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Ninoslav TruharView profile

Professor

• Numerical Linear Algebra
• Systems and Control Theory
• Applied Mathematics
• +4 more

Ninoslav Truhar is Full Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek , Croatia, where he also heads the Department of Applied Mathematics and Computer Science . Education B.S. in Mathematics and Physics, 1987 – University of Osijek M.S. in Mathematics, 1995 – University of Zagreb Ph.D. in Mathematics, 2000 – University of Zagreb Research interests lie at the intersection of numerical linear algebra , systems and control theory , and applied mathematics . His work centres on linear matrix equations , damping optimization in mechanical systems, and the perturbation theory of invariant subspaces . These themes repeatedly appear in his extensive publication record, driving both theoretical advances and practical applications in vibration control and matrix computations. Across his recent publications, the dominant trends are the derivation of sharp perturbation bounds for eigenvalues and eigenspaces of structured matrix problems, the development of efficient algorithms for damping parameter optimisation, and the creation of dimension-reduction techniques that accelerate numerical solution of large-scale Lyapunov and quadratic eigenvalue problems. Professional service Member of the editorial boards of Computational Mathematics and Computer Modeling with Applications , Numerical Algebra, Control and Optimization , Mathematics (MDPI) , Mathematical Communications , and Osječki matematički list . Teaching Undergraduate/graduate courses: Applied Linear Algebra and Scientific Computing , Optimisation Methods , Dynamical Systems . Supervises B.Sc. and M.Sc. theses on topics aligned with his research expertise.

Ting Kei PongView profile

Professor

• Continuous optimization
• Convex relaxations
• First-order methods
• +3 more

Ting Kei Pong is a Professor in the Department of Applied Mathematics at the Hong Kong Polytechnic University, where he has been employed since August 1, 2014. His research focuses on continuous optimization, with particular expertise in convex relaxations and first-order methods for large-scale optimization problems. Dr. Pong received his Bachelor's degree in 2004 and MPhil degree in 2006 from the Department of Mathematics at the Chinese University of Hong Kong. He completed his PhD in 2011 from the Department of Mathematics at the University of Washington under the supervision of Professor Paul Tseng, with co-advisement from Professors Maryam Fazel and Rekha Thomas after Professor Tseng's disappearance. His postdoctoral training included positions at the University of Waterloo (2011-2013) and the University of British Columbia (2013-2014) as a PIMS postdoctoral fellow. His research interests span Continuous Optimization , with current focus on convex relaxations and first-order methods for large-scale problems. He also investigates constraint qualifications for convex optimization, statistical computation, and robust optimization. His work bridges theoretical foundations with practical applications in areas such as compressed sensing, sensor network localization, and machine learning. Dr. Pong's publication record demonstrates consistent high-impact contributions to optimization theory. His recent work (2022-2025) shows a strong focus on convergence analysis, error bounds for conic optimization problems, and development of efficient algorithms for nonconvex optimization. His research often combines theoretical analysis with practical implementation, as evidenced by the availability of code for many of his publications. He serves as Associate Editor for Mathematics of Operations Research (since 2019) and on the editorial boards of Computational Optimization and Applications, Pacific Journal of Optimization, and Open Journal of Mathematical Optimization. Dr. Pong actively mentors students and postdocs, currently supervising PhD students Yanbo Wang and Hao Zhang, and postdoc Jiefeng Xu. His former students have secured positions at institutions including the University of Texas Arlington, University of Hong Kong, and Sun Yat-Sen University. His advising reflects his research expertise, with students working on topics in nonconvex optimization, compressed sensing, and matrix factorization. He maintains an active research schedule, regularly presenting at major optimization conferences including ICCOPT, ISMP, and SIAM Optimization. His upcoming talks in 2025 demonstrate continued research productivity in error bounds for log-determinant cones and single-loop proximal-conditional gradient methods.

