جست‌وجوی استاد و پژوهشگر

• Building HVAC Systems
• Renewable Energy
• Control Systems
• +۶ مورد دیگر

Dr. Yaoyu Li is a Professor of Mechanical Engineering at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a PhD from Purdue University (2004), an MSc from the University of Saskatchewan (1997), and a BSc from Tsinghua University (1992). His research focuses on energy efficiency, renewable energy systems, and advanced control strategies for building HVAC, wind energy, and electric vehicles. He has held academic positions at UTD since 2011, including Associate Professor (2011–2018) and Professor (2018–present), and previously at the University of Wisconsin-Milwaukee (2004–2011). Research Interests: Dr. Li specializes in model-free control for HVAC systems, wind turbine control, and energy management for electric vehicles. His work emphasizes dynamic modeling, self-optimizing control, and fault diagnosis. Key areas include: - Building-to-grid integration and demand response - Offshore floating wind turbine stabilization - Battery diagnostics and worthiness assessment - Photovoltaic system optimization Publications: Over 70 peer-reviewed articles, including work on extremum seeking control, wind farm optimization, and electric vehicle thermal management. Recent trends focus on smart community energy systems, artificial muscle-based control for offshore turbines, and data-driven optimization methods. Awards: Recipient of the ASME DSCC 2020 Best Paper Award, Honda Initiation Grant (2007), and multiple research expenditure recognitions. He has advised numerous graduate students and collaborated with industry partners like Johnson Controls and Eaton Corporation. Teaching and Service: Taught courses including Systems and Controls, Wind Energy, and Heat Transfer at UTD. Served as Associate Editor for MecharAtronics and IEEE/ASME Transactions on Mechatronics . Active in professional organizations including ASME, IEEE, and AIAA.

Floris J.F. van Rielمشاهده پروفایل

پژوهشگر

• Applied Physics
• Nanostructures
• Magnetoelectronics
• +۶ مورد دیگر

Floris J.F. van Riel is a Researcher at Eindhoven University of Technology , affiliated with the School of Applied Physics and Science Education and Physics of Nanostructures department. His work intersects Magnetoelectronics , Exchange Bias , and Thin Films , with a focus on laser-driven magnetic manipulation. Research Themes : Field-free magnetic switching, spintronic sensor design, antiferromagnetic domain imaging, and femtosecond laser pulse interactions with nanostructures. Publications in npj Spintronics (2024) and Communications Materials (2025) highlight his contributions to: Laser-induced reversal of exchange bias Device fabrication for enhanced magnetoresistance Material engineering in thin-film nanostructures His work aligns with Magnetoelectronics and Nanotechnology , advancing optical switching and magnetic sensor functionality. No scientific awards or student advisement details are mentioned in the provided texts. Research collaborations include B. Koopmans and D. Leitao as co-authors.

• Neuroscience
• Machine Learning
• Control Theory
• +۴ مورد دیگر

Dr. LIU Quanying is an Associate Professor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech), where she has been a faculty member since September 2019. She serves as the Principal Investigator of the Neural Computing and Control Laboratory (NCC lab) and is a doctoral supervisor. Prior to joining SUSTech, she earned her PhD in Biomedical Engineering from ETH Zurich and conducted postdoctoral research at Caltech. Education: PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Research Interests: Dr. Liu’s research integrates neuroscience, machine learning, and control theory. Her work focuses on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for neuroscience, and optimization techniques for neuromodulation (tES, TMS). She has developed high-density EEG source localization algorithms and data-driven brain network modeling frameworks, aiming to enhance precision in neural stimulation and control. Scientific Awards: The New Brain 30 (2023) AAIC Travel Award (2019) Estes Stars Award (2018) 深圳市孔雀人才计划C类 Laboratory and Team: As the PI of the NCC lab, Dr. Liu leads a team focused on machine learning algorithms, neurocomputational modeling, and neurofeedback control. The lab actively recruits graduate students, postdocs, and visiting researchers, emphasizing interdisciplinary collaboration in neuroscience and AI.

• Control Systems
• Robotics
• Human-Robot Interaction
• +۴ مورد دیگر

Stefan Sosnowski is a Research Fellow at the Chair of Information-Oriented Control, Technical University of Munich (TUM). He has been affiliated with TUM since 2007, including roles as a research assistant and PhD candidate. His work spans Control Systems , Robotics , and Human-Robot Interaction . PhD in Electrical Engineering (2014), TUM Diploma Engineer (2007), TUM B.Sc. in Electrical Engineering (2005), TUM His research focuses on Data-driven Control (e.g., Koopman Operator theory, Gaussian Processes), Human-Centered Control , and Bio-inspired Design for autonomous systems. Recent publications emphasize learning-based control frameworks and stability analysis for nonlinear systems. Notable projects include SeaClear2.0 , CO-MAN , and ReHyb . He coordinates external theses at ITR and has an Erdős number of 4.

