جست‌وجوی استاد و پژوهشگر

Zeyu Dingمشاهده پروفایل

استادیار

• Privacy
• Algorithmic Fairness
• Machine Learning
• +۵ مورد دیگر

Zeyu Ding is an Assistant Professor in the School of Computing at Binghamton University, with a courtesy appointment in the Department of Mathematics and Statistics. He holds two PhDs: one in Computer Science from Penn State University and another in Mathematics from Binghamton University, along with a BS in Mathematics from Zhejiang University. Research Interests His work focuses on the intersection of privacy, security, machine learning, and algorithmic fairness. He investigates how to protect sensitive personal information through differential privacy mechanisms, formal verification, numerical optimization, and privacy-preserving statistical inference. Article Trends Ding's publications highlight advancements in differential privacy, including the Report Noisy Max with Gap Mechanism and the Permute-and-Flip approach. His research also addresses security challenges like reconstruction attacks and automated verification tools (e.g., Checkdp and DPGen), alongside mathematical explorations of automorphism group schemes and Barsotti-Tate groups. Scientific Awards CCS Outstanding Paper Award, 2018 Caper Bowden PET Award Runner-up, 2019 CCS Best Paper Award Runner-up, 2020 CCS Best Paper Award Runner-up, 2021 Research Award from Penn State University, 2019 Teaching Award from Penn State University, 2021 His research is supported by the NSF grant 2317233, underscoring his contributions to privacy-preserving computational methods.

Meiqin Liمشاهده پروفایل

دانشیار

• STEM Education
• Mathematics Education
• Engineering Education
• +۵ مورد دیگر

Dr. Meiqin Li is an Associate Professor of Applied Mathematics at the University of Virginia (UVA) and a Faculty Fellow at the UVA Center for Teaching Excellence. She joined UVA in 2017 after earning her Ph.D. in Applied Mathematics from Texas A&M University. Her research focuses on STEM education innovation, particularly integrating technology into classrooms, fostering inclusive learning environments, and optimizing curricula for engineering students. She also explores numerical computation, optimization, and nonlinear analysis in applied mathematics. Dr. Li’s educational background includes a Ph.D. from Texas A&M University, College Station, TX (2017). Her teaching portfolio spans courses like APMA 3080 (Linear Algebra) and APMA 2501 (Programming in R & MATLAB), reflecting her commitment to computational and pedagogical excellence. Her research interests emphasize active learning strategies, curriculum redesign for equity, and leveraging MATLAB/Autograder tools in education. Recent publications highlight outcomes from integrating precalculus into calculus courses and enhancing linear algebra instruction through technology. Awards: 2025 ASEE Mathematics Division Best Paper Award 2024 SoTL Dissemination Award (UVA Center for Teaching Excellence) 2020 Thomas E. Hutchinson Award Finalist Dr. Li’s work bridges pedagogical innovation and applied mathematics, with grants and recognitions underscoring her impact in teaching and research. She actively contributes to UVA’s engineering education community and promotes student success through inclusive practices.

• Real Algebraic Geometry
• Combinatorics
• Convex Optimization
• +۱۰ مورد دیگر

Cynthia Vinzant is an Associate Professor in the Department of Mathematics at the University of Washington, College of Arts and Sciences. Her research lies at the intersection of real algebraic geometry, combinatorics, and convex optimization, with a focus on polynomials, determinants, and matroids. Ph.D., Mathematics, UC Berkeley, 2011 B.A., Mathematics and Neuroscience, Oberlin College, 2007 Her research interests include real algebraic geometry, combinatorics, convex optimization, tropical geometry, and spectrahedra. She studies the algebraic and combinatorial structures underlying optimization problems and geometric objects, particularly through the lens of hyperbolic and log-concave polynomials. Her recent publications span topics such as tropicalization of principal minors, determinantal representations, Fourier quasicrystals, and log-concave polynomials. These works demonstrate strong interdisciplinary connections across algebraic geometry, combinatorics, optimization, and mathematical physics. Sloan Research Fellowship (2020) Best Paper Award, STOC (2019) von Neumann Fellowship, IAS (2020–2021) Bernard Friedman Prize, UC Berkeley (2011) Rebecca Cary Orr Prize, Oberlin College (2007) She has advised several Ph.D. students including Tracy Chin, Jonathan Niño-Cortes, Joseph Rogge, Faye Pasley Simon, Michael Ruddy, Georgy Scholten, and Abeer Al Ahmadieh. She has received significant NSF funding, including a CAREER award (2020–2025) on determinantal, hyperbolic, and log-concave polynomials. She has taught courses in tropical geometry, convex algebraic geometry, and optimization at both the University of Washington and North Carolina State University.

