at the Royal Institute of Technology (KTH) in Stockholm, Sweden. He works in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH. His research focuses on developing and analyzing numerical methods for partial differential equations, particularly for wave propagation problems. His educational background includes: MSc in Electrical Engineering from KTH (1992) BSc in Economics & Business Administration from SSE (1994) PhD in Numerical Analysis/Applied Mathematics from KTH (1998) Postdoc at Paris VI University (1999) Postdoc at Princeton University's Program in Applied and Computational Mathematics (2000-2001) Became Docent at NADA in 2003 Appointed Professor at KTH in 2010 Professor Runborg's research centers on the numerical treatment of partial differential equations, with special emphasis on wave propagation problems. His work spans multiple areas including high-frequency waves, multiscale phenomena, numerical homogenization, uncertainty quantification, multiresolution analysis, Gaussian beams, and mesh generation. A recurring theme in his research is developing methods to solve computationally expensive problems more efficiently while maintaining accuracy. His approach often involves coupling different numerical methods or reformulating equations to create more efficient computational approaches. His research has applications across physics and engineering domains where wave phenomena are important. An analysis of his recent publications (2015-2025) shows continued focus on high-frequency wave propagation with expanding applications to areas like the Landau-Lifshitz equation for magnetic materials and elastic wave propagation. His work bridges theoretical numerical analysis with practical computational techniques, often developing novel methods like the WaveHoltz iteration for solving the Helmholtz equation. The publications demonstrate increasing attention to uncertainty quantification and multiscale methods while maintaining strong theoretical foundations in error analysis. Professor Runborg teaches several courses at KTH including Numerical Methods (basic course), Applied Numerical Methods, Numerical Algorithms for Data-Intensive Science, and Selected Topics in Numerical Analysis II. He serves as examiner for degree projects in Scientific Computing. His teaching reflects his research expertise in numerical methods and computational mathematics, providing students with both theoretical foundations and practical implementation skills. Beyond his core research, Professor Runborg has engaged in interesting side projects, such as his analysis of 'Numbers on the Web' where he investigated the frequency of numbers 11-1000 in web content, revealing patterns related to dates, time, computer systems, and cultural phenomena. This demonstrates his broader interest in data analysis and computational approaches to understanding patterns in information.

Arkadi Nemirovskiمشاهده پروفایل

استاد

• Optimization Theory and Algorithms
• Convex Optimization
• Robust Optimization
• +۴ مورد دیگر

Arkadi Nemirovski is the John P. Hunter, Jr. Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech. He holds a Ph.D. in Mathematics (1974) from Moscow State University, a Doctor of Sciences in Mathematics (1990) from the USSR Supreme Attestation Board, and an honorary Doctor of Mathematics from the University of Waterloo (2009). Ph.D. in Mathematics, Moscow State University (1974) Doctor of Sciences in Mathematics, USSR Supreme Attestation Board (1990) Doctor of Mathematics (Honoris Causa), University of Waterloo (2009) His research focuses on Optimization Theory and Algorithms , with emphasis on complexity analysis, efficient methods for nonlinear convex programs, robust optimization, optimization under uncertainty, and applications in engineering and nonparametric statistics. He has pioneered advancements in interior-point methods, semidefinite programming, and stochastic approximation, shaping modern convex optimization. His article trends highlight a trajectory from foundational interior-point algorithms (1990s) to robust optimization (2000s) and recent works on first-order methods, polyhedral estimates, and applications in machine learning, signal processing, and tomography. Key subfields include matrix norms , large-scale optimization , and stochastic uncertainty handling . Scientific awards include: 1982 Fulkerson Prize (joint with L. Khachiyan and D. Yudin) 1991 Dantzig Prize (joint with M. Grotschel) 2003 John von Neumann Theory Prize (joint with M. Todd) 2017 Member, National Academy of Engineering 2018 Fellow, American Academy of Arts and Sciences 2020 Norbert Wiener Prize (joint with M. Berger) He has supervised students like Dmitry Gabelev (polynomial-time cutting plane algorithms), Daureen Steinberg (matrix norms in robust optimization), and Eitan Rubinstein (SVMs via advanced optimization), with their works later formalized in academic journals.