Jacek GondzioView profile

Professor

• Large Scale Optimization
• Interior Point Methods
• Sparse Matrix Methods
• +3 more

Jacek Gondzio is a Professor in the School of Mathematics at the University of Edinburgh. He received his M.Eng. in Electronics (1983) and PhD in Automatic Control and Robotics (1989) from Warsaw University of Technology. His career includes positions at the Polish Academy of Sciences (1989–1993), University of Geneva (1993–1998), and the University of Edinburgh since 1998, where he progressed from Lecturer to Professor. Research Interests: Gondzio's work spans large-scale optimization techniques, including interior point methods, sparse matrix computations, parallel algorithms, and applications in finance and engineering. Key focus areas include: Development of efficient solvers (HOPDM, OOPS) for linear/quadratic/nonlinear programming Matrix-free methods and preconditioning for massive-scale problems Applications in quantum information, tomography, structural design, and financial planning Publication Trends: His recent articles emphasize scalable algorithms for optimization, including proximal methods for semidefinite programming, interior-point innovations, and applications in medical imaging and transport. Work frequently integrates regularization, decomposition techniques, and structure-exploiting linear algebra. Awards: EUROPT Fellow (2019) for contributions to continuous optimization Grants & Advising: Current projects include EPSRC-funded work on building structure optimization (EP/N019652/1), Google-funded LP solvers, and risk modeling with Standard Life Investments. He has supervised 16+ PhD students on topics ranging from interior point methods to machine learning optimization. Software includes HOPDM, PDCGM, and the parallel solver OOPS. Leadership: Organizes workshops on optimization (e.g., COA, Advances in Preconditioners series) and serves on editorial boards for Mathematical Programming Computation , Computational Optimization and Applications , and other leading journals.

• Numerical Linear Algebra
• Partial Differential Equations
• Integral Equations
• +7 more

Difeng Cai is an Assistant Professor in the Department of Mathematics at Southern Methodist University. His research focuses on developing efficient algorithms for numerical linear algebra, partial differential equations, and integral equations, with applications spanning physics, statistics, machine learning, and deep learning. University: Southern Methodist University Department: Mathematics Academic Rank: Assistant Professor Professor Cai's work addresses computational challenges in scientific computing and data science by leveraging mathematical properties to design scalable algorithms. His recent interests involve integrating deep learning techniques, such as generative models, with traditional numerical methods to solve complex scientific problems. His research trends highlight interdisciplinary applications of numerical linear algebra and machine learning in scientific computing. Publications appear in venues like Numerical Linear Algebra and Its Applications , Numerische Mathematik , and SIAM Journal on Scientific Computing , emphasizing algorithm design, error estimation, and kernel matrix approximation.

Yunkai ZhouView profile

Associate Professor

• Numerical Linear Algebra
• Scientific Computing
• Eigenvalue Problems
• +6 more

Yunkai Zhou is an Associate Professor at the Department of Mathematics , Southern Methodist University. His research spans Numerical Linear Algebra , Scientific Computing , and Electronic Structure Calculations with applications in Materials Science and Electrical Engineering . Education : Ph.D. in Computational Mathematics (2002) from Rice University , B.S./M.S. from Xi'an Jiaotong University . Research Focus : Developing polynomial filtered subspace methods for generalized eigenvalue problems, improving mixing schemes in self-consistent field calculations, and extending subspace techniques to time-dependent DFT. His work addresses challenges in dimensionality reduction , machine learning , and high-performance computing . Key Contributions include algorithms for solving large-scale eigenvalue problems in Density Functional Theory and Density Functional Tight Binding . His students include Zheng Wang (recipient of multiple SMU awards) and Iranga Nagasinghe. Contact : Office in Clements Hall 133, Southern Methodist University, Dallas, TX. Email: yzhou@smu.edu . Website: http://faculty.smu.edu/yzhou .

