جست‌وجوی استاد و پژوهشگر

• Quantum Computing
• Quantum Open Systems
• Non-equilibrium Statistical Mechanics
• +۲ مورد دیگر

Tatsuhiko Shirai is an Associate Professor (non-tenure-track) at the Waseda Institute for Advanced Study, Waseda University, specializing in quantum computing and quantum open systems. His research focuses on developing algorithms for quantum and Ising machines to solve complex combinatorial optimization problems, as well as studying fundamental aspects of non-equilibrium quantum dynamics. His educational background includes: Doctoral Program, Department of Physics, Graduate School of Science, The University of Tokyo (2013-2016) Master's Program, Department of Physics, Graduate School of Science, The University of Tokyo (2011-2013) Bachelor's Degree, Department of Physics, Faculty of Science, The University of Tokyo (2007-2011) Shirai's research spans both theoretical and applied aspects of quantum computing. His primary interests include quantum open systems, where he investigates the dynamics of quantum systems interacting with environments, and quantum computing, where he develops novel algorithms for quantum annealers and Ising machines. His work on non-equilibrium statistical mechanics explores fundamental questions about thermalization in open quantum systems and the emergence of steady states. In practical applications, he has made significant contributions to constrained combinatorial optimization problems, developing techniques like variable reduction methods and multi-spin-flip engineering to improve the performance of quantum and quantum-inspired solvers. His recent publications reveal a strong focus on bridging theoretical quantum physics with practical optimization applications. The research trends show an evolution from fundamental quantum open system theory toward increasingly practical quantum computing applications, particularly in combinatorial optimization. His work spans across quantum simulation, quantum algorithms, Ising machines, and quantum-inspired classical computing approaches, demonstrating a unique interdisciplinary perspective that connects theoretical physics with real-world computational challenges. His notable scientific achievements include: 2022 SLDM Study Group Outstanding Paper Award from the Information Processing Society of Japan 16th Young Scientist Encouragement Award (area 11) from The Physical Society of Japan (March 2022) 197th SLDM Study Group Excellent Presentation Award from the Information Processing Society of Japan (January 2022) Shirai has been actively involved in multiple research projects funded by the Japan Society for the Promotion of Science, including the development of physical property control methods in non-equilibrium quantum open systems (2023-2027), high-performance computing for materials simulation using hybrid quantum-classical algorithms (2021-2024), and construction of analytical methods using typical non-equilibrium steady states (2018-2021). His work has attracted media attention, including coverage on EurekAlert! for his research on multi-spin-flip engineering in Ising machines and novel quantum algorithms for combinatorial optimization. As part of Waseda Institute for Advanced Study, Shirai contributes to an interdisciplinary research environment that fosters innovation at the intersection of physics, computer science, and mathematics. His work on quantum algorithms and quantum open systems forms part of a broader effort to advance both the theoretical foundations and practical applications of quantum information processing.

Djordje Zikelicمشاهده پروفایل

استادیار

• Formal Methods
• Trustworthy AI
• Program Analysis and Verification
• +۶ مورد دیگر

Djordje Zikelic is an Assistant Professor of Computer Science at Singapore Management University's School of Computing and Information Systems. He holds a PhD from the Institute of Science and Technology Austria (ISTA), where he was advised by Krishnendu Chatterjee and Petr Novotný. His research focuses on formal methods for ensuring correctness, safety, and trustworthiness of software and AI systems, with an emphasis on probabilistic systems, program verification, and safe autonomy. He has received awards including the Outstanding PhD Thesis and Outstanding Scientific Achievement awards from ISTA. Education: PhD in Computer Science, ISTA (2023) Bachelor's and Master's in Mathematics, University of Cambridge Research Interests: Formal verification of probabilistic systems Trustworthy AI and neural network certification Runtime monitoring and control synthesis Blockchain protocol analysis Teaching: CS423: Heuristic Search and Optimization (Spring 2025/2024) His recent work includes advancements in supermartingale-based verification, neural controller certification, and automated analysis of blockchain systems. He serves on program committees for top conferences like AAAI, CAV, and IJCAI and actively mentors students in PhD, internship, and visiting roles.