Murphy Yuezhen Niuمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Error Correction
• Machine Learning
• +۳ مورد دیگر

Murphy Yuezhen Niu is an Assistant Professor and Stansbury Chair in Computer Science at the University of California, Santa Barbara (UCSB), since 2024. She holds an adjunct role as Adjunct Assistant Professor at the University of Maryland, College Park, and the University of Maryland Institute for Advanced Computer Studies. Niu earned her Ph.D. in theoretical and mathematical physics from MIT (2018) and a B.A. in Physics from Peking University. Her research focuses on quantum computing paradigms, including quantum control optimization, quantum error correction, quantum machine learning, and scalable quantum architectures. Her work applies deep reinforcement learning and generative models to quantum systems, with contributions to superconducting qubit-based processors, ion traps, photonic systems, and neutral atom qubits. Niu's research emphasizes reducing the computational cost of quantum digitization while achieving real-world impacts. Notable achievements include the Claude E. Shannon Research Assistantship for her work in photonic quantum computation and quantum cryptography. Niu’s publications (2021–2025) span topics like quantum error correction thresholds, hybrid analog-digital quantum simulators, and machine learning-driven quantum decoding. Her research bridges theoretical physics and applied quantum computing, with a focus on fault-tolerant architectures and scalable quantum control protocols. Education: Ph.D., Physics, MIT (2018) B.A., Physics, Peking University Awards: Claude E. Shannon Research Assistantship Labs/Teams: Google Quantum AI Team (former role) UCSB Quantum Computing Research Group

Margaret Wrightمشاهده پروفایل

استاد

• Optimization
• Linear Algebra
• Numerical Computing
• +۳ مورد دیگر

Margaret H. Wright is a distinguished academic and researcher in optimization, numerical computing, and scientific applications. She holds a professorship in Computer Science and Mathematics at New York University's Courant Institute, where she also served as Chair of the Computer Science Department. Her career includes roles at Stanford University and Bell Laboratories, where she contributed to foundational work in optimization algorithms and numerical methods. Education: B.S. and M.S. in Mathematics and Computer Science from Stanford University (1966–1976), followed by a Ph.D. in Computer Science (1976). Her research focuses on optimization techniques, linear algebra, and their applications in engineering and science. Key contributions include seminal work on the Nelder-Mead simplex method, interior-point methods, and numerical linear algebra. She authored influential books like Practical Optimization and contributed to over 40 publications. Her articles span optimization theory, computational methods, and interdisciplinary applications. Awards and Recognition: Elected to the National Academy of Engineering (1997), American Academy of Arts and Sciences (2001), and National Academy of Sciences (2005). Honored with the AWM Noether Lecture (2000), SIAM's Distinguished Service Award (2001), and AMS's Public Service Award (2002) for her advocacy in STEM education and diversity. Leadership: Served as President of the Society for Industrial and Applied Mathematics (1995–1996). Active in editorial roles for journals and in promoting women and minorities in mathematics and computing.

• Algebraic Topology
• Category Theory
• Lie Groups
• +۳ مورد دیگر

Halvard Fausk is an Associate Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Computer Science, specifically the Mathematical Modelling group. His research focuses on algebraic topology, category theory, and homotopy theory. His work includes foundational studies on equivariant homotopy theory for pro-spectra and pro-finite groups, induction theorems for compact Lie groups, and explorations of adjoint isomorphisms. Publications span journals like Geometry and Topology , Transactions of the American Mathematical Society , and Homology, Homotopy and Applications . He has co-authored key textbooks such as Lineær Algebra (2024) and Mattenøkkelen (2014), targeting undergraduate education. His recent research trends emphasize algebraic structures in topology, while historical work includes contributions to model categories and topological group theory.

Mikel Sanzمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Quantum Algorithms
• Quantum Technologies
• +۵ مورد دیگر

Mikel Sanz is a Ramón y Cajal Researcher and Ikerbasque Fellow at the University of the Basque Country (UPV/EHU) in Bilbao, Spain. His research focuses on quantum computing, quantum algorithms, quantum technologies, and quantum metrology. His research interests include: Quantum Computing and Quantum Algorithms Quantum Metrology and Quantum Sensing Digital-Analog Quantum Computing Quantum Machine Learning Quantum Simulation Quantum Error Correction and Mitigation Dr. Sanz's recent publications demonstrate a strong focus on practical applications of quantum computing across various domains. His work spans quantum hardware design, quantum algorithm development, quantum machine learning applications, and quantum metrology techniques. He has made significant contributions to digital-analog quantum computing approaches, quantum kernel methods, and quantum-enhanced sensing technologies. His scientific awards include being selected as a Ramón y Cajal Researcher, a prestigious research position in Spain for experienced researchers, and an Ikerbasque Fellow, which is awarded by the Basque Foundation for Science to attract top researchers to the Basque Country. Dr. Sanz has collaborated extensively with researchers across multiple institutions, contributing to a wide range of quantum information science projects. His work often bridges theoretical quantum information concepts with practical implementations, particularly in superconducting quantum computing platforms. He is actively involved in advancing quantum technologies through his research group at UPV/EHU, focusing on developing novel quantum algorithms and exploring applications of quantum computing in various scientific and industrial domains.

