جست‌وجوی استاد و پژوهشگر

Thomas A. Berruetaمشاهده پروفایل

پژوهشگر

• Robotics
• Control Theory
• Active Matter
• +۳ مورد دیگر

Thomas A. Berrueta is a Researcher in the Department of Computing & Mathematical Sciences. His research focuses on advanced robotics, control systems, and active matter, with a particular emphasis on microrobotics, swarm behavior, and data-driven methodologies. He explores topics such as emergent phenomena in robotic systems, optimization of locomotion, and the integration of memristor circuits in robotic applications. His research interests span multiple disciplines, including robotics, control theory, and active matter. He investigates how robotic systems can achieve autonomous behavior through principles like symmetry breaking and collective dynamics. Key areas include the design of microrobots, the development of reinforcement learning algorithms for control, and the application of Koopman operators in active learning frameworks. His work also delves into the stabilization of robotic systems and the optimization of their operational efficiency across various scales. Recent publications highlight a focus on cutting-edge robotics and control methodologies. Trends include the exploration of emergent behaviors in robotic swarms, the application of thermodynamic principles to robotics, and the use of data-driven approaches for motion analysis and control. His work also addresses challenges in micromanipulation and the design of soft robotic systems capable of autonomy across different scales. No scientific awards mentioned. There are no listed advisees or grants associated with Thomas A. Berrueta in the provided information. No lab or team affiliations are mentioned in the provided content.

Youcheng Sunمشاهده پروفایل

پژوهشگر

• AI Safety
• Security
• Automated Reasoning
• +۴ مورد دیگر

Youcheng Sun is a researcher at The University of Manchester, affiliated with the Systems and Software Security group, Centre for Digital Trust and Society, Centre for Robotics and AI, and Autonomy and Verification Network. Previously, he held a Lecturer position at Queen's University Belfast and conducted postdoctoral research at the University of Oxford. He earned his PhD from Scuola Superiore Sant'Anna. His research focuses on AI Safety , Security , and Automated Reasoning , with contributions to trustworthy AI, formal verification, embedded systems, and robotics. His work has been funded by Google and the Ethereum Foundation. He has led projects such as SECT-AIR and AUTOSAC, and contributed to the EU H2020 SAFURE project on safety/security assurance in mixed-critical systems. His publications span top-tier venues like IEEE S&P, ICSE, NeurIPS, and IROS. Awards: Fellow of the Higher Education Academy (FHEA) Advising: Supervising PhD students in AI/Security domains (e.g., funded projects on LLMs in social recommendations) Labs/Teams: Member of interdisciplinary networks advancing robotics, AI ethics, and digital trust.

• Environmental Science
• Computational Science
• Biogeochemistry
• +۳ مورد دیگر

Jonathan Sauder is a Researcher and Doctoral Assistant at EPFL, affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Institute of Infrastructure and Environment (IIE). He is part of two laboratories: the Computational Science Laboratory for Environment and Earth Observation (ECEO) in Sion and the Biogeochemistry Laboratory (LGB) in Lausanne. His research focuses on environmental science, computational methods applied to coral reef ecosystems, deep learning, and signal processing. He is enrolled in the doctoral program in Civil and Environmental Engineering at EPFL. His work bridges environmental science and artificial intelligence, with notable contributions to coral reef monitoring using 3D mapping, semantic segmentation of benthic environments, and self-supervised learning techniques for underwater imaging. He has also explored structured sparse matrices in signal recovery and neural-augmented algorithms for iterative processes. His research locations include EPFL Valais Wallis (Sion) and the EPFL main campus in Lausanne, with labs located at ALP 2 011 (Sion) and GR C2 524 (Lausanne). He collaborates with interdisciplinary teams in environmental engineering and computational science. Professional activities include membership in the EDOC doctoral school (EDCE) and engagement in international conferences. His research themes emphasize scalable solutions for environmental monitoring and advancing deep learning applications in geoscience and marine biology.

