جست‌وجوی استاد و پژوهشگر

• Data Science
• Statistical Learning
• Approximation Theory
• +۷ مورد دیگر

Dr Bertrand Gauthier is a Lecturer at the School of Mathematics, Cardiff University , and a member of the Statistics and Data Science Research Group. His work bridges mathematics and data science, focusing on sampling-based approximation, spectral methods, and computational strategies for large-scale machine learning and uncertainty quantification. Current research: Sampling strategies, kernel methods, sparse approximation Past affiliations: KU Leuven (2015-2016), CNRS - Université de Nice (2012-2014), Université de Saint-Étienne (2007-2011) Research Interests include: Statistical learning and approximation theory Kernel-based modeling and integral operator approximation Low-rank matrix techniques and particle-flow methods Publications highlight his work on Nyström sampling optimization, kernel embedding of measures, and spectral decomposition for IMSE-optimal designs, spanning journals like SIAM Journal on Mathematics of Data Science and Positivity . Collaborations include researchers such as Matthew Hutchings and Kirstin Strokorb. Scientific Awards : Fellow of The Higher Education Academy Supervision includes postgraduate students Harry Bond, Alexandra Zverovich, and past supervisees like Matthew Hutchings (2024) and Michela Corradini (2024). His teaching covers Multivariate Data Analysis and Computational Statistics at undergraduate and postgraduate levels. Labs/Teams : Statistics and Data Science Research Group, Cardiff University School of Mathematics.

Rob Eggermontمشاهده پروفایل

استادیار

• Algebraic Geometry
• Discrete Mathematics
• Polynomial Representations
• +۵ مورد دیگر

Rob Eggermont is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), specializing in the Discrete Algebra and Geometry research group. His work focuses on finiteness properties in infinite-dimensional algebraic settings with symmetry, connecting algebraic geometry, representation theory, and combinatorics. His research has significant applications in fields including phylogenetics, chemistry, and algebraic statistics. Education: MSc in Mathematics from Leiden University (2011), cum laude PhD from Eindhoven University of Technology (2015), cum laude Rob Eggermont's research centers on how algebraic structures with symmetry maintain finiteness properties even in infinite-dimensional settings. His work on topological noetherianity demonstrates that in systems with sufficient symmetry, problems involving millions of variables can be as tractable as those with only a few variables. This has profound implications for computational algebra and geometric modeling. His research group plays a leading role in algebraic graph theory, finite and incidence geometry, and discrete Lie theory. An analysis of his recent publications (2017-2025) reveals a consistent focus on polynomial representations, GL-varieties, and symmetric matrix structures. His work frequently explores how finiteness properties emerge in infinite-dimensional settings through symmetry constraints. Collaborations with Jan Draisma and other mathematicians form a significant portion of his output, indicating strong research networks in algebraic geometry and representation theory. Scientific Awards: Jong Talent Aanmoedigingsprijs (2007) - awarded to the best first-year mathematics student at Leiden University Veni grant 016.Veni.192.113 from the Netherlands Organization for Scientific Research (NWO) Dr. Eggermont has secured research funding through competitive grants and has participated in numerous international workshops and conferences, including the AIM workshop on 'Representation stability' (San Jose, 2016) and the BIRS workshop on 'Free Resolutions, Representations, and Asymptotic Algebra' (Banff, 2016). His research group actively contributes to the DIAMANT symposium and the Intercity Number Theory Seminar. Within the Discrete Algebra and Geometry group at TU/e, Eggermont collaborates with colleagues on advancing the theoretical foundations of algebraic structures with symmetry. His work has helped establish connections between representation theory, combinatorics, and practical applications in scientific computing.

Dmitriy (Tim) Kuniskyمشاهده پروفایل

استادیار

• Probability
• Statistics
• Mathematical Data Science
• +۷ مورد دیگر

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.

