جست‌وجوی استاد و پژوهشگر

• Partial Differential Equations
• Control Theory
• Quantum Control
• +۴ مورد دیگر

Karine Beauchard is a University Professor in the Department of Mathematics at École normale supérieure de Rennes (ENS Rennes), France. Her research focuses on the mathematical analysis and control of partial differential equations with applications to quantum systems and other physical phenomena. Her primary research interests include: Analysis and control of partial differential equations Interaction between geometry (Lie brackets) and analysis in control theory Bilinear control and quantum control (exact/approximate controllability, feedback stabilization of Schrödinger and Bloch equations) Control of degenerate parabolic equations of hypoelliptic type (Grushin, Heisenberg, Kolmogorov, Ornstein-Uhlenbeck) Inverse problems and asymptotic observers Professor Beauchard's research output shows a strong focus on controllability properties of various partial differential equations, particularly examining the minimal time requirements, geometric conditions, and quadratic obstructions to controllability. Her work bridges pure mathematical analysis with applications in quantum physics and engineering systems. Professor Beauchard completed her PhD in 2005 at Université d'Orsay with a thesis on the controllability and stabilization of the Schrödinger equation, supervised by Jean-Michel Coron. She obtained her HDR (Habilitation à Diriger des Recherches) in 2010 at ENS Cachan on the analysis and control of partial differential equations. She has published 44 papers spanning from 2005 to 2025, with recent work focusing on small-time controllability of Schrödinger equations, obstructions to controllability, and control of hypoelliptic equations. Her publications appear in prestigious journals including Annales de l'Institut Henri Poincaré, Journal of Differential Equations, and SIAM Journal on Control and Optimization. Professor Beauchard has collaborated with numerous researchers including F. Marbach, E. Pozzoli, P. Cannarsa, and J.-M. Coron, demonstrating her active engagement in the mathematical community. Her work has contributed significantly to the theoretical foundations of control theory for partial differential equations.

• Control of Partial Differential Equations
• Quantum Control
• Geometric Control Theory
• +۲ مورد دیگر

Eugenio Pozzoli is a CNRS permanent researcher (chargé de recherche) since 2023, jointly affiliated with the Rennes Institute of Mathematical Research (IRMAR) at the University of Rennes and the École Normale Supérieure de Rennes (ENS Rennes). His research bridges advanced mathematics and quantum physics, focusing on control theory for partial differential equations and quantum mechanical systems with applications in quantum computing and molecular dynamics. Education: PhD in Mathematics, Sorbonne University and INRIA team CAGE (2021), supervised by Ugo Boscain and Mario Sigalotti Research Focus: Professor Pozzoli's work centers on small-time controllability problems using geometric control theory and sub-Riemannian geometry . His investigations span quantum control of molecular systems (rotors, chiral molecules), bilinear Schrödinger equations , and nonlinear PDE control (heat, wave equations). Recent breakthroughs include enhancing controllability via static fields and solving enantiomer-selective state transfer in degenerate quantum systems, demonstrating exceptional mathematical rigor with experimental relevance. Publication Trends: Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on quantum control applications (60% of articles), particularly for molecular rotation and chirality, combined with fundamental advances in geometric control theory (30%) and PDE controllability (10%). His work consistently addresses the critical challenge of achieving control in minimal time frames, with increasing emphasis on experimentally feasible strategies for quantum technologies. Grants and Leadership: Principal Investigator of ANR JCJC project QUEST (Quantum and ensemble control in small times) Co-organizer of 'Applications of Mathematics' research seminars at ENS Rennes Lab Environment: As part of IRMAR's mathematics research ecosystem, Professor Pozzoli leads a dynamic group focused on geometric control methods. The QUEST project provides substantial resources for computational modeling and theoretical development, with active recruitment for postdoctoral researchers. His lab emphasizes interdisciplinary collaboration between mathematicians and quantum physicists, maintaining strong ties with Sorbonne University and INRIA while fostering international partnerships through co-authored publications in leading physics and mathematics journals.

• Global Optimization
• Numerical Analysis
• Data Analysis
• +۳ مورد دیگر

Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.

Marius Călinمشاهده پروفایل

مدرس

• Soft Computing in Agricultural Sciences
• Decision Making under Uncertainty
• Decision Support Systems
• +۲ مورد دیگر

Marius Călin serves as a Lecturer in the Department of Exact Sciences at the Faculty of Horticulture, University of Agricultural Sciences and Veterinary Medicine of Iasi, Romania. His academic profile bridges computational science with agricultural applications through expertise in Information Technology, Applied Mathematics, and E-learning systems development. His research portfolio centers on five core domains: Soft Computing in Agricultural Sciences: Pioneering fuzzy logic applications for plant breeding and greenhouse management Decision Making under Uncertainty: Developing models for agricultural scenarios with incomplete data Decision Support Systems: Creating tools for land suitability assessment and resource optimization E-learning Applications: Designing specialized platforms for agricultural education Graph Databases: Implementing knowledge representation systems for agricultural data Analysis of his publication trajectory reveals an evolution from foundational fuzzy decision models (2000-2007) toward increasingly interdisciplinary work. Recent publications (2015-2022) demonstrate strong convergence between e-learning infrastructure development and agricultural informatics, particularly through Moodle-based systems for remote education during the pandemic and computational models for soil management. His work consistently emphasizes practical implementation of grid computing and soft computing techniques in agricultural contexts. Dr. Călin has secured significant research funding through seven major contracts, including leadership as USAMV Iasi responsible for the CEEX 1801 grid computing project (2006-2008) and contributions to sustainable soil management initiatives (ECOSEUMET, CNCSIS 738). His grant portfolio demonstrates sustained focus on translating computational research into agricultural solutions. Within the university ecosystem, he serves as Moodle platform administrator and coordinates Informatics discipline activities, fostering cross-departmental collaboration. His professional affiliations include ESNA (European Society for New Methods in Agricultural Research) and ROMAI (Romanian Society of Applied and Industrial Mathematics), reflecting his commitment to interdisciplinary scientific exchange.

