جست‌وجوی استاد و پژوهشگر

Alexandre Mauroyمشاهده پروفایل

استادیار

• Dynamical Systems
• Control Theory
• Operator Theory
• +۳ مورد دیگر

Alexandre Mauroy is an Assistant Professor at the University of Namur (UNamur) in the Department of Mathematics and the naXys center. He holds a Ph.D. in Applied Mathematics (2011) and a Master's in Aerospace Engineering (2007) from the University of Liège. His research focuses on dynamical systems, control theory, operator theory, and networks, with applications to nonlinear systems and mathematical biology. Key awards include the BELSPO return grant (2013), BAEF fellowship (2011), and FNRS fellowship (2007). Mauroy's work emphasizes the Koopman operator framework for analyzing nonlinear systems, including global stability, spectral identification, and data-driven methods. He leads projects like KOMUDYS (Koopman operator techniques for multiscale systems) and KORNET (nonlinear network identification). His research spans topics such as regions of attraction estimation, switched systems, and urban traffic management through the AUTOMATic project. Education: Ph.D. in Applied Mathematics, University of Liège (2011) M.Sc. in Aerospace Engineering, University of Liège (2007) Key Projects: KOMUDYS: Koopman operator methods for multiscale systems AUTOMATic: Autonomous urban traffic management KORNET: Nonlinear network identification His contributions include advancing the Koopman operator's theoretical foundations and practical applications, with over 49 research outputs since 2008. He actively collaborates internationally and contributes to conferences, workshops, and editorial activities in systems and control theory.

Kevin K Linمشاهده پروفایل

استاد

• Dynamical Systems
• Computational Neuroscience
• Data-Driven Modeling
• +۳ مورد دیگر

Kevin K Lin is a Professor in the Department of Mathematics at the University of Arizona, where he maintains an active research program and teaching schedule including courses through Fall 2025. His academic profile shows he serves as a faculty member with responsibilities in both instruction and research, with office space in Math 606 and virtual availability via Zoom. He is actively involved in several research initiatives including the NSF Research Training Group in Data Driven Discovery, UQ Working Group, and Data & Dynamics Working Group. Professor Lin's research program centers on dynamical systems theory with applications to computational neuroscience and machine learning. His work bridges theoretical mathematics with practical applications in neural modeling and data science. Specific interests include data-driven modeling, model reduction techniques, computational neuroscience approaches, and exploring the connections between machine learning algorithms and dynamical systems theory. His research has evolved to increasingly focus on neural systems, cortical modeling, and the application of dynamical systems theory to understand brain function. The publication record reveals a clear progression from foundational work on dynamical systems and stochastic modeling toward increasingly neuroscience-focused applications. His recent work (2023-2025) shows strong emphasis on neural activity analysis, brain state detection, and cortical modeling, while maintaining connections to his core expertise in dynamical systems and data-driven approaches. The articles demonstrate interdisciplinary work spanning mathematics, neuroscience, and computational science with practical applications in understanding neural dynamics and brain function. Professor Lin serves on the editorial boards of two significant journals in his field: the SIAM Journal on Applied Dynamical Systems and the Journal of Computational Dynamics, indicating his standing within the mathematical and computational science communities. He appears to be actively mentoring students through the NSF Research Training Group and other working groups, though specific advisees aren't listed in the available materials. His involvement with the Data & Dynamics Working Group suggests ongoing collaborative research efforts with colleagues across disciplines. Professor Lin maintains laboratory or research group activities through the Data & Dynamics Working Group and UQ Working Group at the University of Arizona. His research environment appears to be highly collaborative, involving both mathematical theory development and applications to neuroscience problems. The working groups he participates in suggest an interdisciplinary research environment that brings together mathematicians, neuroscientists, and data scientists to tackle complex problems in neural dynamics and computational modeling.

