جست‌وجوی استاد و پژوهشگر

Bedřich Sousedíkمشاهده پروفایل

دانشیار

• Applied and Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +۵ مورد دیگر

Bedřich Sousedík is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He joined UMBC in 2014, transitioning from roles as a Research Associate at the University of Southern California and University of Maryland, College Park. His academic journey includes a Ph.D. in Applied Mathematics (2010, University of Colorado Denver) and a Ph.D. in Civil Engineering (2008, Czech Technical University in Prague). Education: Ph.D. in Applied Mathematics, University of Colorado Denver (2010) Ph.D. in Civil Engineering, Czech Technical University (2008) M.Eng. in Mechanical Engineering, Czech Technical University (2001) Research Interests: Focuses on applied and computational mathematics, including numerical analysis, scientific computing, uncertainty quantification, stochastic finite element methods, and domain decomposition techniques. His work emphasizes scalable algorithms for complex systems with parametric uncertainty. Professional Contributions: Author of ~43 refereed publications (24 journals, 12 conferences) with an h-index of 15 (Google Scholar) Principal Investigator on NSF grants totaling $219,999 for multilevel methods research Recipient of prestigious awards including the 2010 Prof. Babuška Prize and 2020 Early Career Excellence Award Advising & Teaching: Supervised 8 Ph.D. and Master’s students since 2014 Mentored undergraduate research projects in computational fluid dynamics and epidemiological modeling

Mattia Zorziمشاهده پروفایل

دانشیار

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +۸ مورد دیگر

Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.

• Computational Mathematics
• Numerical Analysis
• Isogeometric Analysis
• +۳ مورد دیگر

Mattia Tani is a researcher at the Department of Mathematics, University of Pavia. He is affiliated with the Scientific Computing research group focusing on Numerical Methods and Applications. His work centers on developing advanced numerical techniques for computational mechanics, particularly Isogeometric Analysis (IGA), space-time methods, and efficient solvers. Tani’s research emphasizes computational efficiency through low-rank approximations, preconditioning strategies, and parallelization techniques. His academic contributions include pioneering work on matrix-free methods, Tucker tensor-based solvers, and domain decomposition approaches for multi-patch geometries. Notable methodologies include the IETI-DP method for discontinuous Galerkin discretizations and weighted quadrature rules for hierarchical B-splines. Tani also explores applications in parabolic problems, Schrödinger equations, and thermal engineering simulations. Publications highlight a focus on bridging theoretical numerical analysis with practical computational tools, with emphasis on scalability and robustness for large-scale problems. His work is disseminated across top journals and conferences, reflecting interdisciplinary collaboration between applied mathematics and engineering.

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.

• Theoretical Computer Science
• Statistics
• Probability
• +۵ مورد دیگر

Alex Wein is an Assistant Professor of Mathematics at the University of California, Davis. His research focuses on theoretical computer science, statistics, probability, and data science, with an emphasis on computational complexity of statistical inference, particularly in low-degree polynomial models. He explores challenges in tensor analysis, Bayesian inference, and group actions such as cryo-electron microscopy. His work bridges statistical physics and algorithmic hardness, contributing to foundational understanding of estimation and recovery thresholds. Education: Ph.D. in Mathematics, Massachusetts Institute of Technology (2018) Research Interests: Optimal algorithms for hidden structure detection in noisy data Low-degree polynomial framework for algorithmic hardness predictions Tensors and their computational challenges Connections between Bayesian methods and statistical physics Recent Talks: 'Sharp Phase Transitions in Estimation with Low-Degree Polynomials' at SLMath (2025) 'Unifying Statistical Physics and Low-Degree Polynomials?' at Les Houches School of Physics (2025)

• 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.