Nathan Linialمشاهده پروفایل

استاد

• Combinatorics
• Theoretical Computer Science
• Bioinformatics
• +۶ مورد دیگر

Nathan (Nati) Linial is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, where he has been a faculty member since completing his postdoctoral period at UCLA. He earned his undergraduate degree in mathematics from the Technion and his PhD in graph theory from the Hebrew University. His research spans multiple areas of theoretical computer science and mathematics, with primary focus on combinatorics, theoretical computer science, and bioinformatics. Linial's work has made significant contributions to high-dimensional combinatorics, expander graphs, metric embeddings, and computational molecular biology. His research often bridges geometry, analysis, and combinatorial structures, demonstrating deep connections between seemingly disparate mathematical fields. Linial's recent publications reveal a strong trend toward high-dimensional combinatorial structures, including simplicial complexes, hypertrees, and high-dimensional permutations. His work frequently employs probabilistic methods, linear programming techniques, and geometric approaches to solve fundamental combinatorial problems. The breadth of his research is evident in both pure mathematical contributions and applications to computational biology. Fellow of the American Mathematical Society ISI Highly Cited Researcher Conant Prize (2008) for the influential survey paper "Expander graphs and their applications" Linial has served on the editorial boards of several prestigious journals including the Israel Journal of Mathematics (as Chief Editor 2013-2017), Random Structures and Algorithms, and Combinatorica. His academic leadership extends to organizing conferences and workshops in combinatorics and theoretical computer science. He has mentored numerous students whose work spans theoretical computer science, combinatorics, and computational biology. Linial is associated with research projects including ProtoNet (for protein sequence classification) and EVEREST (for evolutionary conserved protein domains), demonstrating his commitment to interdisciplinary research that bridges computer science with molecular biology.

Elias Jarlebringمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Matrix computations
• Nonlinear eigenvalue problems
• +۱۰ مورد دیگر

Elias Jarlebring is a Professor in Numerical Linear Algebra at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. He has held the position of Full Professor since 2021, following his tenure as Associate Professor (2013-2021) and Dahlquist Research Fellow (2011-2013). His research focuses on numerical analysis, numerical linear algebra, matrix computations, and scientific computing. Jarlebring develops linear algebra algorithms to solve problems from various fields including systems and control, acoustics, electromagnetics, data science, quantum mechanics, and quantum chemistry. He is a core developer of NEP-PACK, a scientific computing software package for nonlinear eigenproblems. His recent publications demonstrate significant contributions to computational methods for nonlinear eigenvalue problems, matrix functions, and parameterized linear systems. The research shows a clear trajectory toward increasingly complex applications in quantum computing, data science, and wave propagation problems. Project grant, Swedish research council (2019) Ruth och Nils-Erik Stenbäcks foundation, junior grant (2019) Göran Gustafsson Prize for junior researchers (2014) Project grant for junior researchers, Swedish research council (2014-2018) Professor Jarlebring has supervised numerous PhD students including Vilhelm Peterson Lithell, Gustaf Lorentzon, Siobhán Correnty, Parikshit Upadhyaya, Emil Ringh, Antti Koskela, and Giampaolo Mele. He has received multiple research grants from the Swedish Research Council and serves as editor for BIT Numerical Mathematics, Linear and Multilinear Algebra, NACO Numerical Algebra Control and Optimization, and CALCOLO. He is actively involved in the numerical linear algebra community as a member of ILAS (International Linear Algebra Society), GAMM Activity Group on Numerical Linear Algebra, and the Nordic Numerical Linear Algebra Association. He also contributes to open source projects, particularly in the Julia programming language ecosystem.

Dan Jiaoمشاهده پروفایل

استاد

• Computational Electromagnetics
• Computational Multiphysics
• Electronic Design Automation (EDA)
• +۸ مورد دیگر

Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team

Chris De Saمشاهده پروفایل

دانشیار

• Machine Learning
• Distributed Systems
• Optimization Algorithms
• +۵ مورد دیگر

Chris De Sa is an Associate Professor in the Department of Computer Science at Cornell University, affiliated with the Cornell Machine Learning Group and leading the Relax ML Lab. His research focuses on algorithmic, software, and hardware techniques for high-performance machine learning, particularly relaxed-consistency stochastic algorithms like asynchronous and low-precision stochastic gradient descent (SGD). He earned his Ph.D. from Stanford University under advisors Kunle Olukotun and Chris Ré. His work emphasizes constructing efficient, parallel, and distributed machine learning frameworks for deep learning and data analytics. Education: Ph.D. in Computer Science, Stanford University (2017) Research Interests: Algorithmic techniques for scalable ML, quantization, distributed optimization, hyperbolic geometry in ML, and reliable measurement of ML systems. His group develops frameworks for efficient inference/training and explores the intersection of ML with domains like agriculture and plant science through courses like PLSCI 7202. Recent Highlights: DARPA YFA Grant (2024), NSF CAREER Award, Google Research Scholar Award, and multiple best paper recognitions. Key contributions include QuIP quantization methods, Coneheads attention mechanisms, and theoretical advances in decentralized training. Awards: NSF CAREER Award DARPA YFA Grant (2024) Google Research Scholar Award Mr. & Mrs. Richard F. Tucker Teaching Award Grants & Advising: Advises 8 Ph.D. students (including Ruqi Zhang, Yucheng Lu, A. Feder Cooper) and holds leadership roles in MLSys conferences. Active in grant-funded research (e.g., NSF Robust Intelligence). Labs/Teams: Leads the Relax ML Lab and participates in Cornell’s Institute for Digital Agriculture (CIDA).