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Daniel FruminView profile

Assistant Professor

• Logic
• Type Theory
• Program Verification
• +5 more

Daniel Frumin is an Assistant Professor in the Department of Fundamental Computing Science at the University of Groningen, affiliated with the Bernoulli Institute. His research focuses on Logic, Type Theory, and Program Verification, with a particular emphasis on formal methods for concurrency, type systems, and homotopy type theory. He has contributed to foundational work in denotational semantics, modular programming language design, and mechanized verification of concurrent systems. His expertise includes the application of logical frameworks to concurrency models, such as ReLoC (Relational Logic for Concurrent Programming) and the integration of type theories with operational semantics. Recent work explores guarded interaction trees, interval domains in homotopy type theory, and compositional security properties for fine-grained systems. Frumin has published extensively in top-tier venues like ESOP, CONCUR, and ACM POPL, with peer-reviewed contributions on topics ranging from bunched implications in session-based concurrency to formal verification of data structures like concurrent queues. His research often bridges theoretical computer science and practical formal verification, leveraging tools like Coq for mechanized proofs. Collaborations include projects with institutions such as Aarhus University (Denmark) and the University of Bologna, focusing on univalent foundations, categorical semantics, and security verification. His work is supported by grants from the Dutch Research Council (NWO) and industry partnerships like Meta's Folly Library verification efforts. Labs/Teams: Active contributor to the Bernoulli Institute's Formal Methods Group and the Univalent Foundations initiative. His research group specializes in applying type-theoretic and categorical methods to concurrency and verification challenges.

Hsiao-Dong ChiangView profile

Professor

• Nonlinear System Theory
• Power System Stability and Control
• Optimal Power Flow
• +12 more

Hsiao-Dong Chiang is a Professor in the School of Electrical and Computer Engineering at Cornell University. He holds a Ph.D. in Electrical Engineering from the University of California, Berkeley, and has made significant contributions to nonlinear system theory and power system stability. His research spans theoretical development and practical applications in electric power systems, nonlinear optimization, and machine learning. B.S., Electrical Engineering, National Taiwan University, 1979 M.S., Electrical Engineering, National Taiwan University, 1981 Ph.D., Electrical Engineering, University of California, Berkeley, 1986 Chiang's research interests focus on nonlinear system theory , power system stability and control , nonlinear optimization , and their applications to modern power grids with high penetration of inverter-based resources. He is renowned for developing the BCU method and TRUST-TECH methodology , which have enabled fast direct stability assessment and global optimization in complex systems. His work bridges fundamental theory with industrial deployment through his companies, Bigwood Systems, Inc. and Global Optimal Technology, Inc. His recent publications (2024–2025) reflect a strong trend toward integrating machine learning and deep neural networks with power system analysis , particularly in state estimation, optimal power flow, and voltage control. There is a clear emphasis on handling uncertainty, non-convexity, and multi-scale dynamics in active distribution networks and integrated energy systems . His work increasingly focuses on resilience , real-time control , and user-centered methodologies for modern grid operations. Chiang has received numerous scientific honors, including: IEEE Fellow (1997) United States Presidential Young Investigator Award (1989) Multiple DOE Grid Optimization Challenge Awards (2020–2023) Best Paper Awards from IEEE Transactions and Conferences Outstanding Education Award, Cornell University (1990) He has successfully managed over 100 research projects and holds 28 U.S. and international patents. As the founder of Bigwood Systems, Inc., he has commercialized advanced software for utility companies across the U.S. and Japan. His team has published over 480 refereed papers and received more than 17,500 citations. He advises a large research group and leads innovations in computational methods for energy systems. His lab is actively involved in developing next-generation tools for grid security, optimization, and machine learning integration.

