جست‌وجوی استاد و پژوهشگر

Jin-Yi Caiمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Complexity Theory
• Holographic Algorithms
• +۳ مورد دیگر

Jin-Yi Cai is a distinguished Professor of Computer Science and Steenbock Professor of Mathematical Sciences at the University of Wisconsin at Madison, where he has been a faculty member since 2000. Previously, he held academic positions at State University of New York at Buffalo (Professor 1996-2000, Associate Professor 1993-1996), Princeton University (Assistant Professor 1989-1993), and Yale University (Assistant Professor 1986-1989). He has also been a Radcliffe Institute Fellow at Harvard University (2007-2008) and a Guggenheim Fellow and Visiting Professor at the University of Toronto (1999-2000). Dr. Cai earned his Ph.D. in Computer Science from Cornell University in 1986, an M.A. in Mathematics from Temple University in 1983, and a Certificate in Mathematics from Fudan University in 1981. His academic journey spans prestigious institutions across the United States and demonstrates a consistent trajectory of scholarly excellence. Professor Cai's research focuses on theoretical computer science, particularly computational complexity theory, with significant contributions to holographic algorithms, counting constraint satisfaction problems, and graph homomorphisms. His work bridges computer science and mathematics, developing sophisticated algorithms and proving fundamental complexity results. His research has evolved from foundational work in structural complexity and oracle separations to specialized work in holographic algorithms and counting problems, demonstrating both depth and breadth in theoretical computer science. His publication record shows a consistent output of high-impact research, with major contributions spanning over three decades. His work on holographic algorithms represents a particularly innovative strand of research that has opened new avenues in computational complexity. The progression of his research demonstrates increasing specialization in counting problems while maintaining connections to broader theoretical frameworks in computer science and mathematics. 2022 Simons Fellowship 2022 CCF Award for Overseas Outstanding Contribution 2022 Fellow, American Mathematical Society (AMS) 2021 Fulkerson Prize in Discrete Mathematics 2021 Gödel Prize in Theoretical Computer Science 2014 Steenbock Professorship, UW Madison 2001 ACM Fellow 1998 John Simon Guggenheim Fellowship 1994 Sloan Fellowship Professor Cai has served as Editor of the Journal of Computer and System Sciences and Associate Editor of the Journal of Computational Complexity. His work has been recognized with numerous prestigious fellowships including the Guggenheim Fellowship, Sloan Fellowship, and Humboldt Research Award. His research has had significant impact in theoretical computer science, earning him the Gödel Prize and Fulkerson Prize, two of the most prestigious awards in theoretical computer science and discrete mathematics respectively.

Mareike Dresslerمشاهده پروفایل

مدرس ارشد

• Real Algebraic Geometry
• Computational Algebraic Geometry
• Polynomial Optimization
• +۶ مورد دیگر

Mareike Dressler is a Senior Lecturer (A/Professor, tenured) and ARC Discovery Early Career Research Fellow at the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney). She joined UNSW in February 2022 as a Lecturer (Assistant Professor, tenure-track) and was promoted to Senior Lecturer with tenure in July 2024. Her educational background includes: PhD in Mathematics from Goethe-Universität Frankfurt/Main (2018), supervised by Thorsten Theobald M.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2013) B.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2010) Mareike's research focuses on real and computational algebraic geometry and polynomial and convex optimization. She also works on problems intersecting with convex geometry, matrix and tensor computation, applied algebraic geometry, algebraic and geometric combinatorics, and real analysis. Her work particularly involves nonnegativity of polynomials, optimization methods, and ranks of matrices and tensors, with a special interest in sums of nonnegative circuits (SONCs) for sparse polynomials. Her research has strong applications in data science and machine learning. Mareike has received several prestigious awards including the ARC Discovery Project 2025 grant for "Quantifying Uncertainty of Risk-Aware Optimization for Safe Decision-Making," the J G Russell Award from the Australian Academy of Science, and the Early Career Impact Award from UNSW Science. She actively supervises research students, including PhD student Hongzhi Liao, Master's student Qi Wang, and recently supervised Moritz Schick to completion of his PhD in 2025. Mareike has secured significant research funding, including an ARC Discovery Early Career Researcher Award (DECRA) for 2024-2026. Mareike is an active member of the academic community, regularly organizing workshops and conferences such as "Optimization Days" at UNSW and minisymposia at major conferences like SIAM Conference on Applied Algebraic Geometry.

