جست‌وجوی استاد و پژوهشگر

• Control systems
• Mechatronics
• Precision engineering
• +۳ مورد دیگر

Chun-Yi Su is a Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on advanced control systems, mechatronics, robotics, and precision engineering. He specializes in nonlinear control, hysteresis compensation, and adaptive systems applied to soft actuators, UAVs, and smart materials. Key research areas include: Control of dielectric elastomer actuators Fault-tolerant cooperative control of unmanned aerial vehicles (UAVs) Adaptive neural network-based control for nonlinear systems Data-driven approaches for power electronics and energy systems Recent work emphasizes: Soft robotics actuation mechanisms Fractional-order control strategies Resilient control under cyber-physical attacks Biomimetic robotic systems His lab develops innovative solutions for smart materials, mechatronic systems, and autonomous robotics. Applications span renewable energy systems, biomedical devices, and aerospace engineering. Active in collaborative research with industry partners.

Mudassir M. Rashidمشاهده پروفایل

استادیار

• Process Control
• Biomedical Engineering
• Pharmaceutical Engineering
• +۲ مورد دیگر

Mudassir M. Rashid serves as Assistant Professor of Chemical Engineering and Director of the Pharmaceutical Engineering Program at Illinois Institute of Technology's Armour College of Engineering. His work bridges chemical engineering fundamentals with cutting-edge applications in biomedical systems and pharmaceutical manufacturing. His academic credentials include: Ph.D. in Chemical Engineering from McMaster University, Hamilton, Canada (2016) B.Eng. in Chemical Engineering from McMaster University, Hamilton, Canada (2011) Dr. Rashid's research program centers on three interconnected domains: Data-driven modeling and control for biological/chemical systems, specializing in recursive identification and adaptive control of nonlinear time-varying processes with variable delays Diabetes technology development through metabolic modeling, simulation of physiological pathways, and automated closed-loop insulin delivery systems Pharmaceutical manufacturing innovation via process analytical technology for real-time monitoring, control, and optimization of drug production quality attributes His methodology integrates advanced control theory with practical healthcare and industrial applications, demonstrating particular expertise in artificial pancreas systems and pharmaceutical process optimization. Analysis of his 15 most recent publications (2016-2019) reveals a dominant research trajectory in adaptive control systems for diabetes management, constituting over 80% of his output. Key themes include glucose prediction algorithms, plasma insulin estimation techniques, and disturbance-handling strategies for meals/exercise within artificial pancreas frameworks. The remaining publications address economic model predictive control applications in chemical batch processes and metallurgical systems, showcasing methodological versatility across biomedical and industrial contexts. No scientific awards are documented in the provided materials. While specific advising activities and grant funding details are absent from the source text, his leadership of the Pharmaceutical Engineering Program indicates significant academic administrative responsibilities. Dr. Rashid directs the Pharmaceutical Engineering Program at Illinois Tech, though the source material does not specify dedicated laboratory facilities or research team structures beyond his program oversight role.

Uduak Inyang-Udohمشاهده پروفایل

استادیار

• Control Theory
• Graph Theory
• Physics-Guided Machine Learning
• +۵ مورد دیگر

Uduak Inyang-Udoh is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan, affiliated with the Autonomous & Intelligent Systems (AI-Sys) Lab. Her research focuses on control theory, graph theory, and physics-guided machine learning applied to data-rich advanced manufacturing, thermal systems, and energy storage. Education: PhD (Rensselaer Polytechnic Institute, 2021), BSc (University of Lagos, 2016) Research Areas: Controls, Energy, Manufacturing, Mechatronics & Robotics Email: udinyang@umich.edu Research interests integrate theoretical frameworks with practical applications in real-time optimal control, nonlinear system analysis, and additive manufacturing optimization. Her group has published extensively in control algorithms, thermal management systems, and data-driven industrial processes. Recent publications highlight advancements in neural co-state regulators, hybrid thermal control systems, and machine learning integration in droplet-based manufacturing. She emphasizes control theory applications to complex systems with input constraints and transient dynamics. Scientific Awards ASME Dynamic Systems and Control Division Rising Star Award (2022) ASME Rudolf Kalman Best Paper Award (2024) Mentoring style prioritizes student ownership of projects, collaborative peer mentoring, and structured progress reporting. Funding supports conference attendance (ACC, MECC) through travel grants and lab resources. Lab policies balance research productivity with vacation periods during academic breaks.