• High-energy-density batteries
• Solid-state electrolytes
• Electrochemical energy storage
• +۷ مورد دیگر

Deng Yonghong is a Professor at the Department of Materials Science and Engineering, Southern University of Science and Technology (SUSTech), and serves as Associate Dean of the School of Innovation and Entrepreneurship. She is a Category B Talent of the Shenzhen Peacock Program and leads the Guangdong Provincial Key Laboratory of Energy Materials for Electric Power. PhD from Tsinghua University Postdoctoral research at Lawrence Berkeley National Laboratory Former Professor at South China University of Technology Her research focuses on high-energy-density batteries , emphasizing solid-state electrolytes , polymer binders , and lithium metal anodes . Recent work explores nonflammable electrolytes , self-bonding nanocomposite anodes , and biomimetic binder designs . Publications span top journals like Nature Communications , Advanced Materials , and Energy & Environmental Science , with over 300 papers and 100 patents. Her research trends highlight dendrite suppression , flexible battery architectures , and low-temperature electrolyte solutions . China Excellent Patent Award Natural Science Award (Ministry of Education, Second Prize) Guangdong/Shenzhen Science and Technology Progress Awards (Second Prize) World's Top 2% Scientists (5 consecutive years) She holds editorial roles in Energy Storage Science and Technology and Battery Industry , and teaches courses on electrolyte fundamentals and materials testing . Her collaborative projects include NSFC grants and Guangdong Key R&D programs.

• Algebraic Geometry
• Combinatorics
• Cluster Algebras
• +۷ مورد دیگر

David Speyer is a Professor in the Department of Mathematics at the University of Michigan. He serves as Associate Chair for Education-Logistics and has been a key figure in curriculum development, particularly for Math 632 , Math 593 , and Math 665 . His research bridges algebraic geometry with combinatorial mathematics, focusing on areas such as cluster algebras , tropical geometry , and flag varieties . Education : Ph.D. in Mathematics from the University of California, Berkeley (2005); B.A. from Harvard University. Previous Positions : Clay Research Fellow (2005-2010), with appointments at the University of Michigan and MIT. Research Interests Speyer's work explores the intersection of algebraic geometry and combinatorics. His key contributions include studies on total positivity , shard theory in Coxeter groups , and combinatorial structures in algebraic varieties . Current projects involve cluster deep loci and 3D plabic graphs , connecting mirror symmetry to discrete mathematics. Scientific Contributions Awards : Clay Research Fellow (2005-2007). Publications : 15+ papers on algebraic geometry, combinatorial structures, and total positivity. Advising and Teaching Speyer has advised 9 graduate students and supervised 8 undergraduate research projects . He is involved in high school outreach through programs like PROMYS and Math Corps . He plans to take 2-4 new graduate students in the coming years.

Konrad Krakowiakمشاهده پروفایل

استادیار

• Cementitious Materials
• Materials Science
• Civil Infrastructure
• +۷ مورد دیگر

Konrad Krakowiak, Ph.D., is an Assistant Professor at the University of Houston's Department of Civil and Environmental Engineering and a Research Affiliate at MIT's CEE Department. He directs the SERC Civil and Environmental Engineering Facility at UH and previously managed MIT's Laboratory for Materials in Extreme Environments. His research focuses on advanced cementitious materials under extreme conditions. Ph.D. in Mechanics of Structures and Materials, University of Minho (Portugal) M.S. and B.E. in Applied Mechanics, Warsaw University of Technology (Poland) Research interests include: Chemo-mechanical analysis of cement-based materials High-temperature/high-pressure applications (e.g., geothermal wells) Multiscale modeling from nano to macro Novel characterization techniques (XRD, EPMA, indentation) Carbon-neutral cement matrices Radiation effects on calcium-silicate-hydrates Recent publications highlight trends in Thermal conductivity reduction via organic cross-linking Creep behavior in colloidal gels Interfacial chemistry in fly-ash composites Lightweight geopolymers for energy retrofit Scientific awards include Dwight David Eisenhower Transportation Fellowship (2019) MIT CEE Postdoctoral Excellence Award (2016) FCT Scholar (2011) Erasmus Scholar (2003) He has supervised Ph.D. students Amir Moshiri and Raj Gopal Nannopaneni, focusing on reduced-aging cement matrices and ultrasonic monitoring of microstructural changes. His $5M X-CEM Schlumberger-MIT collaboration (2011-2016) advanced understanding of cement durability under extreme conditions.