• Stochastic Control
• Financial Mathematics
• Optimization
• +۶ مورد دیگر

Professor Harry Zheng is a Professor of Mathematics at Imperial College London's Faculty of Natural Sciences, Department of Mathematics. He is affiliated with the CFM-Imperial Institute of Quantitative Finance and Mathematical Finance. His research focuses on stochastic control, optimization, and financial mathematics with applications in banking, numerical methods, and statistics. Key research areas include stochastic control theory, mean field games, financial derivative pricing, and machine learning integration in financial systems. His work addresses optimal investment strategies, risk management, and systemic risk modeling in dynamic markets. Notable contributions include applying deep learning to solve high-dimensional stochastic control problems and analyzing governance dynamics using mean field frameworks. Recent publications span innovations in neural network-based solvers for Hamilton-Jacobi-Bellman equations, robust optimization under uncertainty, and behavioral finance models incorporating transaction costs and prospect theory. Professor Zheng collaborates with institutions like the CFM-Imperial Institute to advance quantitative finance methodologies. His research bridges theoretical advancements with practical applications in portfolio management, risk assessment, and computational finance.

Kyler Siegelمشاهده پروفایل

استادیار

• Symplectic Geometry
• Algebraic Geometry
• Differential Geometry
• +۷ مورد دیگر

Kyler Siegel is an Assistant Professor in the Department of Mathematics at the University of Southern California, with a visiting appointment at Seoul National University until 2024. Previously, he held positions as a Ritt Assistant Professor at Columbia University, an NSF Postdoctoral Research Fellow at Columbia (2017–2019) and MIT (2016–2017), and was a Member at the Institute for Advanced Study in Fall 2021. His research focuses on symplectic geometry , bridging smooth topology, differential geometry, algebraic geometry, and mathematical physics. Key interests include pseudoholomorphic curve invariants (Floer theory, symplectic field theory), quantitative symplectic phenomena, and interactions with algebraic geometry (e.g., singular curves, Hamiltonian flows). He has been supported by NSF grants (DMS-2506639, DMS-2533874, DMS-2105578) and a Simons Foundation Travel Award. Notable achievements include solving stabilized symplectic embedding problems, developing ellipsoidal superpotential frameworks, and advancing techniques for singular curve counts. He has advised REU students on projects like numerical symplectic embeddings using machine learning. Teaching includes advanced courses on differential geometry and symplectic methods, with past roles in mentoring workshops and conferences (e.g., Symplectothon 2024). His work also extends to interdisciplinary projects, such as the PuzzleCluster algorithm for metagenomics data analysis.

Emilio Jesús Gallego Ariasمشاهده پروفایل

پژوهشگر ارشد

• Theorem Proving
• Functional Programming
• Computer Music
• +۷ مورد دیگر

Emilio Jesús Gallego Arias is a non-tenured Research Fellow at the French National Center for Scientific Research (CNRS), hosted at the Institute of Fundamental Computer Research (IRIF) of CNRS and University of Paris Cité. He is also a member of the PiCube Inria team. Previously, he held postdoctoral positions at the University of Pennsylvania (2012–2014) and MINES ParisTech (2014–2019). His research spans mechanically-verified functional and logic programming , with a focus on the Coq proof assistant and the Mathematical Components Library . He develops tools like coq-lsp (language-server for Coq IDEs) and jsCoq (web interface), replacing earlier projects like SerAPI . His work bridges programming language theory , digital signal processing , and formal verification , particularly in the ANR FEEVER project for verifying Faust programs. His 15 most recent works (2014–2024) address type systems , differential privacy , and formal verification in domains like audio processing and mechanism design . Publications span journals (e.g., Journal of Privacy and Confidentiality), conferences (ICML, POPL, FARM), and workshops (CoqPL, UITP). He contributes to open-source projects (GitHub), including DFuzz (linear dependent types), DualQuery (privacy algorithms), and RAM (relational machine). He uses formal methods in collaborative development platforms (Gitter, GitLab) and advocates for free software and accessible audio technology .

