جست‌وجوی استاد و پژوهشگر

Weilin Liمشاهده پروفایل

استادیار

• Applied and Computational Harmonic Analysis
• Super-Resolution
• Signal Processing
• +۴ مورد دیگر

Weilin Li is an Assistant Professor in the Department of Mathematics at the City College of New York (CUNY). His research focuses on applied and computational harmonic analysis, with emphasis on super-resolution, quantization methods, and signal processing. He holds a PhD in Mathematics from the University of Maryland, College Park, and was previously a Courant Instructor at New York University. Education PhD in Mathematics, University of Maryland, College Park (201X) Courant Instructor, New York University, 201X-201X Research Interests Dr. Li's work bridges pure mathematics and applied sciences, with key areas including nonharmonic Fourier analysis, spectral super-resolution, and compressed sensing. His methods address challenges in signal reconstruction under quantization constraints and subspace estimation techniques. Recent Trends in Publications Recent work explores optimality of spectral estimation algorithms (e.g., Gradient-MUSIC), stability of Fourier matrices, and applications of scattering transforms in hyperspectral imaging. His research often intersects with machine learning, particularly in understanding approximation capabilities of neural networks under quantization. Awards & Grants Recipient of the 2021 Charles Chui Young Researcher Best Paper Award Funded by NSF, PSC-CUNY, and City College Foundation grants Academic Engagement Co-organizer of the One World MINDS Seminar, CUNY GC Harmonic Analysis and PDE Seminar, and CUNY analysis learning seminars. Active in mentoring graduate students, including Ben Tupper who joined the PhD program in 2025.

Vladimir Shpilrainمشاهده پروفایل

استاد

• Post-Quantum Cryptography
• Computational Complexity
• Group Theory
• +۴ مورد دیگر

Vladimir Shpilrain is a Professor in the Department of Mathematics at The City College of New York (CUNY). His primary research focuses on post-quantum cryptography, computational complexity, and algorithmic problems in group theory. He is a leading figure in applying algebraic methods to cryptographic protocols and has contributed to foundational work in non-commutative cryptography. His academic affiliations include the Algebraic Cryptography Center and collaborative projects at the intersection of mathematics and computer science. Shpilrain has authored over 100 publications, including two influential books on combinatorial and computational group theory. His work bridges pure mathematics (e.g., combinatorial group theory, affine algebraic geometry) with applied areas like secure communication and blockchain technology. He holds patents for cryptographic systems and has advised numerous students in interdisciplinary research areas. Shpilrain teaches advanced courses in linear algebra, probability theory, and cryptography. His research is supported by grants exploring post-quantum security, Cayley hash functions, and delegation protocols. Collaborations span global institutions, emphasizing both theoretical rigor and practical applications in information security.

Igor Shinkarمشاهده پروفایل

دانشیار

• Computational Complexity
• Property Testing
• Discrete Mathematics
• +۳ مورد دیگر

Igor Shinkar is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on theoretical computer science, discrete mathematics, and probability theory, with particular interests in computational complexity, property testing, and local-to-global phenomena in combinatorial objects. He holds a PhD in Mathematics and Computer Science from the Weizmann Institute of Science (2014), an MSc from the same institution (2009), and a BSc in Mathematics and Computer Science from Tel Aviv University (2005). Shinkar has taught numerous courses at SFU, including CMPT 125 (Introduction to Computing Science and Programming II), CMPT 225 (Data Structures and Programming), CMPT 405/705 (Design and Analysis of Algorithms), and CMPT 706 (Design and Analysis of Algorithms for Big Data). He has also co-taught courses at UC Berkeley and NYU, focusing on topics like coding theory and approximation algorithms. His research explores the interplay between theoretical computer science and mathematics, with contributions to areas such as randomized algorithms, approximation algorithms, and probabilistically checkable proofs. He actively seeks MSc and PhD students interested in theoretical computer science.

Michael Mahoneyمشاهده پروفایل

استاد

• Machine Learning
• Big Data
• Statistical Analysis
• +۵ مورد دیگر

Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.

Duan Chenمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Machine Learning Algorithms
• Partial Differential Equations
• +۲ مورد دیگر

Dr. Duan Chen is Associate Professor of Mathematics at UNC Charlotte with affiliate appointment in the School of Data Sciences. His research spans randomized linear algebra, PDEs, and computational biology. Research Focus: Develops efficient algorithms for large-scale problems in machine learning, electromagnetics, and biological systems. Recent publications address kernel matrix compression and gene expression deconvolution. Grant support includes NIH R01GM148971 (2022-2025) and NSF DMS 1619713 (2016-2019).

