جست‌وجوی استاد و پژوهشگر

Yves Caudanoمشاهده پروفایل

مدرس

• Nonlinear optics
• Quantum optics
• Quantum weak measurements
• +۵ مورد دیگر

Yves Caudano is a qualified researcher at the FRS-FNRS and a lecturer in the Physics Department at the University of Namur, where he is attached to the Lasers and Spectroscopies research unit (UR-LLS). He holds active positions within the Philosophical Space of Namur, Namur Institute for Complex Systems, and Namur Institute of Structured Matter. His research spans multiple interdisciplinary domains connecting physics, philosophy, and complex systems theory. His educational background includes: Degree in Physical Sciences from FUNDP (1995) Doctor of Sciences from FUNDP (2000) Dr. Caudano's research focuses on the intersection of quantum mechanics and optics, with particular emphasis on nonlinear phenomena at interfaces. His work bridges fundamental quantum theory with practical applications in spectroscopy and materials science. He investigates how quantum weak measurements can reveal novel phenomena that conventional measurement approaches miss, particularly in surface physics contexts. His research program integrates theoretical frameworks with experimental validation, creating a robust foundation for advancing quantum measurement techniques and their applications. Analysis of his recent publications reveals a strong trajectory toward integrating quantum weak measurement theory with practical applications in materials science and quantum information processing. His work demonstrates increasing sophistication in connecting foundational quantum concepts with practical experimental implementations, particularly in surface nonlinear optics and quantum algorithm development. The research shows a clear progression from fundamental quantum measurement theory toward applications in quantum computing and advanced materials characterization. His scientific recognition includes: Prize of the Belgian Society of Physics for the best bachelor's thesis (1997) Winner of the Travel Grants competition of the French Community of Belgium (1996) Dr. Caudano serves in multiple leadership capacities including as Member of the board of the Belgian Physics Society (representative of UNamur, treasurer), Member of the Advisory Committee of the Higher Council for Scientific Integrity, and Member of the PE2 commission jury for FRIA Awards/Distinctions. He actively supervises graduate students and leads multiple research projects including STRQFAFPCIILEAC (2024-2026), ARTEMIS (2023-2027), and WeaM (2019-2023), securing substantial research funding across these initiatives. He leads the Lasers and Spectroscopies research unit (UR-LLS) where his team develops advanced techniques in sum-frequency generation spectroscopy and quantum measurement approaches. The group maintains strong international collaborations and regularly hosts visiting researchers, contributing significantly to the university's research profile in quantum physics and surface science.

• Nonlinear Optimization
• Trust Region Methods
• Filter Methods
• +۳ مورد دیگر

Dr. Caroline SAINVITU is an active researcher in the field of nonlinear optimization, with a primary focus on trust region methods and filter algorithm development. Her work bridges theoretical advancements in mathematical optimization with practical applications in numerical experimentation. Key research themes: Trust Region Methodology (100%), Filter Algorithm Design (100%), Unconstrained/Constrained Optimization (75% combined) Collaborations with prominent researcher Philippe Toint Specialization in convergence analysis and algorithm robustness Research Output Trends: Between 2006-2009, she developed innovative filter-trust region frameworks applicable to both unconstrained and bound-constrained optimization problems. Her publications demonstrate a consistent focus on improving algorithm efficiency through rigorous numerical experimentation. Professional Engagement: Participated in major conferences and workshops including INFORMS Annual Meeting, Sparse Days Meeting, and FNRS mathematical programming events. Currently involved in 19 documented research activities.

Francis Berghmansمشاهده پروفایل

استاد

• Photonics
• Optical Fibers
• Fiber-Optic Sensors
• +۶ مورد دیگر

Francis Berghmans is a Professor at the Faculty of Engineering, Vrije Universiteit Brussel (VUB), and serves as Vice Dean. He is affiliated with the Applied Physics and Photonics Department and leads the Brussels Photonics (B-PHOT) research group, which includes 70+ members. He earned his PhD in Applied Sciences (1998) and Civil Engineering (Electrotechnical) degree (1992), both from VUB. His research focuses on photonics , optical fiber sensors , and micro-structured optical fibers , with applications in nuclear environments , biomedical imaging , and structural health monitoring . His work has been funded by EURATOM, ESA, and Horizon 2020 programs, including projects like MYRTE, ACTPHAST 4R, and FINESSE. He has supervised 6 PhD students, 3 postdocs, and 1 associate professor. His recent publications analyze fiber-optic leakage detection , high-resolution crack sensing , and scintillation-based dosimeters , reflecting his expertise in advanced fiber technologies and laser dynamics . He is a SPIE Fellow and IEEE Senior Member , with leadership roles in European photonics initiatives. His team also participates in training programs like PhabLabs 4.0 and the Photonics Explorer kit distribution.