Rishikesh Yadavمشاهده پروفایل

پژوهشگر

• Approximation Theory
• Optimization Theory
• Functional Analysis
• +۴ مورد دیگر

Rishikesh Yadav is a Postdoctoral Researcher at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, since July 2024. Previously, he held a postdoctoral position at the Namur Institute for Complex Systems (naXys) and the Department of Mathematics, University of Namur, Belgium (2022–2024). He earned his Ph.D. in Approximation Theory from Sardar Vallabhbhai National Institute of Technology Surat, India, in 2021 under Prof. Vishnu Narayan Mishra and Prof. Ramakanta Meher. His research focuses on the intersection of Approximation Theory, Dynamical Systems, and Koopman Operator Theory. Current work emphasizes Optimization Theory, particularly developing Frank-Wolfe algorithms for convex optimization on measure spaces, with applications in homotopy approaches and stochastic frameworks. He has also explored compressed sensing and l1-norm minimization problems. Key contributions include approximation studies using Bernstein polynomials for Koopman operators and advancements in Szász-Mirakjan-type operators. His publications span journals like Communications in Nonlinear Science and Numerical Simulation (2025), Mathematical Methods in the Applied Sciences (2024), and the Georgian Mathematical Journal (2022). He received the Kempe Foundation Fellowship (2024–2026) for research on iterative algorithms in regularized optimization. Notable engagements include speaking at SIAM Conference on Applications of Dynamical Systems (2023) and presenting at the International Conference on Nonlinear Analysis and Applications (2022). He is actively involved in academic communities, such as the naXys Research Day (2022), and plans to attend the ACOTCA 2025 conference in France.

Siem Jan Koopmanمشاهده پروفایل

استاد

• Time Series Econometrics
• Financial Econometrics
• Forecasting
• +۴ مورد دیگر

Siem Jan Koopman is a Full Professor of Econometrics at the Department of Econometrics, School of Business and Economics, Vrije Universiteit Amsterdam. He is also a Research Fellow at the Tinbergen Institute and holds a long-term Visiting Professor position at CREATES, University of Aarhus. His academic career includes positions at the London School of Economics and CentER (Tilburg University), with long-term visits at the US Bureau of the Census, European University Institute, and European Central Bank. Dr. Koopman earned his PhD in Statistics from The London School of Economics and Political Science between 1989 and 1992, with his degree awarded on March 30, 1992. Professor Koopman's research spans several interconnected areas of econometrics, with a particular focus on time series analysis. His work centers on state space methods, score-driven time-varying parameter models (GAS models), and dynamic factor models. He has made significant contributions to the fields of financial econometrics, forecasting methodologies, and simulation-based estimation techniques. His research bridges theoretical developments with practical applications across economics, finance, and climate science, demonstrating the versatility of econometric methods in addressing complex real-world problems. Koopman's work often involves developing innovative statistical approaches to model time-varying parameters and extract meaningful signals from noisy data. His recent publications reveal a continued emphasis on advancing methodological frameworks for time series analysis, with increasing applications to climate modeling and environmental economics. There's a clear trajectory toward more sophisticated modeling of nonlinear dynamics, volatility, and interdependencies in economic and financial systems. His work increasingly bridges econometrics with climate science, particularly in analyzing CO2 emissions, climate variability, and the economic impacts of natural disasters. Journal of Applied Econometrics Distinguished Author Fellow of the Society of Financial Econometrics (SoFiE) Professor Koopman has supervised 30 PhD theses, demonstrating his significant contribution to mentoring the next generation of econometricians. His research has been supported by multiple competitive grants including the Labex Louis Bachelier grant from the Institut Europlace de Finance, research grants from the National Bank of Poland, and the VILLUM Visiting Professor Programme grant from the Velux Foundation. These grants have supported work on portfolio allocation, interest rate forecasting, and systemic risk analysis. As a Statistical Software Developer, Koopman has created and maintained important econometric tools including STAMP and SsfPack, which are widely used in academic and professional settings for time series analysis. These software packages implement state space methods and have become standard tools in the field of econometrics.

Pieter Koopmanمشاهده پروفایل

استادیار

• Functional Programming
• Embedded Domain-Specific Languages
• Clean Programming Language
• +۳ مورد دیگر

Pieter Koopman serves as an Assistant Professor in the Model Based Systems Department at the Institute for Computing and Information Sciences, Faculty of Science, Radboud University Nijmegen, Netherlands, focusing on functional programming and language-oriented software development. Research Focus: His work centers on the Clean programming language, embedded domain-specific languages, and automated software testing systems. He develops formal specifications using Clean functions for Gast (Generic Automatic Software Test-system), bridging theoretical functional programming with practical testing frameworks. Teaching Portfolio: He instructs key computer science courses including Object Orientation (1st-year Bachelor, with Sjaak Smetsers) New Devices lab (2nd-year Bachelor, with Rinus Plasmeijer) Advanced Programming (Master, with Rinus Plasmeijer) Compiler Construction (Master, with Rinus Plasmeijer and Peter Achten) utilizing Visual Paradigm through the Academic Partner Program. Laboratory Affiliation: Actively involved with the New Devices lab for practical computing education. Personal interests include archery, hiking, and Baritone Saxophone performance.