Spyridon Pougkakiotisمشاهده پروفایل

مدرس

• Continuous Optimization
• Stochastic Optimization
• Risk-Aware Decision-Making
• +۳ مورد دیگر

Spyridon Pougkakiotis is a Lecturer in Optimization at King’s College London’s Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He previously held positions as Lecturer in Mathematics at the University of Dundee (2023–2024) and as a postdoctoral research associate at Yale University (2022–2023). His research focuses on continuous optimization, stochastic optimization, and risk-aware decision-making, with applications in operational research, engineering, and data science. He has developed efficient algorithms for large-scale optimization problems and contributed to preconditioning techniques in interior point methods. Education: BSc in Informatics and Telecommunications, National and Kapodistrian University of Athens (2012–2016) MSc in Operational Research with Computational Optimisation, University of Edinburgh (2016–2017) PhD in Optimization, University of Edinburgh (2017–2022) His research interests emphasize nonlinear optimization (including non-smooth, stochastic, and black-box problems) and algorithm development. He has been recognized with the 2022 OR Society Doctoral Award and was a finalist for the 2024 Euro Doctoral Dissertation Award. Teaching includes undergraduate courses on numerical methods with Python and an MSc module on machine learning. He is involved in King’s Financial Mathematics group and organized events like the “Afternoon of Stochastic Optimisation” workshop in 2025. Awards: 2022 OR Society Doctoral Award (Most Distinguished Doctoral Research in Operational Research) 2024 Euro Doctoral Dissertation Award Finalist Current research projects include a 50/50 Haleon Industry Studentship (2025–2028), focusing on optimization applications in industry.

Nuno Miguel Britesمشاهده پروفایل

دانشیار

• Stochastic Differential Equations
• Fisheries Management
• Epidemiological Modeling
• +۳ مورد دیگر

Nuno Miguel Brites is an Associate Professor of Statistics and Actuarial Science at the Department of Mathematics, Instituto Superior de Economia e Gestão (ISEG), Universidade de Lisboa. He holds a PhD in Mathematics (Statistics) from the University of Évora. His research focuses on stochastic differential equations applied to fisheries management, mortality rate modeling, and epidemiological systems. He has led over 150 publications in peer-reviewed journals and supervised multiple master's and doctoral students. Brites is also an active member of the Centro de Matemática Aplicada à Previsão e Decisão Económicas (CEMAPRE) and has held visiting professorships at the University Aberta and Polytechnic Institute of Beja. Education includes a PhD in Mathematics (2017), MSc in Mathematics (2010), and BSc in Mathematics (2008) from Universities of Évora and Aveiro. His work integrates stochastic models with real-world applications in ecology, public health, and actuarial science. Recent articles explore optimal harvesting strategies under environmental stochasticity and comparative mortality rate analysis between France and Portugal. He coordinates advanced courses in stochastic processes, actuarial computation tools, and mathematical finance methodologies at ISEG. Brites' professional experience spans academic leadership roles including Management Council membership at CEMAPRE and evaluation roles at A3ES. He has developed pedagogic materials for PhD and master's programs in applied mathematics, economics, and finance. His research addresses pressing issues in sustainable resource management and stochastic modeling of complex systems. Current projects include advancing computational methods for stochastic differential equations in actuarial and ecological contexts.

Joel Friedmanمشاهده پروفایل

استاد

• Algebraic Graph Theory
• Combinatorics
• Computer Science Theory
• +۵ مورد دیگر

Joel Friedman is a Professor in the Department of Computer Science at the University of British Columbia (UBC), Faculty of Science. His research focuses on Algebraic Graph Theory, Combinatorics, and Theoretical Computer Science, with significant contributions to spectral graph theory, sheaf theory, and computational complexity. He has supervised numerous PhD and Master's students in these areas. Friedman teaches courses such as CPSC 421 (Introduction to the Theory of Computing) and CPSC 531F (Discrete Hodge Theory and Topological Data Analysis). His work bridges pure mathematics and computer science, including studies on Riemann-Roch theorems for graphs, eigenvalue conjectures, and applications of linear algebra in discrete structures. He has authored over 50 publications, with recent contributions on topological data analysis, graph expansions, and algorithmic methods. Awards and honors include the Izaak Walton Killam Memorial Faculty Research Fellowship. Friedman actively engages in graduate supervision, advising students on topics like sparsifier constructions and coded caching problems. His research is affiliated with the Institute of Applied Mathematics at UBC. He has held academic positions since the 1980s, with a consistent record of contributions to theoretical computer science and discrete mathematics.