• Network Science
• Dynamical Systems
• Stochastic Processes
• +۶ مورد دیگر

Professor István Z. Kiss is a Professor of Network Science at the Network Science Institute, Northeastern University London. His research bridges network science, dynamical systems, and stochastic processes with applications in epidemic modeling, computational neuroscience, and complex systems analysis. He leads the Systems Processes and Networks Lab (SPAN Lab) and maintains active collaborations across multiple institutions globally. Current position: Professor of Network Science Institution: Northeastern University London Research unit: Network Science Institute (SPAN Lab) Key collaborators: Péter L. Simon (Eötvös Loránd University), Joel C. Miller (La Trobe University), Gregory A. Rempała (Ohio State University) His research focuses on theoretical and data-driven problems at the intersection of network science and dynamical processes. Key areas include network inference, exactness of mean-field models, temporal and higher-order networks, adaptive/dynamic networks, and resilience of power networks. He has made significant contributions to understanding epidemic dynamics on complex networks, developing mathematical frameworks that connect approximate models with rigorous counterparts. Recent work emphasizes higher-order network structures, network inference from system-level data, and applications to public health and infrastructure resilience. His publication record demonstrates consistent output in top journals including Journal of Mathematical Biology, Bulletin of Mathematical Biology, and Physical Review E. The research shows a clear progression toward increasingly complex network structures, with recent publications focusing on higher-order interactions, temporal dynamics, and practical applications in epidemiology and infrastructure networks. His work bridges theoretical mathematics with real-world applications in disease control and network resilience. Professor Kiss actively supervises postdoctoral researchers and PhD students, with current advisees including Federico Cosimo Malizia (higher-order network contagion), Kevin Teo (shipping networks), and Yan Li (trade dynamics). His supervision spans mathematical theory, computational modeling, and data analysis across multiple domains. Leverhulme Trust grant RPG-2017-370: Bayesian Inference and Approximations in High-dimensional Network Models Network Science Institute Boston funding for higher-order network research EPSRC grant EP/H001085/1 for information diffusion modeling He leads the Systems Processes and Networks Lab (SPAN Lab), which develops mathematical frameworks for complex networked systems. The lab focuses on both theoretical advancements in network science and practical applications to epidemiology, neuroscience, and infrastructure networks. Current projects include developing tools for sequential temporal network analysis, understanding failure prediction in utility networks, and disentangling contact network structure effects on system-level outputs.

Panagiotis Manoliosمشاهده پروفایل

استاد

• Formal Methods
• Program Verification
• Theorem Proving
• +۵ مورد دیگر

Panagiotis (Pete) Manolios is a Professor in the Department of Computer Science within Northeastern University's College of Engineering in Boston. He leads the Northeastern University Formal Methods (NUFM) research group and maintains an active research program with multiple current PhD students. His primary affiliation is with the College of Computer Science (CCS) at Northeastern University, where he holds a tenured faculty position. Manolios' research focuses on formal methods with particular emphasis on program verification, theorem proving, and safety analysis of systems. His work spans several key areas including floating-point program analysis, resource-aware program verification, protocol verification, and safety-critical systems. He has developed significant tools and methodologies such as ACL2s (a powerful theorem prover) and pioneered approaches for analyzing numeric stability in compiler optimizations. His recent publications demonstrate substantial activity in formal verification, with particular focus on numeric stability analysis, invariant discovery through gamification, and model-based safety analysis of complex system architectures. These works reflect a consistent research trajectory toward making formal verification more practical and applicable to real-world systems, especially those with safety-critical requirements. Manolios has received recognition through multiple NSF grants including the SaTC: CORE: Medium collaborative project on bridging the gap between protocol design and implementation. His work on confidentiality and integrity of deep neural networks represents cutting-edge research at the intersection of formal methods and AI security. He has successfully mentored numerous PhD students who have gone on to positions at major technology companies including Google, Facebook, Intel, and MathWorks. His students' dissertations cover diverse topics within formal methods, from rank-polymorphic programming languages to resource-aware program analysis. Manolios directs several significant research projects including ACL2s (a powerful theorem prover), CID: Confidentiality and Integrity of Deep Neural Networks, Compilation-Dependent Security Properties of Software, and Platform Dependencies of Floating-Point Programs. These projects address fundamental challenges in making formal verification more practical and applicable to real-world systems.