Debora Imperaمشاهده پروفایل

دانشیار

• Differential Geometry
• Geometric Analysis
• Riemannian Geometry
• +۲ مورد دیگر

Debora Impera is an Associate Professor at the Department of Mathematical Sciences of the Polytechnic University of Turin . She is a member of the Functional Analysis and Differential Geometry research group and has held a tenure-track assistant professor (Ricercatore di tipo B) position at the same university prior to her current role. Her research focuses on Differential Geometry and Geometric Analysis , particularly on Riemannian manifolds and mean curvature flow. Key areas include: Geometric rigidity theorems Self-shrinkers and translators Sobolev spaces and cut-off functions Topological constraints in geometric PDEs Her recent publications (2025-2016) span journals like Calculus of Variational PDEs , International Mathematics Research Notices , and The Journal of Geometric Analysis , with recurring themes of Ricci curvature , isoperimetric inequalities , and topological rigidity . Debora teaches courses such as: Differential Geometry (Mathematics for Engineering, 2024/25 and 2025/26) Linear Algebra and Geometry (Aerospace Engineering, Management Engineering, etc.) Problem Solving Lab 2 (Aerospace Engineering, 2020/21)

James Demmelمشاهده پروفایل

استاد

• Numerical Analysis
• Linear Algebra
• Scientific Computing
• +۴ مورد دیگر

James Demmel is a Professor of Computer Science and Mathematics at the University of California, Berkeley, with joint appointments since 1990. His research focuses on numerical linear algebra, high-performance computing, and parallel algorithms. He co-developed widely used libraries like LAPACK and ScaLAPACK. Demmel holds ACM, SIAM, and IEEE Fellowships, and is a member of both the National Academy of Engineering and Sciences. Education: Ph.D. in Computer Science, UC Berkeley, 1983 B.S. in Mathematics, Caltech, 1975 Research Interests: Demmel’s work emphasizes numerical methods for linear algebra, including algorithms for eigenvalue problems, matrix factorizations, and high-performance computing architectures. His contributions bridge theory and practice, addressing challenges in floating-point arithmetic and algorithm scalability. Publications & Awards: Over 150+ publications, including foundational papers on LAPACK and iterative methods. Recipient of the SIAM Activity Group Linear Algebra Best Paper Prize (1988, 1991), Wilkinson Prize (1993), and ACM Supercomputing Test of Time Award (2019). Labs & Teams: Demmel collaborates with groups like Berkeley Benchmarking and Optimization Group (BeBOP) and CLIMB, focusing on algorithmic efficiency and exascale computing.

• Mathematics Education
• Computing Education
• STEM Equity
• +۳ مورد دیگر

Sarah D Castle serves as Assistant Professor of Mathematics Education at the University of Idaho with dual appointments in the Department of Mathematical and Statistical Sciences (College of Science) and Curriculum and Instruction Department (College of Education, Health and Human Sciences). Her research bridges computational methods and mathematics education to advance equity and foster creativity in undergraduate STEM contexts. Her educational background includes: B.A. in Mathematics, Whitworth University (2012-2016) B.S. in Engineering Physics, Whitworth University (2012-2016) M.S. in Mathematics, Michigan State University (2019-2022) Ph.D. in Mathematics Education, Michigan State University (2018-2023) Castle's research centers on computational approaches to uncover systemic inequities in STEM education while creating environments that nurture mathematical creativity, particularly for marginalized students. She investigates how computational modeling—such as Jupyter Notebook implementations in linear algebra—can transform mathematical engagement and understanding. Her work critically examines power structures in educational spaces and develops pedagogical frameworks that leverage coding to deepen conceptual learning and promote equitable participation. Analysis of her 13 publications (2020-2024) reveals three dominant trends: (1) computational tools as catalysts for mathematical creativity and conjecture, (2) quantitative critical analyses of systemic advantages in STEM courses, and (3) investigations of student agency development in proof-based mathematics. These works span ACM computer science education conferences, mathematics education research forums, and interdisciplinary learning sciences venues. Castle actively contributes to major research initiatives: SEISMIC Project: Multi-institutional analysis of equity in introductory STEM courses Transition to Proof Project: Studying sense-making in advanced mathematics Computational Education Research Lab: Developing computational modules for mathematics learning Her teaching portfolio includes Secondary Mathematics Methods (EDCI 434), Proof and Viable Argumentation (MTHE 410), and Calculus I (MTH 132), reflecting her dual expertise. She has extensive experience as instructor of record, teaching assistant, and developer of STEM curricula including high-altitude balloon launch modules for K-12 students.