Ehrhard Behrendsمشاهده پروفایل

استاد

• Functional Analysis
• Probability Theory
• Stochastic Processes
• +۴ مورد دیگر

Prof. Ehrhard Behrends is a Professor of Mathematics at the Department of Mathematics and Computer Science, Freie Universität Berlin. His research focuses on functional analysis, probability theory, and stochastic processes. He is also deeply involved in mathematical education and public outreach, having authored numerous popular mathematics books and articles. Key roles include leading the European Mathematical Society's Raising Public Awareness committee (2009–2015) and developing the mathematics portal mathematics-in-europe.eu . Behrends has organized major exhibitions like 'Mathematics for all the Senses' and contributed to initiatives such as the Year of Mathematics 2008. His work bridges advanced research with accessible communication, reflected in his books on Markov chains, analysis textbooks, and the Five Minutes of Mathematics column.

• Numerical Linear Algebra
• Randomized Algorithms
• Genomics
• +۵ مورد دیگر

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.

Tobias Jahnkeمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Partial Differential Equ
• +۳ مورد دیگر

Prof. Dr. Tobias Jahnke is a faculty member at the Karlsruhe Institute of Technology (KIT) in the Faculty of Mathematics , affiliated with the Institute for Applied and Numerical Mathematics and Research Group 3: Scientific Computing . His office is located at Kollegiengebäude Mathematik (20.30), Room 3.042 , and he offers individual appointments via email at tobias.jahnke@kit.edu . His research focuses on Numerical Analysis and Scientific Computing , particularly for highly oscillatory differential equations , partial differential equations , and stochastic reaction systems . Recent work includes methods for nonlinear Dirac equations , Maxwell equations in heterogeneous media , and dispersion-managed optical systems . Courses taught cover Mathematical Finance , Splitting Methods , and Wavelet Applications . Recent publications emphasize error bounds , high-frequency wave propagation , and ADI schemes . Collaborations include projects on frequency combs and quantum dynamics . He leads seminars and lectures on Scientific Computing , Numerical Methods , and Wavelet Analysis .

Yue Wuمشاهده پروفایل

مدرس

• Stochastic Analysis
• Numerical Analysis
• Stochastic Differential Equations
• +۸ مورد دیگر

Dr. Yue Wu is a Lecturer in the Department of Mathematics and Statistics at the Faculty of Science, University of Strathclyde. She is actively engaged in research and teaching, with a strong focus on stochastic and numerical analysis. She is affiliated with the Alan Turing Institute as a Visiting Researcher and collaborates internationally on advanced mathematical and data science projects. Research Interests: Numerical analysis for stochastic (partial) differential equations (SDEs/SPDEs) Random periodic solutions and their numerical approximation Rough path theory and signature methods Applications in machine learning, data science, and engineering systems Her recent work bridges pure stochastic analysis with practical applications in AI, autonomous systems, and industrial diagnostics. She employs advanced mathematical tools such as log-signatures and randomized numerical schemes to solve complex real-world problems. Publication Trends: Dr. Wu's recent publications (2022–2025) show a strong trend toward integrating stochastic numerics with machine learning. Her work spans theoretical convergence analysis of numerical schemes, feature extraction using rough paths, and PDE-informed deep learning. There is a clear interdisciplinary focus, connecting mathematics with engineering and computer science. Scientific Awards: Strathclyde & TU Braunschweig Joint Collaborative Funding Recipient (2023) ICIAM2023 Financial Support Scheme 2 Recipient (2023) Turing Network Development Award: Trailblazers Competition Recipient (2022) Lower Saxony – Scotland Tandem Fellowship Recipient (2022) Advising and Grants: Dr. Wu is accepting PhD students and offers projects in stochastic numerics and rough path applications. She has secured funding as Principal Investigator (e.g., International Exchanges Round 3) and Co-investigator (e.g., AI-based asteroid navigation project with ESA). Her grants reflect strong international collaboration and interdisciplinary innovation. Labs and Teams: While no specific lab name is mentioned, Dr. Wu is part of active research networks including the Alan Turing Institute and collaborates with teams in aerospace, data science, and applied mathematics. She organizes seminars and workshops, indicating leadership in her research community.