Feiran Zhaoمشاهده پروفایل

پژوهشگر

• Data-Driven Control
• Adaptive Control
• Reinforcement Learning
• +۲ مورد دیگر

Feiran Zhao is a Researcher at the Institute of Automatic Control, part of the Department of Mechanical and Process Engineering at ETH Zürich. He holds a B.S. in Control Science and Engineering from Harbin Institute of Technology (2018) and a Ph.D. from Tsinghua University (2024). His research focuses on data-driven control, adaptive control, reinforcement learning, and their applications in engineering systems. Zhao is currently a postdoc under Prof. Florian Dorfler at ETH's Automatic Control Lab. Research interests span topics like policy optimization for LQR systems, quantized feedback control, and model predictive control acceleration. His work bridges machine learning and classical control theory, with applications in robotics, power systems, and aerospace engineering. His publications (2019–2025) explore theoretical foundations of policy gradient methods, convergence analysis, and practical implementations in autonomous systems. Though no awards are explicitly listed, his active research in high-impact areas suggests potential recognition. As part of the Automatic Control Lab, Zhao collaborates on projects involving data-enabled control strategies and real-world system applications. No student advisees are currently listed.

François Rivestمشاهده پروفایل

دانشیار

• Artificial Intelligence Engineering
• Machine Learning Systems
• Computational Neurosciences
• +۳ مورد دیگر

Dr. François Rivest is an Associate Professor at Queen’s University, affiliated with the Department of Biomedical and Molecular Sciences (School of Medicine, Faculty of Health Sciences). He also holds cross-appointments in the School of Computing (Faculty of Arts and Science) and is a member of the Centre for Neuroscience Studies. His research focuses on machine deep reinforcement learning and animal interval timing, aiming to bridge computational neuroscience insights with advanced AI systems. He leads the Natural and Artificial Adaptive Intelligent Systems Laboratory. Education: PhD in Computer Science (Computational Neuroscience/Machine Learning) – Université de Montréal (2010) MSc in Computer Science – McGill University (2002) BSc in Mathematics and Computer Science – McGill University (2000) Research Interests: Dr. Rivest’s work integrates principles of animal learning, particularly reward-based systems and temporal cognition, into machine learning algorithms. Key areas include: Reinforcement learning frameworks inspired by dopamine signaling Drift-diffusion models for interval timing Adaptive representation construction in real-time systems Applications in smart homes, robotics, and neuroscience Publications: Over 20 peer-reviewed articles (2001–2022) span computational neuroscience, reinforcement learning, and machine learning systems. Recent work includes modeling interval timing dynamics and applying reinforcement learning to smart home systems. Lab & Affiliations: As Principal Investigator of the Natural and Artificial Adaptive Intelligent Systems Lab, he explores interdisciplinary AI applications. Collaborations include the Royal Military College of Canada (2010–present) and Queen’s University’s Center for Neuroscience Studies (2011–present).

Matthew Haleمشاهده پروفایل

دانشیار

• Control Systems
• Optimization
• Multi-Agent Systems
• +۳ مورد دیگر

Matthew Hale is an Associate Professor in the Department of Electrical and Computer Engineering at Georgia Tech. He holds affiliations with the CORE Lab and focuses on control systems, optimization, robotics, and privacy-preserving algorithms. His work emphasizes multi-agent systems and practical applications in autonomous systems. Education: BSE in Electrical and Computer Engineering from the University of Pennsylvania (2012), PhD in Electrical and Computer Engineering from Georgia Tech (2017). Research Interests: His research bridges theoretical control systems with applied robotics, emphasizing distributed optimization, privacy in networked systems, and deception-resistant decision frameworks. Key areas include multi-agent coordination, privacy-aware control, and safe autonomous operations. Publications Trends: Recent work focuses on differential privacy in optimization, hybrid systems models for control, and scalable algorithms for multi-agent systems. Notable topics include privacy-preserving epidemic modeling and autonomous satellite control. Awards: AFOSR YIP (2023), ONR YIP (2022), AFRL Summer Faculty Fellowship (2020), NSF CAREER Award (2019). Advising & Grants: Leads research teams in multi-agent robotics and privacy engineering. Active in securing grants for foundational control theory and applied robotics projects. Labs/Teams: Core contributor to Georgia Tech’s CORE Lab , advancing interdisciplinary work in control systems and robotics.