Michael Karowمشاهده پروفایل

پژوهشگر

• Numerical Linear Algebra
• Control Theory
• Matrix Analysis
• +۶ مورد دیگر

Michael Karow is a Researcher at the Institute of Mathematics , Technical University of Berlin , affiliated with the Faculty II - Mathematics and Natural Sciences . His research focuses on Numerical Linear Algebra , Control Theory , and Matrix Perturbation , with specific interests in Pseudospectra , Stability Radii , and Structured Eigenvalue Problems . Educational background includes a Ph.D. (2003) from the University of Bremen, titled " Geometry of Spectral Value Sets ," and a Habilitation (2009) at TU Berlin. His work spans collaborations with institutions like IIT Guwahati, EPFL Lausanne, and SUNY Utica. His research involves stabilizing discrete-time linear systems via matrix factorizations, structured matrix pencils with no spillover effects, and backward error analysis for palindromic polynomials. Publications highlight applications in finite element model updating , eigenvalue sensitivity , and geometric matrix theory . Teaching includes courses like Numerical Mathematics 1 for Engineering , Differential Equations and Numerics , and Calculus of Variations . He has led workshops in India, Italy, and the U.S., focusing on numerical methods and control theory.

Rongrong Wangمشاهده پروفایل

دانشیار

• Applied and Computational Harmonic Analysis
• Machine Learning
• Neural network training and generalization
• +۶ مورد دیگر

Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.

Arthur GOETSCHYمشاهده پروفایل

مدرس

• Wave Propagation in Complex Media
• Random Lasers
• Quantum Information in Disordered Systems
• +۴ مورد دیگر

Arthur GOETSCHY is a Lecturer at ESPCI Paris and an active researcher at the Langevin Institute, focusing on wave propagation phenomena in complex and disordered media. His work bridges theoretical physics with practical applications in optics, quantum information, and biomedical imaging. He maintains an office at Room R52 with contact number 01 80 96 39 46. GOETSCHY's research centers on coherent control of wave propagation in disordered environments, with particular emphasis on optimization of transmission, absorption, and focusing. His scientific activities span microscopic theory of random lasers , quantum information propagation in complex media, photon-phonon dynamics in optomechanical networks, light propagation in cold atomic gases , and advanced random matrix theory applications. His work demonstrates how wave interference effects can be harnessed to control light in scattering environments. Analysis of his publication record from 2017-2025 reveals a consistent focus on wave control in disordered systems, with increasing sophistication in manipulating light for practical applications. His recent work (2023-2025) shows strong emphasis on broadband light delivery, multi-region control, and quantum aspects of wave propagation. A notable trend is the progression from fundamental theoretical understanding toward applications in biomedical imaging and telecommunications. At the Langevin Institute, GOETSCHY contributes to research on wave phenomena in complex media, collaborating with physicists working on multiple scattering, quantum optics, and nanophotonics. His theoretical work complements experimental efforts at the institute, particularly in wavefront shaping techniques and characterization of light propagation through turbid media.

• High-Performance Computing
• Matrix Factorization
• Multicore Processing
• +۷ مورد دیگر

Dr. hab. Beata Bylina is a Professor at the Faculty of Mathematics, Physics and Computer Science , Maria Curie-Skłodowska University (UMCS) , affiliated with the Department of Information Systems Software . She specializes in high-performance computing, numerical methods, and energy-efficient parallel programming. ORCID ID : 0000-0002-1327-9747 Contact : beata.bylina@umcs.pl / beata.bylina@mail.umcs.pl Office : Room D-522 (Institute of Computer Science) or Room B-2, Akademicka Street 9, Lublin Consultations : Wednesdays 10:00–12:00 (in-person or remote via Microsoft Teams) Her research focuses on: Parallelization and vectorization techniques for multicore architectures Energy consumption optimization in numerical algorithms Matrix factorization methods (WZ, LU, QR) for CPU/GPU hybrid systems Markov chain modeling for network performance analysis Compiler optimization impact on performance and energy metrics Recent publication trends show emphasis on: Time-energy correlations in multithreaded algorithms OpenMP/OpenACC for hybrid CPU-GPU implementations Efficient sparse matrix storage schemes for GPUs Impact of hardware-specific optimizations (Xeon Phi, frequency scaling) Comparative studies of parallelization strategies

Matthias Grossglauserمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

Aljaž Zalarمشاهده پروفایل

استادیار

• Real Algebraic Geometry
• Truncated Moment Problems
• Matrix Polynomials
• +۳ مورد دیگر