Lenore Blumمشاهده پروفایل

استاد

• Computational Complexity
• Real Number Algorithms
• Mathematical Logic
• +۵ مورد دیگر

Lenore Blum is a Professor at the Department of Computer Science within the School of Computer Science at Carnegie Mellon University . She has held significant roles such as Founding Director of Project Olympus and Co-Director of the NSF-ITR ALADDIN Center . Her career spans institutions including UC Berkeley, Mills College, ICSI, and MSRI. Research Interests : Blum's work bridges computational complexity and real computation, focusing on algorithms over continuous domains, polynomial equation solving, and transfer principles between discrete and continuous models. She also advocates for diversity in STEM through initiatives like Women@SCS and Expanding Your Horizons . Scientific Awards : Recognized with an NSF Career Advancement Award, AAAS Fellowship (1979), and an honorary Doctor of Laws from Mills College (1999). Grants & Outreach : Led the NSF-funded ALADDIN Center for algorithm adaptation and co-founded the Association for Women in Mathematics. Her outreach includes organizing symposia on mathematics' societal impact and advising the President's Diversity Advisory Council at CMU.

Peng Jiangمشاهده پروفایل

استادیار

• Machine Learning Systems
• High-Performance Computing
• Graph Processing & Mining
• +۵ مورد دیگر

Peng Jiang is an Assistant Professor in the Computer Science Department at the University of Iowa. His research focuses on machine learning systems, high-performance computing, and graph processing, with a particular emphasis on compiler and programming techniques for GPU acceleration. He earned his Ph.D. in Computer Science from The Ohio State University in 2019 under Dr. Gagan Agrawal. Education: Ph.D., The Ohio State University, 2019 His work spans sparse training, knowledge graph embedding, and subgraph matching, often leveraging fine-grained parameter management and GPU optimization. Key trends in his publications include compiler design for high-performance systems, parallel programming models, and performance-aware weight pruning for neural networks. Scientific Awards 2024 NSF CAREER Award Peng Jiang has collaborated extensively with researchers such as Lihan Hu, Yihua Wei, Shihui Song, and Gagan Agrawal. His contributions to sparse matrix multiplication, distributed learning communication optimization, and PIM architecture-aware frameworks highlight his expertise in bridging machine learning and systems research.

Marko Lindnerمشاهده پروفایل

استاد

• Functional Analysis
• Numerical Analysis
• Operator Theory
• +۸ مورد دیگر

Marko Lindner is a Professor at the Institute of Mathematics, Hamburg University of Technology (TUHH), where he holds the Chair of Applied Analysis. His research lies at the intersection of functional analysis and numerical analysis, particularly focusing on the spectral and Fredholm theory of infinite matrices and linear operators. He has led significant research projects funded by the European Union through Marie-Curie grants and has collaborated with institutions and industries including BAE Systems, UK Met Office, and Schlumberger Ltd. Hamburg University of Technology (TUHH), Institute of Mathematics Chair of Applied Analysis Email: lindner@tuhh.de Office: Room 3,094, Building E, Am Schwarzenberg-Campus 3, D-21073 Hamburg His research interests include operator theory, spectral theory, pseudospectra, finite section methods, discrete Schrödinger operators, and applications in mathematical physics and wave scattering. He investigates the stability and convergence of numerical approximation schemes for infinite-dimensional operators, with deep theoretical contributions to limit operator theory and collective compactness. The recent publications highlight a strong trend in spectral approximation of non-selfadjoint operators, particularly Schrödinger-type operators with periodic, aperiodic, and random potentials. His work combines rigorous functional analytic foundations with computational insights, often involving pseudospectra, condition number asymptotics, and subword-based approximation techniques. The integration of numerical methods with spectral theory is a consistent theme, especially in the context of wave propagation and scattering problems. Scientific awards include: Individual Marie-Curie Fellowship (EU, 2005–2007) Individual Marie-Curie Grant (EU, 2008–2011) Marko Lindner has supervised various research projects, particularly in the areas of wave scattering in unbounded domains and the spectral analysis of infinite matrices. His collaborations span applied mathematics, mathematical physics, and engineering, with funding from both public and private sectors. He is actively involved in the academic community, organizing events such as the Operator Theory Workshop and participating in SIAM & GAMM Chapter activities. He leads and contributes to research teams working on applied operator theory, with a focus on developing stable and efficient numerical methods for complex physical systems. His work bridges theoretical operator algebras and practical computational techniques.