• Electromagnetics
• Finite Element Methods
• Magneto-Quasi-Static Problems
• +۵ مورد دیگر

Ruth Vazquez Sabariego is a Professor at KU Leuven in Belgium, with her office located at Kasteelpark Arenberg 10, box 2445, 3001 Leuven. She maintains an active research program in computational electromagnetics and power electronics with numerous recent publications and ongoing PhD supervision. Her research interests span Electromagnetics , Finite Element Methods , Magneto-Quasi-Static Problems , Power Electronics , Magnetic Components , and the application of Physics-Informed Neural Networks to electromagnetic problems. She has pioneered work in homogenization techniques for magnetic components, simulation of electromagnetic phenomena, and the application of machine learning to traditional electromagnetic problems. Analysis of her recent publications reveals a strong trend toward integrating machine learning with traditional electromagnetic simulation techniques, particularly physics-informed neural networks for solving magneto-quasi-static problems. Her work also shows consistent development of homogenization methods for windings and magnetic cores, with increasing focus on thermal-electromagnetic coupling in power electronics applications. Professor Sabariego actively supervises PhD students as both promotor and co-promotor, including Thomas Jochmans (2024), Michele Vescovo (2025), Jiaze Kong (2023), Sina Toghranegar (2023), and Ziqing Guo (2022). Her research group is involved in multiple significant projects including 'Magnetic components for on-board chargers of electric vehicles', 'Hosting capacity improvement of power electronic converters', and 'AI Development for topology optimisation of magnetics and cooling systems'. Her laboratory work focuses on electromagnetic field computation, particularly for power electronics applications. The research involves developing advanced numerical methods for simulating magnetic components, with recent emphasis on integrating machine learning techniques with traditional finite element approaches to improve computational efficiency while maintaining accuracy.

• Nonlinear Dynamics
• Chaos Theory
• Synchronization
• +۴ مورد دیگر

Gonzalo Marcelo Ramírez-Ávila is a researcher at the Namur Institute for Complex Systems (NICs), focusing on nonlinear dynamics, synchronization, and complex systems. His work spans theoretical and applied studies in chaotic oscillators, neuronal models, and robotic synchronization systems. Projects: Leading the Be-neXst project (2024–2026) on complex systems and synchronization. Participating in a visiting research program at Lodz University of Technology (March–April 2025). His research explores synchronization mechanisms in coupled oscillators, bifurcation phenomena in lasers, and chaotic behavior in biological and artificial systems. Recent publications highlight applications in robotics and neuroscience. Collaborations include interactions with researchers from Italy, Belgium, and Poland, emphasizing interdisciplinary approaches to complex systems.

Thibaut Septonمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Multimodal Interfaces
• Wearable Technology
• +۳ مورد دیگر

Thibaut Septon is a researcher affiliated with the Namur Digital Institute at the University of Namur . His work focuses on Human-Computer Interaction , Augmented Reality , and Wearable Technology , with recent publications on vehicular XR environments and multimodal interface development. Active in multimodal interaction and semantic search applications Key contributions to gesture recognition and data visualization in interactive systems His research spans Wearable Devices , Augmented Reality , and Interactive Computing from 2021 to 2025, emphasizing practical solutions for user interface challenges in emerging technologies. Activities include participation in conferences like ACM IUI , ESANN , and EICS from 2022 to 2025.