Giovanni Fantuzziمشاهده پروفایل

استادیار

• Optimization
• Dynamical Systems
• Fluid Mechanics
• +۴ مورد دیگر

Giovanni Fantuzzi serves as a W1 Professor (equivalent to Assistant Professor) in the Department of Mathematics at Friedrich-Alexander University Erlangen-Nuremberg. He leads research within the FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics under the Alexander von Humboldt Professorship framework, holding office in Room 03.318 with contact details including giovanni.fantuzzi@fau.de and +49 9131 85-67134. His educational background includes a PhD and Master of Engineering in Aeronautics from Imperial College London, supplemented by a research position in Engineering Science at the University of Oxford during his doctoral studies. Key academic milestones are documented through his ORCID, Google Scholar, and LinkedIn profiles. Fantuzzi's research program integrates mathematical analysis with computational optimization to solve nonlinear differential equations, focusing on deriving a priori scaling laws for heat transport and developing provable numerical schemes for PDE-constrained optimization. His methodology bridges convex optimization, polynomial optimization, and dynamical systems theory, with recent applications extending to transformer neural networks and sentiment analysis through hardmax mechanisms. Current teaching includes Data-driven methods for dynamical systems and Polynomial optimization and applications for WS 24/25. Analysis of his 15 most recent publications reveals dominant trends in fluid mechanics (particularly convection and heat transfer), polynomial optimization techniques, and data-driven dynamical systems analysis. His work consistently applies convex optimization frameworks to derive rigorous bounds in physical systems while expanding into machine learning applications like transformer model analysis. Geophysical Fluid Dynamics Fellowship at WHOI (2015) EPSRC Doctoral Prize Fellowship (2018) Imperial College Research Fellowship Fantuzzi's research program is supported by prestigious fellowships including the Imperial College Research Fellowship and EPSRC Doctoral Prize. His academic service includes organizing the FAU MoD Lecture & Workshop on AI for maths and maths for AI (June 2025) and co-hosting the #MLPDES25 Machine Learning and PDEs Workshop. He actively supervises research within the FAU DCN-AvH group, focusing on polynomial optimization applications in dynamical systems and PDEs. As core faculty in the FAU DCN-AvH Chair, Fantuzzi collaborates within a multidisciplinary team specializing in dynamics, control, machine learning, and numerical methods. The group maintains strong international connections through workshops like the Oberwolfach Seminar on Polynomial Optimization for Nonlinear Dynamics and participates in conferences including CIN-PDE and Nečas Seminar on Continuum Mechanics, driving innovation at the intersection of mathematics and computational physics.

Ramen Ghoshمشاهده پروفایل

پژوهشگر

• Complex Systems
• Ergodicity
• Control Theory
• +۵ مورد دیگر

Dr. Ramen Ghosh is a Researcher in the Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) group at Atlantic Technological University, Sligo, where he has been working with Dr. Marion McAfee since July 2022. His research explores how complex systems behave when randomness, interaction, learning, and control intersect, with special focus on ergodicity principles. His academic background includes: PhD in Electrical Engineering, University College Dublin, Ireland (2018-2023) Master of Technology in Mathematics and Computing, Indian Institute of Technology Patna, India Master of Science in Mathematics, Chennai Mathematical Institute, India Bachelor of Science in Mathematics (Honours), Ramakrishna Mission Vidyamandira Belur Math, University of Calcutta, India Dr. Ghosh's research centers on ergodicity—the concept that a system's long-run behavior becomes independent of its initial state—and how this principle can fail, emerge, or be shaped through control and learning. His work spans nonlinear systems, iterated function systems, dynamic mode decomposition, and applications in power grids, environmental systems, and materials science. He approaches complex system behavior through the intersection of randomness, interaction, learning, and control mechanisms. His publication record shows consistent growth with 1 article in 2022, 4 in 2023, 1 in 2024, and 1 in 2025. His research demonstrates strong interdisciplinary connections between theoretical mathematics and practical applications across environmental science, materials engineering, and control systems. The fingerprint analysis of his work reveals significant contributions to Nonlinearity, Iterated Function Systems, Dynamic Mode Decomposition, and Stable State mathematics. Dr. Ghosh has teaching experience including MATH09010 - Introduction to Mathematical and Computational Modelling at Atlantic Technological University (2022), and multiple offerings of EEEN30150-Modelling and Simulation and EEEN30020-Circuit Theory at University College Dublin (2019-2021). His research activities include presentations on ergodicity, predictability, fairness, and control for societal-scale challenges.