• Operational Research
• Combinatorial Optimization
• Heuristics
• +۴ مورد دیگر

Maria Cândida Mourão is a full Professor in Operational Research at the Mathematics Department of ISEG - Lisbon School of Economics and Management, University of Lisbon. She serves on the Executive Committee and coordinates the Operational Research scientific area at ISEG, alongside active participation in CEMAPRE research center. PhD in Mathematics Applied to Economics and Management (1998) Aggregation in Mathematics, Operational Research (2010) Her research focuses on Combinatorial Optimization , Heuristics , and Mixed Integer Linear Programming to solve complex Arc Routing Problems in urban logistics, waste collection, and transportation. She has published extensively in top journals like European Journal of Operational Research , Networks , and Computers & Operations Research , contributing both theoretical models and real-world applications. Her work emphasizes bridging academic research with practical solutions for network design and logistics optimization . She has served on organizing committees for international conferences and acts as reviewer for major journals. Teaching responsibilities include coordinating curricular units in Operational Research and supervising over 25 Master's theses in optimization, scheduling, and routing applications across sectors like urban waste, energy distribution, and public safety.

Veronica Piccialliمشاهده پروفایل

استاد

• Nonlinear Optimization
• Semidefinite Programming
• Game Theory
• +۶ مورد دیگر

Veronica Piccialli is a Full Professor at the Department of Computer, Control and Management Engineering "Antonio Ruberti" (DIAG) at Sapienza University of Rome. She teaches Geometry I for Management Engineering and Optimization Methods for Machine Learning for Data Science. Previously, she was Associate Professor at the University of Rome Tor Vergata (2020-2021) and Researcher there from 2008 to 2020. She serves as Associate Editor for INFORMS Journal on Computing (since 2019) and EURO Journal on Computational Optimization (since 2021). Dr. Piccialli earned her degree in Computer Engineering (summa cum laude) and PhD in Operations Research from Sapienza University of Rome in 2000 and 2004 respectively. In 2006, she completed a postdoc at the Combinatorics & Optimization department of the University of Waterloo, Canada. She obtained Italian national scientific qualifications as Associate Professor in 2013 and as Full Professor in 2017. Her research focuses on the intersection of optimization and machine learning, with particular expertise in nonlinear optimization, semidefinite programming, and mixed integer nonlinear programming. She applies these methods to diverse areas including Brain Computer Interfaces, electric consumption disaggregation, and process engineering for membrane systems. Her work demonstrates how advanced optimization techniques can enhance machine learning algorithms and solve complex engineering problems. Her recent publications show a strong trend toward integrating optimization with machine learning, particularly in clustering algorithms, support vector machines, and neural networks. She has developed exact algorithms for semi-supervised learning problems and applied optimization techniques to real-world challenges in logistics, energy systems, and process engineering. Her interdisciplinary approach bridges theoretical advances in optimization with practical applications across multiple domains. Dr. Piccialli has authored or co-authored over 40 articles in prestigious journals including Mathematical Programming, SIAM Journal on Optimization, IEEE Transactions on Neural Networks and Learning Systems, and Computational Optimization and Applications. She has also contributed 3 refereed book chapters to international publications. As an educator, she supervises student research and teaches advanced courses in optimization methods and geometry. Her teaching materials demonstrate a commitment to connecting theoretical concepts with practical applications, particularly in data science and machine learning contexts. Her current research involves collaborations with Université de Lorraine on membrane systems for gas filtration and with RFI (Rete Ferroviaria Italiana) on optimizing last-mile facilities for freight trains, demonstrating the real-world impact of her optimization expertise.