Yaniv Planمشاهده پروفایل

دانشیار

• Applied Probability
• Compressive Sensing
• Matrix Completion
• +۳ مورد دیگر

Yaniv Plan is an Associate Professor of Mathematics at the University of British Columbia. His research focuses on applied probability, compressive sensing, matrix completion, and mathematical foundations of machine learning. He co-organizes the interdisciplinary group Mathematics of Information, Learning, and Data (MILD). His work bridges high-dimensional probability with applications in signal processing and data science. He teaches advanced courses such as Probability in High Dimensions and Compressed Sensing , emphasizing theoretical foundations like concentration inequalities, random matrix theory, and optimization. His research has led to advancements in 1-bit compressed sensing, matrix completion, and robust recovery algorithms. Notably, he received the NIPS 2018 Best Paper Award for contributions to learning Gaussian mixtures via sample compression. Plan’s publications address topics like sub-Gaussian matrices, weighted matrix completion, and non-smooth stochastic gradient descent. His work often combines rigorous mathematical analysis with practical applications in compressed sensing and sparse recovery. He collaborates widely, with co-authors including Roman Vershynin, Emmanuel Candès, and Mary Wootters. His research has been supported by grants exploring compressed sensing, high-dimensional data, and algorithmic robustness.

Song Hanمشاهده پروفایل

دانشیار

• Efficient Deep Learning
• Neural Network Compression
• Hardware-Aware AI
• +۷ مورد دیگر

Song Han is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). His research focuses on efficient deep learning computing, bridging algorithm and hardware design to enable scalable AI systems. PhD in Electrical Engineering from Stanford University Research Interests Efficient Deep Learning Neural Network Compression Hardware-Aware Transformers Sparse Attention Mechanisms Quantization Techniques Edge and IoT Computing Recent Publication Trends highlight advances in LLM optimization, diffusion model quantization, and quantum-classical co-design. His work emphasizes reducing computational costs while maintaining model fidelity. Scientific Awards Best Paper, ICLR and FPGA Symposium NSF CAREER Award MIT Technology Review 35 Innovators Under 35 Collaborations include the MIT-IBM Watson AI Lab, focusing on AI hardware and system co-design. Many of his techniques are integrated into commercial AI chips.

• Numerical Linear Algebra
• High Performance Computing
• Krylov Subspace Methods
• +۴ مورد دیگر

Dr. Hussam Al Daas is a Postdoctoral Research Fellow at the Max Planck Institute for Dynamics of Complex Technical Systems since January 2019, specializing in numerical algorithms for large-scale scientific computing. His work bridges theoretical numerical analysis and high-performance implementation. Education: PhD in Applied Mathematics, Inria-Paris and Sorbonne University (2015-2018), funded by TOTAL Master in Fundamental and Applied Mathematics, Paris-Sud University (2012-2014) His research centers on developing communication-avoiding algorithms for sparse linear systems and tensor computations, with emphasis on robust preconditioners (algebraic two-level Schwarz, domain decomposition), Krylov subspace methods, and low-rank approximations. He addresses critical challenges in parallel scalability through rigorous complexity analysis and memory-efficient implementations, particularly for distributed-memory architectures. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Algebraic multilevel preconditioners achieving robustness for ill-conditioned sparse systems, (2) Theoretical communication lower bounds with optimal algorithm design for tensor/matrix operations, and (3) Novel extensions of Krylov methods for sequences of shifted systems and tensor train decompositions. These contributions target reservoir simulation, PDE-constrained optimization, and high-dimensional data problems. Funded by TOTAL during his PhD and currently by the Max Planck Society, his work shows no evidence of student advisement or external grant leadership. He contributes to the Computational Methods in Systems and Control Theory group, focusing on scalable solvers for complex dynamical systems through collaborative software development and algorithmic innovation.