Giulia FerrandiView profile

Researcher

• Statistics
• Linear Algebra
• Optimization
• +3 more

Giulia Ferrandi is a Researcher and Guest Researcher at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). She completed her PhD under the supervision of Prof. Michiel Hochstenbach (TU/e) and Prof. Rosário Oliveira (IST, Lisbon) as part of the European BIGMATH project. Her research focuses on Statistics, Linear Algebra, and Optimization applied to statistical problems, with contributions to gradient methods, trace ratio problems, and Markov chain analysis. She holds a master’s degree from Università degli Studi di Milano (Italy), specializing in Probability and Statistics. Prior to her academic roles, she briefly taught in high school and worked in industry. Research Interests : Her work bridges numerical linear algebra and optimization, with emphasis on: Development and analysis of gradient methods (e.g., limited memory, harmonic frameworks) Applications of Rayleigh quotients in optimization and eigenvalue problems Robust multigroup classification via trace ratio techniques Statistical analysis of Markov chains for farmland transitions and non-stationary processes Publications : Her recent work demonstrates contributions to: Subspace methods for large-scale trace ratio problems Advancements in unconstrained optimization via limited-memory gradient approaches Integration of Rayleigh quotients into gradient-based algorithms Harmonic frameworks for optimizing stepsizes in numerical methods Collaborations & Grants : Her PhD was funded by the EU’s BIGMATH project. Collaborators include Prof. Michiel Hochstenbach (TU/e) and Prof. Nataša Krejić (University of Novi Sad). No explicit advising roles or grants are listed beyond her doctoral funding. Technical Expertise : Expertise in numerical analysis, statistical modeling, and linear algebra applications.

Joel A. TroppView profile

Professor

• Mathematics of Data Science
• Machine Learning
• Numerical Linear Algebra
• +3 more

Joel A. Tropp is the Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology (Caltech), within the Division of Engineering and Applied Science. His academic career includes roles as Assistant Professor (2007–2012), Professor (2012–2017), and Steele Family Professor (2017–present). He holds a Ph.D. in Computational Applied Mathematics from the University of Texas at Austin (2004). Tropp's research focuses on applied mathematics, machine learning, data science, numerical algorithms, and random matrix theory, with notable contributions to matching pursuit algorithms, randomized SVD methods, and matrix concentration inequalities. Education: Ph.D. in Computational & Applied Mathematics, University of Texas at Austin (2004) M.S. in Computational & Applied Mathematics, University of Texas at Austin (2001) B.S. in Mathematics and B.A. in Plan II Liberal Arts, University of Texas at Austin (1999) Research Interests: Tropp’s work bridges applied mathematics, computer science, and engineering, emphasizing rigorous, practical algorithms for linear algebra, numerical analysis, and optimization. He develops user-friendly tools for high-dimensional probability and matrix analysis, with applications in machine learning, signal processing, and data science. His recent focus includes randomized algorithms for large-scale matrix computations, kernel methods, and quantum computing. Articles Trends: His recent publications address scalable randomized algorithms for kernel matrices, eigenvalue problems, and matrix approximation. Themes include computational efficiency, theoretical guarantees, and applications in machine learning, quantum computing, and dynamical systems. Awards: 2025 Richard P. Feynman Prize for Excellence in Teaching 2024 IMS Fellow 2020 IEEE Fellow 2019 SIAM Fellow 2008 PECASE Award Advising & Grants: Tropp has advised numerous Ph.D. students and postdoctoral researchers in areas like randomized algorithms, optimization, and quantum computing. He leads grants from ONR, NSF, and Caltech’s Carver Mead Fund, focusing on large-scale kernel computations and matrix solvers. His mentorship extends to interdisciplinary collaborations in turbulence modeling and signal processing. Labs & Teams: He contributes to Caltech’s Center for Mathematics of Information (CMI) and Computational Mathematics + X (CMX) initiatives, fostering research in data science, optimization, and computational methods.