• Signal Processing
• Communication Systems
• Wireless Communications
• +۵ مورد دیگر

Assoc. Professor Jun Tong is affiliated with the School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong. He holds a PhD from City University of Hong Kong and specializes in signal processing for communication systems and instrumentation, focusing on robust, low-complexity algorithms for high-dimensional signals and imperfect models. His research enhances spectrum/energy efficiency in challenging environments. Research Interests: Signal processing applications in communication systems, MIMO and 5G/6G technologies, optical systems, machine learning for signal processing, and sensor networks. He has published extensively in top-tier journals like IEEE Transactions and conferences such as ICC and MWP. Teaching & Supervision: Teaches Digital Signal Processing, Engineering Electromagnetics, and Digital Hardware. Supervises PhD/Master’s projects on topics like 6G wireless communication, MIMO systems, and light-field imaging. Current students are researching areas such as ODDM modulation and sub-THz communications. Funding & Grants: Secured grants including the Advancement and Equity Grants Scheme for Research (AEGiS) for multi-modal defect detection in manufacturing. Previously led projects on UOW-UESTC collaborations. Service & Leadership: Member of the IEEE. Actively involved in academic leadership and curriculum development within his school.

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۳ مورد دیگر

Dr. Moojan Ghafurian is an Assistant Professor in Systems Design Engineering at the University of Waterloo, cross-appointed to the Department of Electrical and Computer Engineering (ECE). His research focuses on human-robot interaction, artificial intelligence (AI), and assistive technologies, particularly in healthcare, transportation, and aging-related contexts. He explores topics such as socially interactive agents for mental health support, robotic learning through kinesthetic teaching, and smart infrastructure systems. His work bridges engineering and social sciences, addressing challenges like public speaking anxiety through robotic coaches and improving elderly care via ambient assisted living technologies. He has contributed to systematic reviews in AI for pavement performance prediction and social robots in cardiovascular medicine. His interdisciplinary approach involves collaboration across robotics, AI, and healthcare domains. Research trends in his articles emphasize AI-driven solutions for aging populations, human-robot collaboration in teaching scenarios, and autonomous systems in shared spaces. His studies often evaluate user perceptions and practical implementation of emerging technologies in real-world settings. No scientific awards are explicitly listed. His advising and grants are not detailed here, but his research aligns with grants focusing on robotics, healthcare tech, and smart infrastructure. He is involved in lab initiatives around assistive robotics and human-centric AI design.

• Drone Autonomy
• Drone-Based Remote Sensing
• Aerospace Engineering
• +۷ مورد دیگر

Dr. Fernando Vanegas Alvarez is a Lecturer at the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT). He holds a M.Sc. in Electrical Engineering from Halmstad University and a PhD in Aerial Robotics from QUT. His research focuses on Drone Autonomy, UAV navigation in GNSS-denied environments, and AI-assisted remote sensing. Education: M.Sc. in Electrical Engineering, Halmstad University PhD in Aerial Robotics, QUT Research Interests: Dr. Vanegas' work emphasizes motion planning for UAV exploration , POMDP , SLAM , Visual Odometry , and AI-driven remote sensing applications . His projects include UAV frameworks for planetary exploration, invasive species mapping, and multi-UAV coordination in challenging environments. Scientific Awards: Advanced Queensland Industry Research Fellowship Grant (2024) Supervision & Grants: Dr. Vanegas has supervised three postgraduate students, including topics like planetary exploration UAV systems and multi-agent UAV search algorithms. He actively accepts new students for Honours, Masters, and PhD programs. His research is supported by grants and collaborations with the QUT Centre for Robotics. Teams & Affiliations: He is a core member of the QUT Centre for Robotics and contributes to the School of Electrical Engineering & Robotics. His work bridges robotics, AI, and environmental science.