Sriram Krishnamoorthyمشاهده پروفایل

استاد پژوهشی

• Parallel Programming Models
• Fault Tolerance
• High-Performance Computing
• +۶ مورد دیگر

Sriram Krishnamoorthy is a Research Professor at Washington State University's School of Electrical Engineering & Computer Science and a research scientist at Pacific Northwest National Laboratory (PNNL), where he serves as the System Software and Applications Team Leader in PNNL's High Performance Computing group. Dr. Krishnamoorthy earned his B.E. from the College of Engineering, Guindy in Chennai, India, and his M.S. and Ph.D. degrees from The Ohio State University. He is a senior member of the Institute of Electrical and Electronics Engineers. His research focuses on parallel programming models, fault tolerance, and compile-time/runtime optimizations for high-performance computing. He has made significant contributions in areas including: Fault tolerance techniques that minimize rollback during failures Dynamic load balancing for irregular parallel applications Compiler and runtime optimizations for HPC applications GPU programming and heterogeneous computing Quantum chemistry simulations and quantum computing Dr. Krishnamoorthy's publications span computational science, high-performance computing, and quantum chemistry. His recent work shows strong trends toward quantum computing applications, fault tolerance in large-scale systems, and optimization of computational chemistry methods. He has developed techniques for density matrix quantum circuit simulation, floating-point error analysis, and scalable execution of coupled-cluster models. His scientific achievements have been recognized with several prestigious awards: Best Paper Award at International Conference on High Performance Computing (HiPC'03) Best Paper Award at International Parallel and Distributed Processing Symposium (IPDPS'04) U.S. Department of Energy Early Career award (2013) PNNL's Ronald L. Brodzinski Award for Early Career Exceptional Achievement (2013) The Ohio State University's Outstanding Researcher award (2008) Dr. Krishnamoorthy has advised numerous graduate students and collaborated extensively with researchers across computational science domains. His work on the NWChem project demonstrates significant grant funding and large-scale collaborative research efforts in computational chemistry. He leads research efforts in PNNL's High Performance Computing group, focusing on system software and applications development for next-generation supercomputing platforms.

• High-dimensional Probability
• Random Matrices
• Random Graphs
• +۹ مورد دیگر

Nicholas Cook is an Assistant Professor in the Department of Mathematics at Duke University. Currently on leave at the Berkeley SLMath Institute for the program on Probability and Statistics of Discrete Structures (Spring 2025). He will teach Math 690-40: Introduction to Random Matrix Theory in Fall 2025. His research focuses on high-dimensional probability, including random matrices, random graphs, large deviations, and universality phenomena, with connections to graph theory, additive combinatorics, free probability, and mathematical physics. Background: Cook earned his PhD in 2016 from UCLA under Terence Tao. He was a Stein Fellow at Stanford University's Statistics Department and held NSF postdoctoral positions at Stanford and UCLA. His academic journey includes affiliations with Amir Dembo and Jun Yin as postdoctoral sponsors. Research interests span broad areas such as concentration of measure, universality in random structures, and applications in numerical analysis and statistics. His recent work emphasizes large deviation principles for eigenvalues, universality in random matrix ensembles, and combinatorial problems in graph theory. Articles published since 2018 reflect a deep engagement with spectral properties of random structures and their probabilistic behaviors. Teaching and academic service: Currently on research leave, Cook has taught advanced courses in probability and random matrix theory. His academic contributions are supported by NSF grants, though specific grant details are not provided here.

• High-dimensional probability
• Random matrix theory
• Random graph theory
• +۵ مورد دیگر

Nicholas A Cook is an Assistant Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. His research focuses on high-dimensional probability, random matrices, random graphs, and large deviations theory. He holds a Ph.D. from UCLA (2016), advised by Terence Tao, and has held postdoctoral positions at Stanford and UCLA. Cook has received the Bernoulli Society New Researcher Award (2019). Education: Ph.D. in Mathematics, University of California, Los Angeles (2016) Bachelor's degree (not detailed) Research Interests: High-dimensional probability, random matrix theory (including Wigner matrices, Ginibre ensembles, and non-Hermitian matrices), random graph theory (sparse ERGMs, regular digraphs), large deviations for subgraph counts, universality phenomena, and applications to numerical analysis and mathematical physics. His work often intersects with combinatorics, functional analysis, and statistical mechanics. Grants & Awards: NSF RTG Grant: Training Tomorrow's Workforce in Analysis and Applications (Co-PI, 2021–2026) NSF Grant: Large Deviations and Extremes for Random Matrices, Tensors, and Fields (PI, 2022–2025) Stein Fellowship, Stanford University (2019–2020) Bernoulli Society New Researcher Award (2019) Advising: PhD advisee: Haotian Gu (2021–present) Undergraduate mentorships: DOMath research project on Brascamp–Lieb inequalities (2022) and Pengda Liu's honors thesis (2019–2020) Teaching: Spring 2025: On leave at Berkeley SLMath Institute Fall 2025: Teaching Math 690-40 (Introduction to Random Matrix Theory)