Ming Guمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Scientific Computing
• Matrix Computations
• +۳ مورد دیگر

Ming Gu is a Professor in the Department of Mathematics at the University of California, Berkeley . He specializes in Numerical Linear Algebra and Scientific Computing , with a focus on developing efficient algorithms for structured matrices and large-scale data analysis. Organized Matrix Computations and Scientific Computing Seminars (2009-2017) Published 15+ papers on QR algorithms , Toeplitz matrices , randomized algorithms , and low-rank approximations His research addresses rank-revealing factorizations , randomized subspace iteration , and preconditioning techniques , often bridging numerical analysis with applications in machine learning and optimization . Students advised by him (e.g., Jiaming Wang, Onyebuchi Ekenta) have explored spectrum-revealing CUR decomposition and truncated SVD . Contact: mgu@math.berkeley.edu Office: 861 Evans Hall, UC Berkeley

Pierre Duchesneمشاهده پروفایل

استاد

• Time Series Analysis
• Sampling Theory
• Multivariate Analysis
• +۲ مورد دیگر

Pierre Duchesne is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal . He serves as Professor-responsibility for the M.Sc. and Ph.D. in Statistics programs (2000-2004). His research focuses on applied statistics with emphasis on: Time Series Analysis (univariate and multivariate models, serial correlation testing, portmanteau statistics) Sampling Theory (robust estimation methods, calibration estimators) Multivariate Analysis (ARCH effects, vector autoregressive models, causality testing) Applications in Econometrics and Financial Econometrics His work combines theoretical development with practical implementation through: Wavelet-based diagnostic methods Simulation studies for model validation Software development (S-PLUS/SAS) for statistical analysis Collaboration with organizations like Statistics Canada and Canadian Journal of Statistics He has served as Associate Editor for journals including Computational Statistics & Data Analysis (CSDA) and Canadian Journal of Statistics (CJS/RCS) .

R.K. Shyamasundarمشاهده پروفایل

استاد

• Real-Time Programming
• Reactive Programming
• Logic Programming
• +۷ مورد دیگر

R.K. Shyamasundar is a Professor at the Indian Institute of Technology Bombay , with a focus on Real-Time and Reactive Programming, Logic Programming, Pi-Calculus, and Parallel Programs. Research spans formal verification, concurrency, and distributed systems. Key contributions include RT-CDL semantics, Esterel language extensions, and hybrid system controller synthesis. Scientific awards include JC Bose National Fellow, Fellowships at Indian Academy of Sciences and Indian National Science Academy, and Senior Membership in IEEE. His work involves collaborations with institutions like TCS Group and researchers such as Basant Rajan, N. Raja, and Deepak Kapur.

Matthew Collinsonمشاهده پروفایل

مدرس ارشد

• Logic
• Theoretical Computer Science
• Information Security
• +۱۲ مورد دیگر

Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.

• Theoretical Computer Science
• Mathematics
• Spectral Graph Theory
• +۵ مورد دیگر

Daniel Alan Spielman is the Sterling Professor of Computer Science at Yale University , where he has been affiliated since 2006. He serves as Chair of the Department of Statistics and Data Science and is Co-Director of the Yale Institute for Network Science. His research spans theoretical computer science and mathematics, focusing on algorithms, spectral graph theory, and numerical linear algebra. Educational background: Bachelor of Arts in Mathematics and Computer Science from Yale University (1992) PhD in Applied Mathematics from MIT (1995) Research interests include smoothed analysis of algorithms , graph-based codes , spectral graph theory , and numerical linear algebra . His work on the Kadison–Singer problem and graph theory applications has been groundbreaking. Scientific awards and honors include: Gödel Prize (2008, 2015) Nevanlinna Prize (2010) MacArthur Fellowship (2012) Pólya Prize (2014) Breakthrough Prize in Mathematics (2022) Election to the National Academy of Sciences (2017) His publications highlight advancements in network science , operator theory , and algebraic combinatorics , with a focus on algorithmic solutions and mathematical proofs.