• Soft Robotics
• Micro-electromechanical Systems (MEMS)
• Fluidic Actuators
• +۴ مورد دیگر

Benjamin Gorissen is an Assistant Professor (tenure track) in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Science. He is a core member of LIMNI (KU Leuven Institute for Micro- and Nanoscale Integration), contributing to committees including the Council of the Faculty of Engineering Science and programme committees for Biomedical Engineering and Mechanical Engineering. His research focuses on interdisciplinary soft robotics, emphasizing fluidic systems, nonlinear actuators, and biomimetic designs. Key areas include pneumatic artificial muscles, origami-based robotic structures, metamaterials for programmable fluids, and medical simulators. His work integrates materials innovation with control systems to develop autonomous, intelligent soft machines. Gorissen leads multiple projects such as MORPHIS (mammary organoid platforms), origami-topology soft robots, haptic human extenders, and anti-inflammatory drug delivery systems. His publications frequently appear in high-impact journals ( Nature , Science Robotics ), exploring themes like embodied intelligence, fluidic oscillators, and metamaterial programming. He teaches courses in Micro-electromechanical Systems, Engineering Entrepreneurship, and integrated design projects, emphasizing practical innovation.

• Robotics
• Optimal Control
• Model Predictive Control
• +۵ مورد دیگر

Wilm Decré is an Associate Professor at KU Leuven, holding a prestigious BOF (Bijzonder OnderzoeksFonds) position in the Department of Mechanical Engineering within the Faculty of Engineering Science. He is a key member of the MECO Research Team, where he leads research in robotics, control systems, and motion planning. His work bridges theoretical control algorithms with practical robotic applications, particularly in optimal control and motion planning. His research interests include: Model Predictive Control (MPC) and optimal control for robotics Time-optimal motion planning and trajectory optimization Non-holonomic path planning algorithms Constraint-based robot programming Human-robot collaboration and safety Real-time implementation of control algorithms Prof. Decré's recent publications demonstrate significant contributions to making advanced control techniques computationally feasible for real-world deployment. His work addresses the gap between theoretical optimality and practical implementation constraints, with applications spanning industrial robotics, autonomous vehicles, and collaborative systems. His research shows a progression from theoretical foundations to increasingly practical applications with real-world impact. His notable scientific contributions include: ASAP-MPC: An asynchronous update scheme for online motion planning Efficient techniques for reducing computational complexity in optimal control Practical approaches for time-optimal MPC implementation Accelerated Reeds-Shepp algorithms for mobile robot path planning Integration of MPC with constraint-based robot programming for unstructured environments Prof. Decré actively supervises PhD students and has developed strong collaborations both within KU Leuven and internationally. His research is supported by the BOF position and appears to involve multiple industry partnerships given the applied nature of many projects. He maintains an active publication record in top robotics and control venues including IEEE Transactions on Robotics, IEEE Robotics and Automation Letters, and major conferences like ICRA and IROS. His laboratory, part of the MECO Research Team, focuses on experimental validation of control algorithms using various robotic platforms including drone systems, mobile robots, and industrial manipulators. The lab emphasizes both theoretical rigor and practical applicability of control solutions, with recent work showing validation on quadcopters navigating cluttered environments and scale model truck-trailers in structured lab environments.

• Robust Statistics
• Econometrics
• Time Series Analysis
• +۳ مورد دیگر

Christophe Croux is a full professor at the Faculty of Economics and Business (FEB) at KU Leuven and serves as the coordinator of the Operations Research and Statistics Research Group (ORSTAT). His research focuses on robust statistical methods, econometric modeling, and high-dimensional data analysis with applications in finance, marketing, and chemometrics. He has contributed extensively to the development of robust estimation techniques, sparse regression models, and volatility forecasting frameworks, often leveraging computational tools like R packages such as 'robustbase.' Key areas of research include robust correlation analysis, time series modeling (e.g., state space models, volatility spillovers), and machine learning applications for classification and prediction. His work emphasizes handling outliers and large datasets, with practical implications for financial risk management, credit scoring, and commodity market dynamics. Croux has published over 150 articles since 2015, spanning topics from robust canonical correlation to sparse vector autoregressive models. Notable contributions include the development of the robustbase R package, which provides essential tools for robust statistical analysis. His interdisciplinary approach bridges theoretical statistics with practical applications in economics and business analytics. He holds leadership roles in academic networks and committees, furthering the impact of his research in both academia and industry.