Kirsten WinterView profile

Professor

• Formal Verification
• Concurrent Programming
• Computer Security
• +3 more

Dr. Kirsten Winter is an Honorary Senior Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. Her research focuses on formal verification, concurrent programming, and weak memory models. She has contributed significantly to areas such as model checking, railway interlocking systems, and behavior trees. Her work spans theoretical foundations (e.g., linearizability, concurrency semantics) and practical applications in security and embedded systems. Recent projects include the Program Analysis Cell and BASIL: Boogie Analysis for Secure Information-Flow Logics. Winter has collaborated extensively with researchers like Graeme Smith and Robert Colvin. Her most recent publications address speculative execution vulnerabilities and compositional reasoning in weak memory architectures.

Tharindu P. De AlwisView profile

Assistant Professor

• High-dimensional Multivariate Statistics
• Sufficient Dimension Reduction
• Dimension Reduction
• +9 more

Dr. Tharindu P. De Alwis is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is actively engaged in teaching and research, with a focus on high-dimensional data analysis and machine learning applications. Ph.D. in Mathematics (Statistics), Southern Illinois University Carbondale M.S. in Mathematics, Southern Illinois University Carbondale B.Sc. in Statistics and Operations Research, University of Peradeniya, Sri Lanka His research centers on dimension reduction techniques, particularly Sufficient Dimension Reduction (SDR), envelope methods, and their applications in multivariate time series and spatial-temporal data. He integrates deep learning and neural networks into statistical modeling, with recent work on stacking-based deep neural networks and Fourier-based SDR methods. His work bridges statistical theory with practical machine learning applications in complex datasets. His recent publications (2021–2024) demonstrate a strong focus on developing innovative statistical and machine learning methods for time series and high-dimensional regression. Key themes include nonlinear modeling, dimension reduction, R package development, and AI-augmented reliability analysis. These works reflect interdisciplinary applications in engineering, data science, and systems safety. Dr. De Alwis has presented his research at national academic conferences in the USA and has publications in peer-reviewed journals such as Statistical Methods & Applications and Reliability Engineering & System Safety , as well as preprints on arXiv and software on CRAN. He teaches a variety of courses including Precalculus with Trigonometry, Linear Algebra, Applied Statistics, and Data Science. While no formal advisees or grants are mentioned, his active publication record suggests ongoing research mentorship and scholarly engagement. He previously served as a post-doctoral scholar at Worcester Polytechnic Institute before joining UWF.

• Arithmetic Geometry
• Algebraic K-Theory
• Non-Archimedean Geometry
• +1 more

Prof. Dr. Georg Tamme is a faculty member at the Institute of Mathematics (Johannes Gutenberg University Mainz). His research focuses on Arithmetic Geometry and Algebraic K-Theory , with significant contributions to non-Archimedean geometry, motivic homotopy theory, and regulator maps. Research Interests: Tamme's work bridges Algebraic K-Theory Arithmetic Geometry Non-Archimedean Geometry Differential Algebraic K-Theory Advising: He supervises doctoral students including Andreas Gieringer Luca Passolunghi

• Operator Algebra
• C*-algebras
• K-theory
• +3 more

Søren Eilers is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on Operator Algebras , particularly the classification of C*-algebras related to discrete and low-dimensional structures. He is a member of the FNU network 'Automorphisms and Invariants for Operator Algebras' and advocates for experimental mathematics using computational methods in pure mathematics. Education: MS in Mathematics and Computer Science, University of Copenhagen (1993) PhD in Mathematics, University of Copenhagen (1995) Research Interests: Operator Algebras K-theory Symbolic Dynamics Discrete Mathematics Experimental Mathematics Recent Publications (2016-2024) demonstrate expertise in graph C*-algebras , symbolic dynamics , and computational approaches to pure mathematics, with key collaborations in Denmark, Japan, Canada, and the U.S. Scientific Leadership: President, Danish Mathematical Society (2006-2008) Principal Investigator, Villum Fonden (2012-2016) Main Organizer, Mittag-Leffler Institute Program (2016) Advisory Roles: Supervised 28 master's theses and mentored 9 PhD students/postdocs (2003-2022) across institutions in Denmark, Canada, Japan, and the U.S.