Laura GrigoriView profile

Professor

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +4 more

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry

Divya Sri PraturiView profile

Researcher

• Fluid Dynamics
• Climate Physics
• Turbulence
• +4 more

Divya Sri Praturi is a Scientist in the Climate Surface Interaction research group within the Department of Climate Physics at the Max Planck Institute for Meteorology (MPI-M) in Hamburg, Germany. She works as a postdoctoral researcher on the EU-funded NextGEMS project under the supervision of Prof. Dr. Bjorn Stevens, focusing on high-resolution climate modeling. Her primary research interests span multiple domains of fluid dynamics, including geophysical, astrophysical, and engineering flows. She specializes in turbulence theory, magnetohydrodynamics, compressible flows, and climate dynamics. Her current work investigates the breakdown mechanisms of the Intertropical Convergence Zone (ITCZ) using kilometer-scale coupled atmosphere-ocean-land simulations. Dr. Praturi's publication record demonstrates expertise across theoretical fluid dynamics and applied climate science. Her recent work shows a clear transition from fundamental turbulence research to climate modeling applications, with a focus on understanding how small-scale processes influence large-scale climate phenomena. She has made significant contributions to both compressible turbulence theory and high-resolution climate modeling. Alexander von Humboldt Postdoctoral Fellowship As a postdoctoral researcher, Dr. Praturi has secured funding through the prestigious Alexander von Humboldt Foundation and is currently contributing to the EU's NextGEMS project. Her research bridges theoretical fluid dynamics with practical climate modeling applications, working within collaborative international research frameworks. She is actively involved in the Climate Surface Interaction group at MPI-M, contributing to the institute's mission of advancing climate science through high-resolution modeling approaches. Her work connects fundamental fluid dynamics principles with pressing climate research questions, particularly regarding tropical climate dynamics and scale interactions in Earth system models.

José R. HerreroView profile

Associate Professor

• High Performance Computing
• Parallel Computing
• Dense Linear Algebra Algorithms
• +3 more

José R. Herrero is an Associate Professor in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). His primary research area is High Performance Computing (HPC), focusing on parallel algorithms, dense linear algebra, and multicore processor optimization. He has held administrative roles including Vice-Dean for Academic Studies (2010–2013) and Vice-Dean for Institutional and International Relations (2013–2015) at FIB. Education: PhD in Computer Science from UPC (2006), Postgraduate in Training of University Professors (1997), and a Computer Science degree (1993). Teaching experience includes over 15 courses in Computer Architecture, Operating Systems, and Parallel Programming at both undergraduate and graduate levels. Research interests span HPC, parallel computing, and algorithm design. His work emphasizes optimizing matrix computations and linear algebra libraries for modern architectures. Notable contributions include advancements in sparse Cholesky factorization, task-parallel algorithms, and energy-efficient computing. Recognition includes UPC's Teaching Mention for Outstanding Quality (2009, 2014, 2019) and a Certificate of Merit for contributions to scientific computing workshops. He advises numerous PhD students and collaborates internationally, including visiting roles at GeorgiaTech and École Polytechnique de Montréal.

Zhongyuan ChenView profile

Assistant Professor

• Biostatistics
• Cancer Genomics
• Bioinformatics
• +5 more

Zhongyuan Chen, PhD, is an Assistant Professor in the Division of Biostatistics at the Medical College of Wisconsin (MCW). She holds a PhD in Statistics from Rice University and previously served as a Visiting Assistant Professor at Purdue University's Department of Statistics. Her research focuses on integrating statistical methodologies with biosciences, cancer genomics, and computational biology, emphasizing applications in personalized medicine, high-dimensional data analysis, and large-scale scientific computing. Dr. Chen collaborates extensively with cancer researchers at institutions like MCW Cancer Center, MD Anderson Cancer Center, and the IU School of Medicine. Her key research interests include statistical machine learning for treatment recommendation, causal inference, and cancer genomic data analysis. She has developed novel strategies for analyzing somatic mutations and germline variations, as well as improving eigenvalue solution algorithms for numerical computations. Dr. Chen's work bridges statistical theory with practical biomedical applications, particularly in oncology and precision medicine. Notable publications highlight her contributions to understanding mitochondrial genomic variants in transplantation outcomes, Hippo-YAP pathway regulation in bone metastasis, and metabolic subtypes in cancers. She actively seeks motivated students and postdocs for collaborative research projects, emphasizing interdisciplinary approaches to biomedical challenges. Teaching responsibilities include courses in biostatistical computing and statistical quality control.