Karl Meerbergenمشاهده پروفایل

استاد

• Algebraic eigenvalue problems
• Algebraic model order reduction
• Preconditioning
• +۷ مورد دیگر

Karl Meerbergen is a Full Professor in the Department of Computer Science at KU Leuven, Faculty of Engineering Sciences. He leads research in numerical analysis and applied mathematics with a focus on eigenvalue problems, model order reduction, and computational linear algebra. He is a member of the Numerical Analysis and Applied Mathematics (NUMA) research unit and holds affiliations with multiple KU Leuven institutes including iSi Health, Leuven.AI, Leuven.AM, and the LGI Gravitation Institute. His research spans several key areas of numerical mathematics with emphasis on algebraic eigenvalue problems, algebraic model order reduction, preconditioning techniques, computational acoustics, tensor computations, exascale computing, generic programming, and parallel computing. His work bridges theoretical numerical analysis with practical applications in engineering and scientific computing. Analysis of his recent publications shows a strong focus on advanced numerical methods for eigenvalue problems, model order reduction techniques, and parallel computing approaches. His work frequently addresses challenges in large-scale scientific computing, with applications in structural dynamics, acoustics, and optimization problems. The research demonstrates increasing sophistication in handling nonlinear and parametric systems through rational approximation methods and specialized preconditioning techniques. Dr. Meerbergen actively contributes to the academic community through his teaching responsibilities and supervision of graduate students. His work has significant implications for computational science and engineering applications requiring efficient numerical solutions to complex mathematical problems.

Annick Sartenaerمشاهده پروفایل

استاد

• Optimization
• Nonlinear Programming
• Algorithms
• +۳ مورد دیگر

Annick Sartenaer is a Professor at the University of Namur, affiliated with the Department of Applied Mathematics and Complexity. She holds a Doctor of Science degree (1991) and an HDR (1999). Her research focuses on optimization algorithms, nonlinear programming, and numerical methods, with contributions to trust region methods, constrained optimization, and convergence analysis. She leads and collaborates on projects such as MASTERMIND2 (2021–2026), focusing on meta-model-based optimization, and has extensive experience in complex systems and interdisciplinary research. Her work spans theoretical advancements and practical applications in fields like environmental modeling and digital communication. Key projects include the development of optimization algorithms for medical imaging (2013–ongoing) and data assimilation techniques (2011–ongoing). She actively participates in academic activities, including conferences, workshops, and international collaborations. Her research aligns with sustainable development goals, particularly in education and complex problem-solving. Annick has supervised 83 academic works and contributed to over 40 research outputs. She is involved in academic governance and international partnerships, including a visiting appointment at the Institut Supérieur de Pédagogie de Bukavu (2016). Her work emphasizes bridging theoretical research with real-world applications, particularly in resource management and technological impact analysis.

Guillaume Crevecoeurمشاهده پروفایل

دانشیار

• Dynamical Systems
• Nonlinear Optimal Control
• Model-Based Optimization Algorithms
• +۴ مورد دیگر

Guillaume Crevecoeur (born 1981) serves as Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Electrical Energy, Metals, Mechanical Constructions, and Systems. He leads research at the Electrical Energy Laboratory (EELAB) and is an affiliate member of Flanders Make, the strategic research center for manufacturing industry. His international collaborations include membership in scientific steering committees and prior visiting researcher positions at Technical University Ilmenau and Physikalische Technische Bundesanstalt in Berlin. Crevecoeur earned his MSc (2004) and PhD (2009) in Engineering Physics from Ghent University, followed by a postdoctoral fellowship with Research Foundation Flanders (FWO-Flanders). His research spans dynamical systems modeling, optimization, and control with foundational work in model-based algorithms, inverse problems, and nonlinear optimal control. Current strategic focus areas include machines and motion systems, particularly airborne wind energy applications and robotics. Analysis of his 2022-2025 publications reveals dominant trends in physics-informed machine learning for control systems , with significant contributions to reinforcement learning for underactuated systems, tactile sensing in robotics, and computational fluid dynamics for renewable energy. His work consistently integrates model-based optimization with data-driven approaches, especially in wind turbine control and aerial robotics co-design. Best Paper Award at 22nd International Conference on System Theory, Control and Computing (2018) As Principal Investigator, Crevecoeur directs research funded through FWO-Flanders and industry partnerships with Flanders Make. His lab focuses on mechatronic systems development, particularly in robotic manipulation and renewable energy conversion. Current projects include airborne wind energy system optimization, collision-tolerant robotics, and digital twin implementations for industrial machinery. He actively contributes to international scientific steering committees and serves as academic advisor for graduate students in electrical energy systems. Crevecoeur's research group operates within EELAB's infrastructure, utilizing high-performance computing resources for computational fluid dynamics simulations and experimental facilities for mechatronic system validation. The team collaborates with industry partners through Flanders Make on industrial automation challenges, with recent work focusing on real-time control for high-speed manufacturing processes and adaptive energy management systems.