Darij Grinbergمشاهده پروفایل

استادیار

• Algebra
• Combinatorics
• Abstract Algebra
• +۳ مورد دیگر

Summary Darij Grinberg is an Assistant Professor at Drexel University's Department of Mathematics. His research focuses on algebra, combinatorics, and their intersections, with notable contributions to Hopf algebras, binomial coefficients modulo primes, and combinatorial inequalities. He has taught a variety of courses, including Graph Theory, Algebraic Combinatorics, and Discrete Mathematics, both at Drexel and previously at the University of Minnesota and MIT. His teaching and research emphasize problem-solving techniques and rigorous mathematical exposition. Grinberg is actively involved in writing detailed lecture notes and expository papers aimed at both undergraduate and graduate audiences. Education and Background While explicit details of his education are not provided, his extensive publication record and academic roles suggest a strong background in mathematics, including advanced studies in algebra and combinatorics. Research Interests Grinberg's work spans several areas within algebra and combinatorics, including: Algebraic Combinatorics : Focus on Hopf algebras, symmetric functions, and combinatorial structures. Number Theory : Investigations into binomial coefficients modulo primes and congruences. Discrete Mathematics : Contributions to graph theory, enumeration problems, and problem-solving techniques. Teaching He has taught courses such as: Graph Theory (Drexel, Spring 2025) Algebraic Combinatorics (Drexel, 2021–2024) Enumerative Combinatorics (Drexel, 2019–present) Discrete Mathematics (Undergraduate-level, Drexel) Publications and Notes Grinberg has authored numerous papers and lecture notes, including: Notes on the combinatorial fundamentals of algebra (2015) On binomial coefficients modulo squares of primes (2017) Fleck's binomial congruence using circulant matrices (2016) His work often bridges foundational theory with problem-solving applications, particularly in mathematical competitions and journals.

Anastasios Kyrillidisمشاهده پروفایل

دانشیار

• Optimization for Machine Learning
• Convex and Non-convex Algorithms
• Large-Scale Optimization
• +۳ مورد دیگر

Overview Anastasios Kyrillidis (Tasos) is a Noah Harding Associate Professor in the Department of Computer Science at Rice University, part of the George R. Brown School of Engineering. His research focuses on optimization for machine learning, convex/non-convex algorithms, and large-scale systems, with recent ventures into quantum computing and bioinformatics applications. He has held postdoctoral positions at IBM T.J. Watson Research Center and the University of Texas at Austin, following his PhD in Computer Science from EPFL (Switzerland). Education PhD, School of Computer and Communication Sciences, EPFL (2014) MSc, Electrical and Electronics Engineering, Technical University of Crete (2010) Diploma, Electrical and Electronics Engineering, Technical University of Crete (2008) Research Interests His work bridges optimization theory and practical machine learning systems. Key areas include: Designing efficient algorithms for high-dimensional problems Quantum computing applications in optimization Protein structure prediction via 3D Patterson maps (CrysFormer framework) Federated learning and distributed systems Neural network compression and acceleration Awards & Funding Recipient of NSF CAREER Award, Amazon Research Award, and Welch Foundation Grant. His research is supported by NSF, Amazon, Microsoft, and Intel collaborations. Notable recognitions include the Goldstine Fellowship (IBM) and Simons Foundation Postdoctoral Fellowship. Teaching Instructs advanced optimization courses (COMP 545, COMP 414) and Algorithmic Thinking (COMP 182). Focuses on connecting theory with real-world machine learning challenges. Labs & Teams Leads the OptimaLab at Rice, advancing research in optimization, quantum algorithms, and bioinformatics. Collaborates with interdisciplinary teams on projects like AI-OWLS and CrysFormer protein modeling.

• Computational Complexity
• Communication Complexity
• Coding Theory
• +۶ مورد دیگر

Anna Gál is a Professor in the Department of Computer Science at the University of Texas at Austin, part of the College of Natural Sciences. She is a member of the Algorithms and Computational Theory (ACT) group and teaches core theoretical computer science courses such as Algorithms and Complexity, Theory of Computation, and Analysis of Boolean Functions. Her Ph.D. in Computer Science was awarded by the University of Chicago in 1995 under the supervision of János Simon. Ph.D. in Computer Science, University of Chicago, 1995 Her research focuses on foundational aspects of computation, including computational complexity, communication complexity, coding theory, circuit complexity, and combinatorics. She investigates lower bound methods and the structural properties of Boolean functions, often employing algebraic and combinatorial techniques. Her work has significant implications for understanding the limits of efficient computation. The recent publications demonstrate a consistent focus on complexity measures of Boolean functions, circuit lower bounds, communication models, and coding theory. There is a strong emphasis on sensitivity, block sensitivity, formula size, and locally decodable codes, reflecting deep engagement with open problems in theoretical computer science. Her scientific achievements have been recognized through prestigious awards: Machtey Award for best student paper at FOCS 1991 EATCS best paper award at Track A of ICALP 2003 She has successfully advised multiple Ph.D. students, including Jeff Ford, Vladimir Trifonov, Andrew Mills, Keith Jing-Tang Jang, and Siddhesh Chaubal, whose theses contributed to multiparty communication complexity, circuit complexity, and locally decodable codes. While specific grant details are not listed, her sustained publication record in top venues suggests active funding support. Her role in the ACT group indicates collaboration with other leading researchers in algorithms and complexity. Anna Gál leads a research program centered on the Algorithms and Computational Theory group at UT Austin, mentoring students and contributing to the advancement of theoretical computer science through rigorous mathematical analysis and innovative problem-solving.