Assistant Professor Aljaž Zalar is affiliated with the University of Ljubljana at the Faculty for Computer and Information Science . His research focuses on Real Algebraic Geometry , Truncated Moment Problems , and Matrix Polynomials , with applications in Operator Theory and Positive Linear Maps . PhD in Mathematics, University of Ljubljana (2017) MSc and BSc in Mathematics, University of Ljubljana (2013, 2011) Zalar's work bridges theoretical mathematics and computational applications, including copositive matrices , positive semidefinite matrix completions , and noncommutative polynomial positivity . His recent projects address truncated moment problems on curves and algebraic structures in optimization . His publications from 2016–2025 span journals like Linear Algebra and its Applications , SIAM Journal on Applied Algebra and Geometry , and Integrable Equations and Operator Theory , emphasizing polynomial operator analysis and matrix inequalities . Zalar supervises postdoctoral and graduate students, including PhD candidate Rajkamal Nailwal and Igor Zobovič, and mentors undergraduate researchers. He leads the ARIS grant project J1-60011 on real algebraic geometry approaches to moment problems.

• Operator theory
• Local spectral theory
• Fredholm theory
• +۴ مورد دیگر

Professor Ji Eun Lee is an Associate Professor in the Department of Mathematics and Statistics at Sejong University, where she has been serving since March 2023. Previously, she was an Assistant Professor at the same institution from March 2017 to February 2023, and a Visiting Professor from March 2014 to February 2017. Her research focuses on operator theory, particularly complex symmetric operators, Toeplitz operators, and their applications in function spaces. Professor Lee received her academic degrees from prestigious Korean institutions: Ph.D. in Mathematics (Functional Analysis), Ewha Womans University, 2008 M.S. in Mathematics (Analysis), Ewha Womans University, 2003 B.S. in Mathematics, Gyeongsang National University, 2001 Her primary research interests lie in operator theory, with specific focus on complex symmetric operators, Toeplitz operators, local spectral theory, and function space theory. Professor Lee has made significant contributions to the field, particularly in the characterization of complex symmetric Toeplitz operators on the Hardy space and the development of local spectral theory of complex symmetric operators. Her work bridges abstract operator theory with concrete applications in function spaces. The research trends in her recent publications show a deepening focus on Newton spaces, m-expansive operators, and the interplay between complex symmetry and operator matrix structures. Professor Lee has received several prestigious awards for her academic excellence: 2022 Research Excellence Professor Award (August 2022) 2017 Teaching Excellence Award (February 2018) Professor Lee actively advises undergraduate and graduate students, with a track record of mentoring students who have pursued advanced degrees and successful careers in mathematics, statistics, and related fields. Her research has been supported by multiple grants from the National Research Foundation of Korea, including the Leap Forward Project in 2022, which focuses on Toeplitz operators in Newtonian space. She has led numerous research projects investigating complex symmetry operators and their invariant subspaces. She leads the Operator Theory Laboratory at Sejong University, which focuses on advancing research in operator theory, particularly in the areas of complex symmetric operators, Toeplitz operators, and their applications in various function spaces. The laboratory provides a vibrant research environment for students and collaborators interested in functional analysis and operator theory.

• Combinatorial Optimization
• Polynomially Solvable Special Cases
• Assignment Problems
• +۶ مورد دیگر

Eranda Dragoti-Cela is an Associate Professor at the Department of Optimization and Discrete Mathematics, Graz University of Technology. She specializes in combinatorial optimization, focusing on polynomially solvable special cases of NP-hard problems like the Quadratic Assignment Problem (QAP), portfolio optimization, and algorithmic graph theory. Education: M.Sc. in Applied Mathematics, University of Tirana (1992) Ph.D. in Technical Sciences, Graz University of Technology (1995) Habilitation in Applied Mathematics (2001) Her research spans combinatorial optimization (QAP, assignment problems, location problems), portfolio optimization (risk-return models, ordinal information integration), and graph theory (edge intersection graphs, network flows). Recent work includes linearization of quadratic shortest path problems and data arrangement on trees. She supervises bachelor's and master's theses in discrete mathematics and teaches courses like Combinatorial Optimization 1 and Risk Theory and Management , emphasizing algorithmic rigor and practical applications in finance and engineering.

Bin Gaoمشاهده پروفایل

دانشیار

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +۵ مورد دیگر

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.