• Machine Learning
• Applied Mathematics
• Nonlinear Systems
• +۶ مورد دیگر

Mariya Ishteva is an Associate Professor at the KU Leuven , affiliated with the Faculty of Engineering Technology and the Department of Computer Science . Her work bridges applied mathematics, machine learning, and industrial sustainability. Leading projects in tensor methods , neural network compression , and nonlinear system identification Developing thermochemical data-driven control systems for Waste-to-Energy processes Contributing to decoupling multivariate functions in signal processing and control theory Her recent publications focus on integrating tensor networks with machine learning for industrial combustion optimization and multivariate function analysis. She collaborates extensively with engineering teams in sustainable energy systems and process control . She teaches courses in Big Data Analysis , Mathematical Modeling , and Applied Mathematics , contributing to educational programs at the Geel Campus.

Jean-Jacques Strodiotمشاهده پروفایل

استاد

• Optimization
• Equilibrium Problems
• Variational Inequalities
• +۵ مورد دیگر

Jean-Jacques Strodiot is a Professor in the Department of Mathematics at the University of Liège's Faculty of Science. With a research career spanning over 45 years from 1977 to 2022, he has established himself as a leading expert in optimization theory and numerical methods. His research focuses on equilibrium problems, variational inequalities, and numerical optimization techniques. Key areas include algorithms development, convergence analysis, Hilbert space methods, and regularization techniques. His work bridges theoretical mathematics with practical computational approaches, making significant contributions to both pure and applied mathematics. Analysis of his 76 research outputs reveals a consistent focus on developing and analyzing algorithms for equilibrium problems and variational inequalities. His recent work (2017-2022) emphasizes strongly convergent algorithms, adaptive regularization parameters, and innovative stepsize rules that improve numerical performance. The research shows strong interdisciplinary connections between optimization theory, functional analysis, and computational mathematics. As Principal Investigator on 11 research projects, Professor Strodiot has secured substantial research funding. His projects include 'Duality and characterization of optimality for equilibrium problems,' 'Approximate solutions and Lagrangian duality for semi-infinite mathematical programming problems,' and 'Extension of proximal methods to equilibrium problems.' Professor Strodiot has maintained strong international collaborations, particularly with Vietnamese institutions. His teaching missions to Vietnam National University in Ho Chi Minh City demonstrate his commitment to academic exchange. He has supervised 90 students throughout his career, contributing significantly to mathematical education and research training.

• Macroeconomic Forecasting
• Machine Learning
• Econometrics
• +۶ مورد دیگر

Jad Beyhum is an Associate Professor in Economics at KU Leuven, affiliated with the Faculty of Economics and Business and its Economics Research Group. He specializes in econometrics and statistics, focusing on high-dimensional data analysis, instrumental variable methods, and survival analysis techniques. Current research themes include high-dimensional econometrics (e.g., LASSO-type procedures, factor models) and instrumental variables techniques (e.g., dynamic treatments, censored data). His methodological contributions target econometric challenges in nonlinear panel data, competing risks models, and duration outcomes. Key applications span corporate bankruptcy prediction, program evaluation, and macroeconomic forecasting. Recent publications highlight innovations in instrumental variable estimation under censoring, testing treatment effect homogeneity, and combining machine learning with traditional econometric frameworks. He actively supervises projects on topics like "Beyond Instrumental Variables" and "New methods to control unobserved heterogeneity" . Selected project leadership roles: "Predicting bankruptcy with machine learning methods" (Promotor, 2023-2027) "Partial identification methods for survival analysis" (Co-promotor, 2023-2027)