Alonso Marco-Valleمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Machine Learning
• Control Theory
• +۵ مورد دیگر

Alonso Marco-Valle is a Senior Robotics/AI Engineer at Figure AI, where he develops control algorithms for humanoid robots and machine learning systems. Previously, he held a Postdoctoral Research Fellow (Research Fellow) position at the Hybrid Systems Lab at the University of California Berkeley, focusing on safe autonomous systems with out-of-distribution monitoring. PhD : Robotics and Machine Learning, Max Planck Institute for Intelligent Systems and University of Tübingen, Germany MSc : Artificial Intelligence, Polytechnical University of Catalonia, Spain His research spans robotics, machine learning, and control theory, with specific interests in Bayesian optimization, Gaussian processes, model-based reinforcement learning, and safe learning frameworks. He has contributed to out-of-distribution detection, probabilistic dynamical models, and failure-aware optimization algorithms that balance exploration with risk. His recent publications focus on data-efficient learning, safety in autonomous systems, and physics-informed probabilistic modeling. Awards include the Rafael del Pino Excellence Fellowship, a highly competitive recognition for Spanish researchers. Collaborations include work with leading researchers such as Prof. Claire J. Tomlin (UC Berkeley), Prof. Sebastian Trimpe (Max Planck), Prof. José Miguel Hernández-Lobato (Cambridge), and Prof. Philipp Hennig (Max Planck), with research stays at institutions like Stanford, NASA ULI, and Meta AI.

• Machine Learning
• Representation Learning
• Geometric Deep Learning
• +۴ مورد دیگر

Siamak Ravanbakhsh is an Assistant Professor at McGill University’s School of Computer Science and a Canada CIFAR AI Chair at Mila. His research focuses on machine learning, particularly representation learning with geometric and probabilistic principles. He holds affiliations at both McGill and Mila, with offices in McConnell Engineering Building (MC 325) and Mila (I.9). Education: Ph.D. and M.Sc. from the University of Alberta (2015, 2009), B.Sc. from Sharif University. Previously held positions at UBC and a postdoc at Carnegie Mellon University’s Machine Learning Department. Research interests include geometric deep learning, probabilistic models, symmetry exploitation in AI, and applications in science. Recent work emphasizes equivariant neural networks and diffusion models for tasks like anomaly detection and goal-reaching in robotics. Publications span 20+ top venues (ICML, NeurIPS, ICLR), with notable contributions to equivariant architectures and physics-informed networks. His work has been recognized with awards for his dissertation and thesis. Advises over 20 students (Ph.D., M.Sc.) and collaborates on projects like EqR for reinforcement learning and crystal structure analysis. Active in teaching (COMP 588, 451, 551) and has contributed to grants in AI and astrophysics.

Aleksandar Stankovicمشاهده پروفایل

استاد پژوهشی

• Power Systems
• Power Electronics
• Electric Drives
• +۳ مورد دیگر

Aleksandar Stankovic is a Research Professor in the Department of Electrical and Computer Engineering at Tufts University. His research focuses on modeling, control, and estimation in electric energy processing, power electronics, power systems, and electric drives. He holds a Ph.D. from MIT (1992) and M.S./B.S. degrees from the University of Belgrade. His work emphasizes analytical and experimental approaches to dynamic power quality monitoring, control system design, and resilient power grid operations. Research contributions include dynamic phasor analysis, distributed state estimation, and hybrid modeling techniques for large-scale systems. Recent studies address inverter-dominated grids, transient stability, and data-driven predictive control methods. Key technical areas include: Power system resilience quantification Dynamic phasor-based modeling Multi-agent power flow analysis Model reduction for complex networks Nonlinear control strategies Publications highlight advancements in: Real-time power quality metrics Grid topology adaptation Adaptive compensation algorithms Secure distributed estimation No awards or grants explicitly listed in available materials.