• Modal Logic
• Temporal Logic
• Automated Reasoning
• +۵ مورد دیگر

Cláudia Nalon is a full professor at the Department of Computer Science at the University of Brasília, where she has established herself as a leading researcher in modal logic and automated reasoning. Her academic career spans over two decades with continuous contributions to theoretical foundations and practical implementations of reasoning systems. Research Profile Professor Nalon specializes in proof methods for combined modal logics, particularly those allowing interaction between different logical components. Her PhD work focused on resolution-based methods for interactions between linear-time temporal logic and multi-modal S5. She has developed tableau-based methods for temporal logics of knowledge with both no learning and perfect recall scenarios. Her expertise extends to the theoretical foundations and implementation of reasoning tools for normal modal logics, including systems with symmetry, reflexivity, seriality, transitivity, and Euclideaness. More recently, she has pioneered resolution methods for Coalition Logics, a non-normal modal logic for reasoning about cooperative agency. Publication Trends Analysis of her 15 most recent publications (2020-2024) reveals consistent focus on modal logic reasoning with particular emphasis on resolution methods, reduction techniques, and model construction. Her work demonstrates a strong connection between theoretical foundations and practical implementations, with numerous papers presenting evaluated theorem-proving techniques and their applications. The publications span major venues in automated reasoning including IJCAR, TABLEAUX, and CADE. Professional Service Editorial board of The IfCoLog Journal of Logics and their Applications (since 2015) Editorial board of Annals of Mathematics and Artificial Intelligence (2025-2027) Steering committee for LSFA (2018-2021), TABLEAUX (2016-2023), CADE (2021-2024) Executive committee of the Brazilian Logic Society (2017-2021) Program committees for over 30 international conferences from 2010 to 2025 Mentorship and Software Development Professor Nalon has supervised multiple student projects resulting in significant software implementations for modal logic reasoning. She has personally developed several theorem provers including ltl2snf (for temporal logic translation), KSP (for multimodal K), and CLProver (for Coalition Logic). Her mentorship has produced numerous student-led implementations that extend these systems, demonstrating her commitment to training the next generation of researchers in formal methods. Community Building Professor Nalon has been instrumental in establishing research communities focused on logic and automated reasoning in Brazil. She served as co-chair for TABLEAUX 2017 in Brasília and has organized multiple editions of the Brazilian Logic Workshop (WBL). Her work with the LSFA workshop series has fostered a strong international community of researchers in logical and semantic frameworks.

• Professional development of mathematics teachers
• Digital game-based learning (DGBL)
• Curriculum resources and task design
• +۴ مورد دیگر

Dr. Ljerka Jukić Matić is an Associate Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek . As Head of the Geometry and Mathematics Education Group , her work focuses on: Teacher professional development Digital game-based learning (DGBL) Curriculum resource design and utilization Her recent publications examine: Non-routine task approaches in undergraduate mathematics Constructionist DGBL implementation challenges Longitudinal curriculum material interactions Systematic reviews of teacher professional development Research trends in her 15 most recent articles highlight: Integration of DGBL in secondary mathematics Teacher-student-textbook dynamics Cross-cultural comparisons of mathematical concepts Assessment of conceptual vs procedural knowledge retention She employs mixed-methods research designs, combining: Longitudinal case studies Triangulated data collection Frameworks like curricular noticing and Ref*AER Her work contributes to understanding: Mathematics teacher design capacity Textbook's role in educational reform Implementation of creativity-fostering tasks Remote education challenges during the pandemic