• Statistical Learning Theory
• Recommender Systems
• Matrix Completion
• +۵ مورد دیگر

Antoine LEDENT is a tenure-track Assistant Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), holding a PhD in Mathematics from the University of Luxembourg (2017) and undergraduate/graduate degrees from Clare College, University of Cambridge. His academic trajectory includes a postdoctoral position at TU Kaiserslautern under Prof. Marius Kloft, establishing his foundation in theoretical computer science. His educational background spans rigorous mathematical training at Cambridge followed by doctoral research on stochastic differential equations at Luxembourg, directly informing his current theoretical work. This path transitioned into computer science through postdoctoral research in algorithmic learning theory. Professor LEDENT specializes in Statistical Learning Theory and Recommender Systems, with groundbreaking contributions to generalization bounds for deep neural networks, contrastive learning frameworks, and matrix completion theory. His research uniquely bridges abstract mathematical principles with practical AI applications, particularly in high-dimensional data analysis and robust recommendation engines, emphasizing provable guarantees over empirical results. Analysis of his 15 most recent publications reveals a dominant focus on theoretical machine learning, with 70% concerning generalization analysis in deep learning and matrix completion. His work consistently targets top-tier venues (ICML, NeurIPS, AAAI), demonstrating expertise in transforming mathematical insights into scalable AI solutions, especially for non-IID data and uncertainty-aware systems. Key recognitions include: Outstanding Meta Reviewer (ICML 2025, top 2%) Area Chair appointments for NeurIPS/ICML 2025 Action Editor for TMLR (2024) Certificate of Excellence in Reviewing (KDD 2023) Top Reviewer distinctions at NeurIPS/ICML/ICLR (2022-2024) He actively mentors PhD candidates including AISG Scholarship recipient NONG Minh Hieu and SENARATH ARACHCHIGE Dilan Dinushka, offering fully funded 4-year positions with pathways to Presidential Scholarships. His recruitment targets mathematically strong candidates for projects in Statistical Learning Theory, Matrix Completion, or Recommender Systems, with stipends exceeding 6000 SGD/month for exceptional performers. Based in Singapore's AI hub at SMU—ranked #41 globally in AI (CSRankings)—he operates within SCIS's dynamic ecosystem that connects academia with regional AI startups and industry partners, leveraging Singapore's strategic position at the Asia-Pacific research nexus.

• Machine Learning
• Bayesian Statistics
• Latent Variable Models
• +۶ مورد دیگر

Piyush Rai is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He also holds an Adjunct Assistant Professor position in Electrical and Computer Engineering at Duke University. His academic journey includes postdoctoral research at Duke University and the University of Texas at Austin, following his PhD from the University of Utah. His research interests focus on Machine Learning and Bayesian Statistics, with specializations in Latent Variable Models, Probabilistic Modeling, Approximate Inference, and Nonparametric Bayesian Methods. His work bridges theoretical foundations with practical applications in artificial intelligence and data science. Dr. Rai's publication record shows a consistent focus on tensor factorization, Bayesian nonparametrics, and scalable algorithms for large datasets. His work spans conferences including NIPS, ICML, UAI, and AISTATS, demonstrating strong contributions to both theoretical and applied machine learning. Best Student Paper Award at ECML-PKDD (2015) National Science Foundation (USA) EAGER Award (2015) Dr. Deep Singh and Daljeet Kaur Faculty Fellowship at IIT Kanpur (2015) NIPS 2013 Reviewer Award Sheldon Ekland-Olson Postdoctoral Fellowship (2012) He teaches advanced courses in Machine Learning and Probabilistic Machine Learning at IIT Kanpur, mentoring the next generation of researchers in statistical machine learning techniques. His collaborative work with Lawrence Carin and other researchers demonstrates strong interdisciplinary connections between institutions.

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.

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +۳ مورد دیگر

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

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.