• Wireless Communication
• Network Optimization
• Machine Learning
• +۳ مورد دیگر

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

Ruggero Carliمشاهده پروفایل

دانشیار

• Control Systems
• Robotics
• Machine Learning
• +۶ مورد دیگر

Ruggero Carli is an Associate Professor at the Department of Information Engineering, University of Padova. His research focuses on control systems, robotics, and optimization, with emphasis on model-based reinforcement learning, distributed optimization algorithms, and energy systems. His work bridges theoretical advancements with real-world applications, including autonomous robotics, smart grids, and nonlinear control. Key contributions include physics-informed machine learning frameworks, ADMM-based distributed optimization methods, and MPC-driven control solutions for underactuated systems. Research interests include: Model-Based Reinforcement Learning for Robotics Nonlinear Model Predictive Control (NMPC) Distributed Optimization and ADMM Variants Energy Networks and Smart Grids Robot Dynamics and System Identification Recent publications emphasize: Continual learning for driver behavior analysis Physics-informed control for underactuated systems Robust optimization in unreliable networks Autonomous robotic manipulation with large language models His research integrates control theory with modern machine learning techniques, addressing challenges in edge computing, distributed systems, and real-time implementation.

Tommaso Grigolettoمشاهده پروفایل

استاد مدعو

• Quantum Mechanics
• Control Engineering
• Theoretical Physics
• +۳ مورد دیگر

Tommaso Grigoletto is an Adjunct Professor at the Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, and a Postdoctoral Fellow at the Department of Information Engineering, University of Padua. His research focuses on theoretical and numerical methods for complex dynamical systems, particularly quantum and classical probability models. He is part of the Quantum Dynamics, Information and Control group at the University of Padua, led by Prof. Francesco Ticozzi. Education: Ph.D. and Master's degrees in Information Engineering from the University of Padua. Research interests include quantum system control, model reduction techniques, and feedback stabilization strategies. He teaches 'Fundamentals of Automatic Controls' at Ca' Foscari University (2024-2025). His articles explore quantum model reduction, feedback control for quantum systems, and stochastic dynamics. He is actively mentoring students interested in control engineering and quantum systems simulations. Open to collaborative research and thesis supervision in these areas. Professional networks include the Quantum Dynamics group at Padua and academic affiliations with both universities. Engaged in open-source projects like an arXiv update bot for tracking research trends.

Mehrdad Ghandhari Alavijhمشاهده پروفایل

استاد

• Power System Dynamics
• Power System Stability and Control
• FACTS and HVDC Systems
• +۳ مورد دیگر

Mehrdad Ghandhari Alavijh is a Full Professor in Electric Power Systems at KTH Royal Institute of Technology. He leads research in power system dynamics, stability, and control, with a focus on FACTS (Flexible AC Transmission Systems), HVDC systems, and advanced control strategies. His work addresses challenges in grid reliability, renewable energy integration, and environmental sustainability. Education: Received M.Sc. (1995) and Ph.D. (2000) in Electrical Engineering from KTH. His research group develops innovative methods for secure, efficient power system operation, leveraging advanced monitoring and communication technologies. Research Interests: Power System Dynamics and Stability Transient Stability Assessment HVDC and FACTS Technologies Renewable Energy Integration Publications: Recent works focus on HVDC systems, battery storage, transient stability algorithms, and frequency control. His studies emphasize real-time monitoring and control strategies to enhance grid resilience against outages. Teaching: Supervises courses in power systems engineering, including degree projects in Electric Power Systems and Energy Innovation. Serves as an examiner and course responsible for multiple graduate programs.

Barry C. Sandersمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Information Theory
• Quantum Algorithms
• +۳ مورد دیگر

Barry C. Sanders is a Professor and Director of the Institute for Quantum Science and Technology at the University of Calgary. He holds additional roles as Lead Investigator of the Alberta Major Innovation Fund Project on Quantum Technologies, a Thousand Talents Chair at the University of Science and Technology China, and Vajra Visiting Faculty at the Raman Research Institute. His research focuses on quantum computing, quantum algorithms, and optical implementations of quantum information tasks. Sanders has a PhD from Imperial College London (1987) and has held faculty positions at Macquarie University and the Australian National University. Education: BSc (University of Calgary, 1984), PhD (Imperial College London, 1987) Affiliations: Institute for Quantum Science and Technology, Alberta Major Innovation Fund Project His research interests include quantum-limited measurement, nonclassical light, quantum cryptography, and quantum algorithm development. He has pioneered work on Schrödinger cat states, qudit systems, and quantum error correction. Awards include Fellowships from the Institute of Physics, Optical Society of America, and Royal Society of Canada. His work bridges theory and application, with contributions to quantum technology commercialization and interdisciplinary collaborations. Editor-in-Chief, New Journal of Physics Past leadership roles: President of the Australian Optical Society, Founding Co-Chair of the Canadian Association of Physicists Division His recent publications explore quantum speedup in machine learning, topological quantum walks, and fault-tolerant quantum computing architectures.