Marc CASTELLAمشاهده پروفایل

مدرس

• Signal Processing
• Blind Source Separation
• Neural Networks
• +۳ مورد دیگر

Prof. Marc CASTELLA is a Lecturer at Telecom SudParis, part of the SOP (Signal and Optimization Processing) unit. His research focuses on signal processing, particularly in blind source separation, nonlinear reconstruction, and optimization techniques. He has extensively contributed to areas such as sparse signal recovery, neural networks, and tensor decomposition. His work often addresses challenges in noisy environments and nonlinear systems. Recent publications include advancements in clipped signal recovery, motor torque estimation, and global optimization methods. Though no specific awards are listed, his prolific publication record reflects his impactful contributions to signal processing and applied mathematics. He collaborates with researchers like Jean-Christophe Pesquet and Arthur Marmin on projects involving polynomial systems and neural network models. His work is applied in diverse fields from industrial electronics to data analysis.

Peter Oswaldمشاهده پروفایل

استاد

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +۷ مورد دیگر

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

César A. Uribeمشاهده پروفایل

استادیار

• Distributed Optimization
• Decentralized Control
• Algorithm Analysis
• +۵ مورد دیگر

César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.

Daniel Irving Bernsteinمشاهده پروفایل

استادیار

• Combinatorics
• Discrete Geometry
• Algebraic Statistics
• +۲ مورد دیگر

Daniel Irving Bernstein is Assistant Professor in Tulane University's Department of Mathematics. His research spans combinatorics, discrete geometry, and algebraic statistics, with particular focus on matroid theory, rigidity theory, and geometric combinatorics. He teaches undergraduate and graduate courses in linear algebra, topology, and geometric combinatorics. His research explores: Combinatorial structures in algebraic statistics Geometric rigidity and reconstructibility problems Matroid representations and lifts Algebraic methods in machine learning Recent work develops connections between combinatorial geometry and statistical learning theory. His publications demonstrate: Applications of matroid theory to statistical thresholds Geometric approaches to matrix completion Combinatorial foundations of deep learning Tropical geometric methods in phylogenetics He maintains active collaboration with computational biology and machine learning researchers.

• Machine Learning
• Bioinformatics
• Pattern Recognition
• +۵ مورد دیگر

Riikka Huusari is a Visiting Professor at the Department of Computer Science at Aalto University. Her research focuses on advanced machine learning techniques, particularly in the domains of bioinformatics, pattern recognition, and computational biology. She has collaborated with Professor Juho Rousu and other researchers on various projects. Her recent work includes scalable methods for two-view learning, cross-view kernel transfer, and entangled kernel approaches that extend beyond traditional separability. Publications in venues such as Briefings in Bioinformatics, Machine Learning, and the Journal of Machine Learning Research highlight her contributions to these fields. Riikka's research applies mathematical and computational techniques to solve complex problems in drug discovery and multiview data analysis. She utilizes projection operators, sparse kernel methods, and low-rank decompositions to improve predictive modeling and algorithmic efficiency. Her work bridges theoretical machine learning with practical biomedical applications.

Alperen Ergürمشاهده پروفایل

استادیار

• Real algebraic geometry
• Convex geometry
• Randomized numerical algorithms
• +۵ مورد دیگر

Alperen Ergür is an Assistant Professor at the University of Texas at San Antonio , affiliated with the College of Sciences and Department of Mathematics. His teaching integrates modern mathematical and computational skills, including courses on probability theory, optimization, algorithmic foundations of data science, reinforcement learning, and computational abstract algebra. Ph.D. in Mathematics from Texas A&M University M.S. in Mathematics from TOBB University of Science and Technology B.S. in Mathematics from Bilkent University Research spans real algebraic geometry , convex geometry , randomized numerical algorithms , and reinforcement learning . Key trends in his publications include: Polynomial system analysis Tropical geometry applications Tensor rank approximation Algorithm complexity in computational geometry Probabilistic methods in optimization Homotopy algorithms for real zeros He organizes the UTSA Algorithms Seminar and co-founded a student-friendly research seminar on Geometry, Probability, and Computing with Grigoris Paouris and Petros Valettas. With Suleyman Tek and Rodrigo Velez, he established a creative/competitive math circle for middle schoolers.

Berkant Savasمشاهده پروفایل

دانشیار

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +۳ مورد دیگر

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.