Dominika Zgidمشاهده پروفایل

استاد

• Theoretical Condensed Matter Physics
• Theoretical Atomic, Molecular & Optical Physics
• Quantum Chemistry
• +۳ مورد دیگر

Dr. Dominika Zgid is a Professor of Physics and Chemistry at the University of Michigan. Her research focuses on theoretical condensed matter physics, theoretical atomic, molecular, and optical physics, with a strong emphasis on developing advanced computational methods to understand electron correlation effects in materials and molecules. She holds a Ph.D. from the University of Waterloo (2008) and a B.Sc. from the University of Warsaw (2003). Her work bridges chemistry, physics, and materials science, aiming to create accurate theoretical tools for predicting experimentally relevant properties. Key projects include developing self-energy embedding theory (SEET), improving GW approximations, and exploring quantum computing applications for electronic structure problems. She has been recognized with a NSF CAREER Award in 2015 for her innovative contributions to Green's function methods. Zgid’s research addresses challenges such as describing weakly and strongly correlated electrons in transition metal systems, optimizing basis sets for solids, and modeling catalytic surfaces. Her group actively collaborates on projects involving molecular magnets, quantum dots, and superexchange mechanisms in heterostructures.

Laura Grigoriمشاهده پروفایل

استاد

• High-Performance Scientific Computing
• Numerical Linear Algebra
• Communication-Avoiding Algorithms
• +۶ مورد دیگر

Laura Grigori is a Professor of Applied and Computational Mathematics at EPFL and Head of the Laboratory for Simulation and Modeling at the Paul Scherrer Institute (PSI), Switzerland. She holds a Ph.D. in Computer Science from Université Henri Poincaré (2001) and has been a leading figure in high-performance numerical algorithms and communication-avoiding methods. Her research focuses on numerical linear algebra, randomized algorithms, and scalable scientific computing for applications in astrophysics, molecular simulations, and exascale systems. Affiliations: EPFL Chair of High-Performance Numerical Algorithms & Simulation; Head, PSI Laboratory for Simulation and Modeling. Education: Ph.D. in Computer Science, Université Henri Poincaré (2001); Postdoctoral Researcher, UC Berkeley/LBNL (2001–2003); Senior Researcher at INRIA (2004–2023). Research Interests: Communication-avoiding algorithms, randomized numerical linear algebra, parallel preconditioners, tensor computations, and exascale computing challenges. Awards: SIAM Fellow (2020), ERC Synergy Grant (2018), SIAM Supercomputing Career Prize (2024), and Best Paper Awards (2016, 2023). Grants & Leadership: PI of the ERC Synergy Project EMC² (Extreme-scale Computational Chemistry); Chair of PRACE Scientific Steering Committee (2021–2022); SIAM SIAG Supercomputing Chair (2016–2017). Labs/Teams: Alpines Group (INRIA/Sorbonne University), EMC² Collaboration (Cancès, Maday, Piquemal).

• Numerical Analysis
• Linear Algebra
• Matrix Equations
• +۴ مورد دیگر

Valeria Simoncini is a Full Professor in the Department of Mathematics at the University of Bologna. Her research focuses on numerical analysis, linear algebra, and computational mathematics, with particular emphasis on matrix equations, Krylov subspace methods, and optimization algorithms. She has contributed to advancements in iterative solvers, randomized sketching techniques, and preconditioning strategies for large-scale problems. Her work often intersects with applications in partial differential equations and scientific computing. Her research interests include the development of efficient numerical methods for solving matrix equations, such as Sylvester and Lyapunov equations, as well as optimization problems arising in control theory and data science. She explores topics like tensor decompositions, approximation techniques for matrix functions, and the analysis of numerical stability in iterative algorithms. Simoncini’s publications span over two decades, with recent contributions addressing the integration of randomized algorithms into numerical linear algebra, the acceleration of quadrature methods, and the application of low-rank approximations in high-dimensional problems. Her work frequently appears in top-tier journals such as SIAM Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications . Despite the extensive list of publications, no specific scientific awards or grants are explicitly mentioned in the provided text. She advises students through her academic role but no specific advisee names are listed here.