Christophe GeuzaineView profile

Professor

• Computational Electromagnetism
• Scientific Computing
• Finite Element Method
• +5 more

Prof. Christophe Geuzaine is a Full Professor in the Department of Electrical Engineering and Computer Science at the University of Liège , Belgium. He previously held academic positions at Case Western Reserve University (Assistant Professor of Mathematics) and California Institute of Technology (Postdoctoral Scholar in Applied and Computational Mathematics). Research Focus: Computational electromagnetism, biomedical/geophysical modeling, and open-source software development (Gmsh, GetDP) Teaching: Courses in electromagnetic energy conversion, scientific computing, and multiphysics projects Location: Montefiore Institute B28, Quartier Polytech 1, Liège, Belgium His research employs ONELAB tools for high-temperature superconductivity ( Life-HTS ), photonics ( ONELAB-Photonics ), and domain decomposition ( GetDDM ). Current work emphasizes large-scale time-harmonic wave simulations and quantum computing applications.

• Numerical Linear Algebra
• Randomized Algorithms
• Genomics
• +5 more

Petros Drineas is the Department Head and Professor of Computer Science at Purdue University's College of Engineering. His research focuses on randomized numerical linear algebra (RandNLA), algorithms for large-scale data analysis, computational genomics, and machine learning. He leads the ENIGMA-TS initiative for global neuroimaging genetics collaboration and has developed tools like TeraPCA for tera-scale genotype analysis. Drineas has held leadership roles in NSF and BSF-funded projects, including studies on Tourette syndrome, Alzheimer's disease, and algorithmic efficiency. His work bridges theoretical computer science with applications in genetics, neuroscience, and optimization. Key contributions include matrix sketching frameworks, randomized algorithms for PCA, and methods for GWAS and polygenic risk score analysis. His grants include funding for RandNLA applications in inverse problems, fast least squares solvers, and collaborative research on genetic variation. He has pioneered methods for handling massive datasets in genetics, such as the MaSk-LMM framework for linear mixed models and Patch2Self2 for self-supervised denoising.

Laurent Saloff-CosteView profile

Professor

• Analysis
• Probability Theory
• Stochastic Processes
• +7 more

Laurent Saloff-Coste is the Abram Rogers Bullis Professor of Mathematics at Cornell University , affiliated with the College of Arts and Sciences . His research focuses on analysis , probability theory , stochastic processes , and their interplay with Riemannian geometry and geometric group theory . He explores heat diffusion on manifolds, random walks on groups (both finite and infinite), and quantitative estimates for ergodic Markov chains. Education: Ph.D. in Mathematics (1983), Université Paris VI Doctorat d'État (1989), Université Paris VI Research Interests: His work bridges analysis and probability, studying properties of heat kernels, potential theory, and functional inequalities. He investigates geometric aspects of large-scale structures, such as Cayley graphs, and the relationship between group algebraic properties and random walk behavior. Key areas include: Heat kernel estimates on manifolds and graphs Isoperimetric profiles and their applications Mixing times of Markov chains Sub-elliptic diffusions on Lie groups Recent Trends in Publications (2023–2025): His recent work emphasizes geometric analysis of heat kernels, long-range random walks on nilpotent groups, and functional inequalities. He explores applications to stochastic processes on discrete and continuous spaces, including studies of Lévy processes on nilpotent groups and transient subgraphs. Notable themes include: Estimates for hitting times and Harnack inequalities Perturbation methods for Dirichlet eigenfunctions Uniform doubling properties in Lie group geometries Awards: 2022 Simons Fellow Advising and Grants: No specific advisees or grant details are listed in the provided materials. His research has been supported by collaborations with institutions like the Institute of Mathematics in Wrocław, Poland, and participation in events such as the Midwest Probability Colloquium. Labs/Teams: No dedicated lab or team is explicitly mentioned, though his work involves interdisciplinary collaborations in geometric analysis and probability.

Jia LiuView profile

Professor

• Computational Mathematics
• Numerical Linear Algebra
• Sparse Matrix Computations
• +9 more

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.