• Computational Thinking Education
• Informatics Education
• Educational Technology
• +۳ مورد دیگر

Prof. Dr. Valentina Dagienė serves as a Professor and Senior Researcher at the Educational Systems Group within the Institute of Data Science and Digital Technologies at Vilnius University, Lithuania. Her academic career spans several decades with significant contributions to informatics education globally, particularly through her leadership in the international Bebras contest initiative. Her research focuses on computational thinking education through constructionist learning approaches, with particular emphasis on task design that promotes deep conceptual understanding in K-12 settings. Prof. Dagienė has pioneered methods for integrating computational thinking into primary education curricula while addressing cultural differences in learning approaches. Her work bridges theoretical frameworks with practical classroom implementations, making complex informatics concepts accessible to young learners through engaging short tasks. Analysis of her recent publications reveals a clear progression toward interdisciplinary integration of computational thinking with STEAM education and digital competence frameworks. Her research increasingly addresses assessment methodologies for computational thinking skills and explores the connections between computational and algebraic thinking. The Bebras contest serves as both a research platform and practical implementation vehicle for her educational theories. Prof. Dagienė has established herself as a key figure in international informatics education through her editorial work, conference organization, and cross-national collaborations. She has fostered partnerships between educators and researchers across Europe and beyond, creating sustainable communities around computational thinking education. Her leadership in the Bebras International Contest has engaged millions of students worldwide in computational problem-solving activities. Through her work with the Educational Systems Group, Prof. Dagienė has developed comprehensive teacher training programs that support educators in implementing computational thinking concepts in diverse classroom settings. Her research on student approaches to problem-solving has informed the design of learning environments that accommodate different learning styles and cultural backgrounds.

• complex differential geometry
• Monge-Ampère type equations
• infinite dimensional geometries
• +۳ مورد دیگر

Tamás Darvas is a Professor in the Department of Mathematics at the University of Maryland, College Park. He maintains an active research program in complex differential geometry and serves as a key organizer of the Informal Geometric Analysis Seminar, which provides a forum for researchers and graduate students interested in geometric analysis across multiple academic years from 2015 through 2024. Professor Darvas's research focuses on complex differential geometry, Monge-Ampère type equations, and infinite dimensional geometries. His work explores the deep connections between geometric analysis, pluripotential theory, and complex Monge-Ampère equations. He has made significant contributions to understanding the metric geometry of spaces of Kähler metrics, particularly in big cohomology classes, developing important results related to twisted Kähler-Einstein metrics and the structure of singularity types. His research often bridges abstract mathematical theory with concrete geometric problems. Analysis of Darvas's publication record reveals a consistent focus on the interplay between complex geometry, pluripotential theory, and metric geometry. His most recent works examine lines in Kähler metric spaces, transcendental approaches to non-Archimedean metrics, and the Hausdorff distance on toric singularity types. A notable trend in his research is the extension of classical results from Kähler geometry to more general settings, particularly when working with big cohomology classes rather than just Kähler classes, as demonstrated in his significant 2024 paper establishing a uniform Yau-Tian-Donaldson existence theorem for Kähler-Einstein metrics. Professor Darvas is actively engaged with the mathematical community, co-organizing the thematic semester "Analysis and geometry on complex manifolds" in Budapest for August-December 2025. While specific grant information isn't provided in the available texts, his extensive publication record across top journals indicates ongoing research support. Through the Informal Geometric Analysis Seminar he helps organize, he provides mentorship opportunities for graduate students interested in geometric analysis, creating an environment where students of all levels are encouraged to attend and participate in discussions.

Cheng Huangمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Aerospace Propulsion
• Data-driven Modeling
• +۳ مورد دیگر

Cheng Huang is an Assistant Professor in the Department of Aerospace Engineering at the University of Kansas. His research focuses on computational fluid dynamics, aerospace propulsion, turbulent combustion modeling, and reduced-order modeling techniques. He is affiliated with the Computational AeroPropulsion Laboratory and can be contacted at chenghuang@ku.edu. Education: B.S. from Shanghai Jiaotong University M.S. and Ph.D. from Purdue University Research Interests: LES Modeling of Turbulent Reacting Flows Data-Driven and Reduced-Order Modeling of Complex Fluid Flows Combustion Instability Analysis in Aerospace Propulsion Recent Work Trends: His publications emphasize reduced-order modeling techniques for rocket combustion dynamics, rotating detonation engines, and multiscale fluid systems. Key methodologies include projection-based models, data-driven approaches, and nonlinear approximations of latent dynamics.