Joachim KockView profile

Associate Professor

• Category theory
• Homotopy theory
• Combinatorics
• +4 more

Joachim Kock is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen, with research focusing on category theory, homotopy theory, combinatorics, and algebraic geometry. His work explores intersections with probability theory, mathematical physics, and computer science, particularly through decomposition spaces, operads, and categorical structures. Education: Not explicitly mentioned External Employment: Professor Titular at Autonomous University of Barcelona Research Areas: Category theory and higher structures Homotopy-theoretic combinatorics Decomposition spaces and incidence algebras Applications in computer science and mathematical physics Recent Publications: 2024: ∞-operads as symmetric monoidal ∞-categories 2022: Tracelet Hopf Algebras and Decomposition Spaces 2022: Infinity-Operads as Analytic Monads 2021: Every 2-Segal space is unital 2021: Operadic categories and decalage Editorial Activities: Editor, Theory and Applications of Categories (since 2017) Editor, Higher Structures (2016–2022) Editor, Compositionality (since 2018)

Zeyu DingView profile

Assistant Professor

• Privacy
• Algorithmic Fairness
• Machine Learning
• +5 more

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 LiView profile

Associate Professor

• STEM Education
• Mathematics Education
• Engineering Education
• +5 more

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
• +10 more

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.

Vanessa MiemietzView profile

Professor

• Representation Theory
• Homological Algebra
• 2-Representation Theory
• +4 more

Vanessa Miemietz is a Professor of Pure Mathematics at the University of East Anglia , based in the School of Engineering, Mathematics and Physics. Her research focuses on representation theory, homological algebra, and categorification, with specializations in 2-representation theory and algebraic structures related to finite-dimensional algebras and algebraic groups. Education: PhD in Mathematics (Dr.rer.nat), University of Stuttgart (2002–2005) Diploma in Mathematics (Dipl.Math), University of Stuttgart (1997–2001) Research Interests: Structural properties of representation categories, categorification phenomena, and applications of homological algebra in Lie-theoretic contexts. She actively explores connections between algebraic groups and finite-dimensional algebra representations, with a focus on advancing 2-representation theory frameworks. Projects & Grants: Lead on 2-representation theory projects funded by EPSRC (2019–2023, 2025–2028) Hosted LMS Prospects in Mathematics Meeting (2021) Collaborative grants with Dr. R. Laugwitz and Dr. Jyun-Ao Lin Editorial Roles: Editorial Board Member, Applied Categorical Structures (2023–present) Editorial Board Member, Communications in Algebra (2021–present) Supervision & Mentoring: Supervises PhD students in representation theory of algebraic groups and homological algebra. Open to discussing projects beyond listed opportunities and securing funding alternatives.

Frederik MarksView profile

Researcher

• Algebra
• Representation Theory
• Homological Algebra
• +3 more

Fredrik Marks is a Researcher at the Institute of Algebra and Number Theory at the University of Stuttgart. He holds a PhD in Mathematics from the University of Stuttgart (2011-2014) and completed his Habilitation in Mathematics there in 2022. His research focuses on algebra, representation theory, homological algebra, category theory, and silting/tilting theory. Marks has coordinated the DFG-funded Network on Silting Theory since 2021 and led a Baden-Württemberg Stiftung postdoctoral grant (2020-2024). He has organized numerous conferences, including the 2019 Two Weeks of Silting conference and the 2022 Silting Theory, Algebras and Representations workshop. His teaching portfolio includes courses such as Fachdidaktik 2 , Algebra 2 , and seminars on tilting theory and polynomials over finite fields. His publications explore advanced topics like universal localisations, torsion classes, and functorial approaches to triangulated categories. Awards include a DAAD PRIME fellowship (2015-2016) and ongoing DFG/DFG support for collaborative research.

Margaret WrightView profile

Professor

• Optimization
• Linear Algebra
• Numerical Computing
• +3 more

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.

Mikel SanzView profile

Researcher

• Quantum Computing
• Quantum Algorithms
• Quantum Technologies
• +5 more

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 GuView profile

Professor

• Numerical Linear Algebra
• Scientific Computing
• Matrix Computations
• +3 more

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