Jun Liuمشاهده پروفایل

استاد

• Hybrid Systems
• Control Theory
• Robotics
• +۲ مورد دیگر

Jun Liu is a Professor and Canada Research Chair in Hybrid Systems and Control in the Department of Applied Mathematics at the University of Waterloo. He directs the Hybrid Systems Lab, focusing on control theory, robotics, and machine learning. His research integrates formal methods with data-driven techniques for nonlinear and stochastic systems. Key areas include neural Lyapunov functions, Koopman operators, and reinforcement learning for safety-critical applications. He co-authored books on formal methods and model-based reinforcement learning. Editorial roles include Automatica and Systems & Control Letters.

Javad Velniمشاهده پروفایل

استاد

• Control Systems
• Machine Learning
• Multi-agent Systems
• +۴ مورد دیگر

Javad Velni is a Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He directs the Velni Lab, conducting interdisciplinary research at the intersection of control theory, machine learning, and applied mathematics with applications in precision agriculture, autonomous vehicles, and industrial processes. Education: Ph.D., Mechanical Engineering, University of Houston, 2008 M.S., Electrical Engineering, University of Tehran, 2002 B.S., Electrical Engineering, Sharif University of Technology, 1999 Research focuses on developing analytical tools for complex systems through: Learning-based control and reinforcement learning for industrial applications Distributed model predictive control of multi-agent systems AI-driven solutions for precision agriculture including greenhouse automation and crop monitoring Cyber-physical security and robust control of connected vehicles Data-driven modeling of nonlinear systems using Bayesian neural networks Research publications demonstrate strong emphasis on model predictive control variants (distributed, learning-based, safe) with applications spanning agricultural technology, automotive systems, plasma processing, and multi-agent coordination. Recent work increasingly integrates Bayesian optimization, reinforcement learning, and physics-informed neural networks. Significant research grants include: Collaborative Research: CPS: Medium: Multi-Scale Sensing and Control for Optimizing Crop Production and Energy Efficiency (2024) GOALI/Collaborative Research: Control-Oriented Modeling of Natural Gas Engines (2022) CPS: DFG Joint: Perceptive Stochastic Coordination in Automated Vehicle Platoons (2020) Distributed Predictive Control of Microplasma Jet Arrays (2019) The Velni Lab develops distributed control algorithms and coverage strategies for heterogeneous multi-agent systems, with experimental applications in agricultural monitoring, vehicle platooning, and plasma-based biomaterial processing.

Sebastian Peitzمشاهده پروفایل

استاد

• Machine Learning
• Control Systems
• Computational Mathematics
• +۲ مورد دیگر

Sebastian Peitz is Professor of Safe Autonomous Systems at TU Dortmund University's Department of Computer Science since October 2024. His educational background includes a PhD in Mathematics from Paderborn University (2017), an MSc in Mechanical Engineering from RWTH Aachen (2013), and a BSc in Mechanical Engineering from RWTH Aachen (2011). His research focuses on physics-informed machine learning, data-driven modeling and control of complex systems, and multi-criteria optimization. These areas integrate computational mathematics with engineering applications to develop robust autonomous systems. Prof. Peitz's recent publications demonstrate strong emphasis on Koopman operator theory, reinforcement learning for PDE control, multi-objective optimization, and surrogate modeling. The computational methods span fluid dynamics, quantum systems, and mechanical design, frequently utilizing equivariant architectures and kernel techniques.

Elisa Martinez-Marroquinمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Computer Vision
• +۳ مورد دیگر

Professor Elisa Martinez-Marroquin holds a prestigious position at the University of Canberra as a Professor and Associate Dean Education within the Faculty of Education, Science, Technology and Mathematics. She has a multidisciplinary academic background, including degrees in Telecommunications Engineering, Electronic Engineering (PhD), and specialized training in Project Management, Entrepreneurship, and Computer Vision. Her career includes roles as Assistant Professor (1996–2001), Associate Professor (2001–2011), and leadership positions such as Head of Discipline, Director of Master’s programs, and Academic Director of Entrepreneurship initiatives. Educations: B.Eng in Telecommunications Engineering M.Eng and PhD in Electronic Engineering Executive Program on Management Skills (ESADE Business School) MSc in Computer Vision Master in Project Management Her research focuses on Learning Analytics , Computer Vision , Robotics , and Machine Learning , with applications in healthcare robotics, cybersecurity, and engineering education. She leads projects like the A Local Navigation Tool for Mental Health Care (MChart) (2022–2025), emphasizing technology transfer and innovation. Her recent publications highlight advancements in AI-driven medical interventions, rehabilitation robotics control systems, and ethical frameworks in engineering education. She has authored over 48 research outputs, including patents and peer-reviewed articles, and serves as an expert evaluator for EU ICT projects. Professor Martinez-Marroquin actively contributes to academic leadership, having directed degree programs such as the MSc in Entrepreneurship and Innovation and the Master of Research in Information Technology and Management. Her work bridges technical innovation with societal impact, emphasizing interdisciplinary collaboration and educational reform.