• Autonomous Systems
• Target Tracking
• Navigation
• +۵ مورد دیگر

Edmund Førland Brekke is a Full Professor at NTNU's Department of Engineering Cybernetics, specializing in autonomous maritime systems. He leads major projects like Autosea, Autosit, Autosight, and SFI Autoship. His research focuses on estimation theory, target tracking, SLAM, and collision avoidance for autonomous ships. He has held roles including Project Manager for key initiatives and was a Research Fellow at NUS's Acoustic Research Lab. Brekke teaches courses on sensor fusion and nonlinear state estimation. PhD: NTNU (2010), MSc: NTNU (2005) Research interests include sensor fusion, maritime navigation, and autonomous system safety. He supervises over 20 PhD students in topics like radar tracking, SLAM, and digital twins. His publications span collision avoidance algorithms, multi-sensor integration, and autonomous vessel control. He co-founded Zeabuz and collaborates on projects like Autoferry and AutoBarge. Recent work emphasizes maritime target tracking, scenario-based collision risk assessment, and sensor fusion for autonomous ferries. His contributions advance autonomous vessel autonomy and regulatory compliance with COLREGs.

Raktim Bhattacharyaمشاهده پروفایل

استاد

• Uncertainty Quantification
• Robust Control
• Nonlinear Systems
• +۳ مورد دیگر

Raktim Bhattacharya is a Professor in the Department of Aerospace Engineering at Texas A&M University, serving as Director of the Graduate Studies Program. He holds a Ph.D. in Aerospace Engineering from the University of Minnesota (2003) and a B.Tech from IIT Kharagpur (1996). His research focuses on uncertainty quantification, robust control, and nonlinear systems, with applications in aerospace systems design and control. Dr. Bhattacharya leads the Intelligent Systems Research Laboratory (ISRL), advancing algorithms for next-generation aerospace systems operating in uncertain environments. His work integrates optimal control, stochastic modeling, and data-driven methods to enhance system reliability and performance. Notable contributions include probabilistic robustness analysis, model validation frameworks, and sparse sensing architectures. Recent research trends emphasize optimal transport theory for state estimation, privacy-aware machine learning, and sensor-actuator co-design for resource-constrained systems. His publications span topics like UAV configuration optimization, LPV control frameworks, and invariant set estimation using physics-informed neural networks. Dr. Bhattacharya’s lab collaborates on projects involving tensegrity structures, cyber-physical systems, and space situational awareness. His work addresses challenges in hypersonic flight dynamics, celestial navigation, and resilient control under actuator degradation.

Douglas Allaireمشاهده پروفایل

دانشیار

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +۴ مورد دیگر

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.

Katy Craigمشاهده پروفایل

دانشیار

• Optimal Transport
• Nonlinear Partial Differential Equations
• Calculus of Variations
• +۳ مورد دیگر

Katy Craig is an Associate Professor in the Department of Mathematics at the University of California, Santa Barbara (UCSB), within the College of Letters and Science. Her research focuses on nonlinear partial differential equations, optimal transport theory, calculus of variations, and numerical analysis. She leads the NSF-funded CAREER project on Optimal Transport and Dynamics in Machine Learning and co-organized the Optimal Transport Graduate Summer School at UCSB in 2025. Dr. Craig's academic career includes roles as an Assistant Professor (2016-2023) and postdoctoral fellowships at UCSB and UCLA. She has been recognized with awards such as the 2023 Mochizuki Memorial Fund Award and the 2020 Hellman Faculty Fellowship. Her teaching spans courses in real analysis, optimal transport, and graduate-level topics in mathematics. Her research has been supported by grants from the NSF and UC system, and she actively mentors graduate and undergraduate students in interdisciplinary projects. Craig is also involved in conference organization, including leading workshops on optimal transport and machine learning, and contributes to academic service roles such as NSF panel participation and editorial work for journals. Key research themes include the development of numerical methods for optimal transport, applications to machine learning and physics, and the analysis of gradient flows in metric spaces. Her work bridges theoretical mathematics with computational and applied challenges, fostering collaborations across disciplines.