Elmar Rueckertمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Robotics
• Machine Learning
• +۹ مورد دیگر

Prof. Elmar Rueckert is the Chair of the Cyber-Physical-Systems Institute at Montanuniversität Leoben in Austria since March 2021. He holds a PhD in Computer Science from TU Graz (2014) and previously served as a Senior Researcher at TU Darmstadt (2014–2018) and Assistant Professor at the University of Lübeck (2018–2021). His research focuses on Cyber-Physical Systems, Robotics, Machine Learning, and Human Motor Control, with applications in industrial automation, healthcare robotics, and environmental sustainability. Key research interests include stochastic machine/deep learning, reinforcement learning, brain-computer interfaces, and tactile learning. His work bridges robotics, neuroscience, and AI, emphasizing practical applications such as autonomous navigation, exoskeleton control, and sensor-based recycling technologies. Rueckert has led projects like the AI Robot Lab funded by Robert Bosch Stiftung and the KIRAMET recycling initiative. Recent publications highlight advancements in neural networks for industrial condition monitoring, multimodal robotic learning, and environmental infrastructure modeling. Awards include the German Young Researcher Award (2019) and the Advanced Robotics Best Paper Award (2018). Rueckert advises a team of researchers, including notable students like Daniel Tanneberg (PhD graduate, 2019) and Linus Nwankwo (best student paper winner, 2023). His lab develops open-source tools like ROMR and collaborates on datasets like EnvoDat for robotic spatial reasoning.

• Cyber-Physical Systems
• Multi-Agent Systems
• Autonomous Systems
• +۳ مورد دیگر

Andreas Brandstätter is a PostDoc Researcher in the Cyber-Physical Systems department at TU Wien. His work focuses on multi-agent systems, autonomous robotics, and control theory with applications to drone flocking and autonomous racing. He is affiliated with the Scuderia Segfault team, TU Wien’s autonomous F1TENTH racing group. His research emphasizes distributed control mechanisms and sensor-based coordination in robotic systems. Education: Completed a Diploma Thesis (2019) on local positioning systems for quadcopters at TU Wien. Research Interests: Developing algorithms for multi-agent coordination, predictive control in autonomous vehicles, and leveraging sensor networks for real-time system adaptation. His work intersects robotics, machine learning, and formal methods in control systems. Publications (Selected): 5 peer-reviewed articles in top robotics venues (ICRA, ISoLA) since 2019, focusing on flock formation, predictive control, and autonomous systems. Advising: Supervised a 2024 diploma thesis on coordinated ground-aerial vehicle control. Labs/Teams: Active contributor to Scuderia Segfault, advancing autonomous racing and real-world robotics applications.

• Algorithms and Computational Theory
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Sanem Sariel Uzer is a Professor at Istanbul Technical University's Department of Artificial Intelligence and Data Engineering within the Faculty of Computer and Informatics. He holds a PhD in Computer Engineering from ITU and has been teaching since 2007, specializing in Artificial Intelligence, Robotics, and Algorithms. He leads the ITU Artificial Intelligence and Robotics Laboratory and coordinates the Game and Interaction Technologies Master's Program. Education: B.Sc. in Control and Computer Engineering (1995-1999), M.Sc. and Ph.D. in Computer Engineering (1999-2007), all from ITU. He conducted research at Georgia Institute of Technology (2004-2006) and served as a visiting researcher at the University of South Florida (2008) and University of Pennsylvania (2011). Research focuses on cognitive robotics, multi-robot team strategies, lifelong learning for robots, and reinforcement learning applications. His work includes developing open-source robot vision systems and safety-critical manipulation techniques. He has led TUBITAK projects on artificial learning methods and humanoid robotics. Awards include TÜBİTAK's 2018 Technology Award for collaborative robotics software and multiple project performance recognitions. He is an active member of IEEE, AAAI, and RoboCup's executive board, co-founding Turkey's Robotics Conference (ToRK). Advising over 30 graduate students, his lab has contributed to RoboCup competitions and developed systems for robot vision and environment modeling. Current projects explore deep reinforcement learning for daily task automation in humanoid robots.