Jianguo Zhaoمشاهده پروفایل

دانشیار

• Robotics
• Control Systems
• Mechatronics
• +۳ مورد دیگر

Dr. Jianguo Zhao is an Associate Professor in the Department of Mechanical Engineering at Colorado State University, leading the Adaptive Robotics Lab. He focuses on creating adaptive robotic systems capable of shape, structure, and functional adaptability for diverse environments. His research is supported by NSF grants and has produced over 100 publications in journals like Nature Communications and IEEE Transactions on Robotics. Education: Ph.D. (2015, Michigan State University - Electrical Engineering), M.Eng. (2007, Harbin Institute of Technology - Mechatronics), B.S. (2005, Harbin Institute of Technology - Mechanical Engineering). Research Interests: Robotics and Control, Biologically Inspired Systems, Soft Robotics, and Adaptive Mechanisms. His work emphasizes modular robotic swimmers, tensegrity-based locomotion, and shape-morphing robots. Notable Achievements: NSF CRII Award (2018), Finalist for Best Student Paper (2024, 2018), Fitch H. Beach Award (2015). He has secured $4.3M+ in grants, including NSF and industry partnerships. Teaching: Courses include Fundamentals of Robotics, Mechatronics, and Biologically Inspired Robotics. Supervised over 30 graduate and undergraduate students, many advancing to academic and industry roles. Labs/Teams: Adaptive Robotics Lab at CSU, collaborating with institutions like Harvard and Penn State on NSF-funded projects.

Tobias Breitenمشاهده پروفایل

استاد

• Model Reduction
• Control Theory
• Optimal Control
• +۳ مورد دیگر

Prof. Dr. Tobias Breiten is a Professor of Mathematics at Technische Universität Berlin, affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on model reduction, control theory, and numerical methods for complex systems. Education: Diploma in Technomathematik (2009, TU Kaiserslautern), PhD in Mathematics (2013, Otto-von-Guericke University Magdeburg), Habilitation (2018, University of Graz). Roles: Associate Professor (2019-2020, University of Graz), Junior Professor (2020-2022, TU Berlin), Full Professor since 2022. Research interests include structure-preserving model reduction, nonlinear observer design, and optimal control of port-Hamiltonian systems. He leads projects on topics like thermoacoustic spectra and nonlinear control perspectives. Notable publications include works on H2 optimal rational approximation, balanced truncation methods, and Mortensen observer theory. His work often bridges numerical analysis and systems theory.

Guorong Wuمشاهده پروفایل

استادیار

• Neuroscience
• Machine Learning
• Alzheimer’s Disease
• +۵ مورد دیگر

Guorong Wu is an Assistant Professor at the University of North Carolina at Chapel Hill, holding joint appointments in the Department of Psychiatry and Computer Science within the UNC School of Medicine. His research focuses on computational neuroscience, machine learning, and neurodegenerative diseases, particularly Alzheimer’s disease. He develops advanced mathematical and computational models to understand brain functional dynamics, neural pathway propagation, and spatiotemporal progression of neuropathological events. Key research interests include graph theory applications to brain networks, optimal transport models for disease progression, and interpretable AI frameworks for medical diagnosis. His work spans interdisciplinary collaborations, integrating neuroimaging data with machine learning to uncover mechanisms of Alzheimer’s pathology, tau propagation, and amyloid-beta dynamics. His recent publications emphasize spatiotemporal modeling of neurodegenerative diseases, hub identification in brain networks, and the development of tools like PainFace for standardized mouse grimace analysis. While no specific grants or awards are mentioned, his contributions align with cutting-edge computational neuroscience and medical AI applications.