Cathy McLellanمشاهده پروفایل

دانشیار

• Interventional Cardiology
• Cardiac Program Management
• Medical Education
• +۳ مورد دیگر

Cathy McLellan is an Associate Professor and Interim Division Chair of Cardiology at Queen’s University, a role she has held since December 2024. She joined the Queen’s Cardiology Division in 2003 and served as Chair from 2017 to 2022. Dr. McLellan is also an interventional cardiologist at Kingston General Hospital, where she leads clinical roles in Percutaneous Coronary Interventions and Cardiac Program Medical Direction (2017–2020). Her work focuses on expanding regional cardiac care programs, such as the Southeastern Ontario Regional MI program, and overseeing postgraduate training in cardiology. Her expertise spans clinical cardiology, healthcare leadership, and medical education. She has held key administrative roles, including directing the Cardiology residency program and contributing to institutional governance through divisional leadership positions. While no specific awards are listed, her contributions to clinical practice and education are central to her professional profile. Dr. McLellan’s current focus includes advancing cardiac care protocols and maintaining operational excellence in hospital-based cardiology services. No formal research grants or student advisement details are provided in the text, though her administrative roles suggest involvement in training future clinicians.

Abhinav Kumar Singhمشاهده پروفایل

مدرس

• Power System Dynamics
• Power System Estimation and Control
• Large-scale Integration of Renewables
• +۴ مورد دیگر

Dr. Abhinav Kumar Singh is a Lecturer of Power Systems at the University of Southampton's School of Electronics and Computer Science since 2019. Previously, he held academic roles at the University of Lincoln (2017–2019) and Imperial College London (2015–2017 as Research Associate). His research focuses on real-time estimation and control of power systems, particularly addressing challenges posed by renewable energy integration. Key areas include power system dynamics, decentralized estimation/control methodologies, and modeling of renewable generation systems. Education: B.Tech from Indian Institute of Technology (IIT) New Delhi (2010), PhD in Electrical Engineering from Imperial College London (2015). He leads the 68-bus benchmark system development for IEEE PES and serves as editor for IEEE Transactions on Power Systems and Journal of Modern Power Systems and Clean Energy . Research Projects: PI of National Grid’s £480K 'Economic Ageing of Transformers' (2019–2021), Co-I on UK-China EPSRC-NSFC £780K 'Resilient Operation of Sustainable Energy Systems' (2020–2023). Teaching: Modules include Power System Dynamics, Power Electronics for DC Transmission, and Mathematics for Electrical Engineering. Awards: EPSRC Doctoral Prize (2015), IEEE PES Recognition Awards (2016, 2022), and The President’s Award for Outstanding Research Team (2016). Labs/Teams: Member of the Electrical Power Engineering group and Tony Davies High Voltage Laboratory. Active in IEEE PES Task Forces on Dynamic Estimation and Standard Test Cases.

Dominik Kopczynskiمشاهده پروفایل

پژوهشگر

• Lipidomics
• Mass Spectrometry
• Metabolic Pathways
• +۳ مورد دیگر

Dominik Kopczynski is a researcher affiliated with the Department of Analytical Chemistry at the Faculty of Chemistry. His work focuses on lipidomics, mass spectrometry, and the development of bioinformatics tools for lipid structure analysis and experimental standardization. He has contributed to initiatives like the lipidomics reporting checklist and Goslin nomenclature system. Active in organizing academic events such as the de.NBI Spring School for lipidomics bioinformatics and Keystone Symposia on lipid biology, he collaborates extensively with international researchers. Research Interests: Lipid metabolism, mass spectrometry-based analytical methods, lipid nomenclature standardization, proteomics, and computational tools for omics data analysis. His work bridges biochemical experimentation with bioinformatics to enhance reproducibility and interdisciplinary collaboration in lipidomics research. Activities: Organized 36 academic events including conferences, schools, and poster sessions. Notable contributions include co-organizing the 2025 de.NBI Spring School and contributing to the Keystone Symposia on lipid cellular function and disease. Engaged in both presenting research and fostering collaborative research networks. Labs/Teams: Collaborates within the Department of Analytical Chemistry and participates in international lipidomics consortia. His work often involves cross-disciplinary teams focused on integrating biochemical and computational approaches. Grants/Advising: While specific grants are not detailed, his extensive publication record and collaborative activities suggest involvement in funded projects related to lipidomics infrastructure development and analytical method standardization.