• Aerospace Engineering
• Autonomous Systems
• Robotics
• +۵ مورد دیگر

Professor Jennifer Palmer is a leading academic in Aerospace Engineering at RMIT University, serving as Head of Department and Associate Dean. Her research focuses on autonomous systems, UAV propulsion, and urban environment operations. She previously led the Trusted Autonomous Systems (TAS) Defence CRC and held roles at the Defence Science and Technology Group (DSTG), emphasizing industry collaboration. Her career spans over 15 years in aerial autonomy and defense-related R&D, with expertise in hybrid power systems for UAVs and flapping flight mechanics. She has published extensively in top-tier journals across Aerospace, Mechanical, and Computer Science disciplines. Current projects include optimizing multimodal transport logistics and exploring fuel types for advanced propulsion systems. Professor Palmer’s work integrates academic research with practical defense and civilian applications, fostering innovation in autonomous technologies and robotic teaming strategies. She is open to supervising PhD and Masters students in aerospace engineering and related fields.

Jing Fuمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Telecommunications
• +۶ مورد دیگر

Dr. Jing Fu is a Lecturer at the School of Engineering, RMIT University in Australia. Her research focuses on applying optimization algorithms and machine learning to telecommunications, wireless networks, and edge computing. She specializes in restless bandit models for dynamic resource allocation, multi-agent coordination, and energy-efficient systems design. Key areas include satellite communications, radar systems, and distributed computing architectures. Her work bridges theoretical operations research with practical applications in 5G/6G networks, UAV communication platforms, and smart sensor networks. She has published extensively on topics like beam scheduling, edge computing offloading strategies, and adaptive wireless protocols. Current supervision interests span neural networks for sensor systems, IoT resource allocation, and next-generation mega satellite networks. Research Themes: AI-driven network optimization, distributed radar systems, energy-efficient edge computing Key Projects: Reinforcement learning-based IoT resource allocation 6G wireless communication with AI integration Optimal beam scheduling for phased array radars Collaborations: Active in multi-disciplinary projects involving telecommunications, aerospace engineering, and computer science Dr. Fu supervises research projects on neural networks for telecommunications, adaptive wireless techniques, and satellite formation flying. She is based at RMIT's City Campus and open to guiding postgraduate research in her areas of expertise.

• Safety Analysis of Autonomous Systems
• Multi-Agent Systems
• Hazard Analysis Simulation
• +۳ مورد دیگر

Rob Alexander is a Senior Lecturer in the Department of Computer Science at the University of York. He holds a BSc from Keele University (2001) and a PhD in Computer Science from the University of York (2008). His research focuses on safety analysis and testing of autonomous robots and multi-agent systems, including simulation methods for hazard analysis and empirical evaluation of safety engineering techniques. He is part of the High Integrity Systems research group. Key research interests include situation coverage-based safety analysis, autonomous systems verification, and safety assurance methodologies. His work spans robotics, autonomous vehicles, and safety-critical systems, with applications in mine environments, healthcare, and transportation. Recent publications emphasize rigorous testing frameworks, risk assessment in complex systems, and bridging theoretical and practical safety engineering challenges. No scientific awards or grants are explicitly listed. He serves as a Union Rep within the department and is involved in academic leadership roles. His research group collaborates on projects such as SCALOFT and SASSI, focusing on safety assurance in autonomous systems.