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Jonathan BlackView profile

Professor

• Space Systems Engineering
• Autonomous Vehicles
• Structural Dynamics
• +5 more

Jonathan Black is a Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech. He serves as Director of the Aerospace and Ocean Systems Laboratory within the Ted and Karyn Hume Center for National Security and Technology, and Co-Director of the Center for Space Science and Engineering Research (Space@VT). Additionally, he holds the Northrop Grumman Senior Faculty Fellowship in C4ISR. His work bridges mission platforms and payloads across multiple research centers at Virginia Tech. Black's academic background includes a PhD from the University of Illinois at Urbana-Champaign. Prior to joining Virginia Tech, he was a faculty member at the Air Force Institute of Technology (AFIT), where he founded and directed the Center for Space Research and Assurance, overseeing a $2M annual research portfolio and leading five spaceflight experiments. His research focuses on autonomous systems, sensor data processing in low-SWAP environments, mission-driven payload control, and distributed resource management in space systems. Key areas include structural dynamics of deployable space structures, micro UAV development, and taskable satellite systems for defense, commercial, and scientific applications. Black's leadership roles include directing the Aerospace and Ocean Systems Laboratory and contributing to the Hume Center’s national security initiatives. His work emphasizes cross-disciplinary collaboration and real-world application of advanced technologies in space systems engineering.

Almuatazbellah BokerView profile

Assistant Professor

• Autonomous Systems
• Robotics
• Machine Learning
• +2 more

Almuatazbellah Boker is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He earned his PhD in Electrical Engineering from Michigan State University in 2013. His research focuses on autonomous systems, machine learning for robotic decision-making, and distributed control optimization. Key research areas include Koopman operator-based control, sparse controller design, and human-supervised autonomous systems. His research explores: Nonlinear control strategies for robotic and motion systems Data-driven approaches for optimal control Human-robot interaction in autonomous navigation Publication analysis shows strong focus on Koopman operator applications for system modeling and control, with recent extensions to digital twins and fault detection. Work consistently addresses practical implementation challenges in robotic systems and industrial automation. Dr. Boker has supervised PhD candidate Muhammad Al Saaideh to graduation. His research receives support from the US Department of Energy and Siemens USA, including a $300,000 subaward for sensor placement optimization in solar systems (2024-2027).

• Computational Fluid Dynamics
• Chemical Engineering
• Optimisation
• +4 more

Nausheen Basha is a Researcher in the Department of Chemical Engineering at Imperial College London, part of the Faculty of Engineering. Previously, she held a Research Assistant position at City, University of London. She earned her PhD in Chemical Engineering from City, University of London (2017–2021) and an MSc in Aerospace Engineering from the University of Manchester (2013–2014). Education: PhD in Chemical Engineering, City, University of London (2017–2021) MSc in Aerospace Engineering, University of Manchester (2013–2014) Research Interests: Nausheen’s work focuses on integrating machine learning and computational fluid dynamics (CFD) to enhance engineering design and optimization. Her research spans computational fluid dynamics, chemical engineering optimization, and the application of machine learning techniques such as deep learning and Bayesian optimization. She explores multiphase systems, reactor design, and the development of surrogate models to improve simulation efficiency and accuracy in fluid dynamics and mechanical engineering contexts. Research Trends: Her recent work emphasizes multi-fidelity methods, Bayesian optimization, and machine learning-driven approaches to solve complex fluid dynamics problems. She has contributed to advancements in flow reactor design, laminar mixing systems, and the analysis of dispersed oil-water flows, often leveraging CFD simulations combined with data-driven frameworks. Advising & Grants: No formal advisees or grant information is listed in the provided text. Her contributions are primarily through research publications and academic positions.

• Systems Engineering
• Sustainable Manufacturing
• Cyber-Physical Systems
• +5 more

Dr. Ali Asghar Bataleblu is a Senior Research Fellow at the Sustainable Manufacturing Laboratory within the Faculty of Engineering at the Free University of Bozen-Bolzano . His work focuses on transdisciplinary systems engineering for resilience, sustainability, and circular economy , with over a decade of experience in uncertainty-based Multidisciplinary Design Optimization (MDO) . He was previously the director of an MDO research center for nearly ten years. Academic Rank: Research Fellow Key Research Areas: Systems Engineering, Cyber-Physical Systems, Resilience Engineering, Sustainable Manufacturing Dr. Bataleblu’s research bridges model-based systems engineering (MBSE) , digital twins , and surrogate-assisted optimization to address sustainability challenges in manufacturing and mobility. He employs multi-agent systems and system-of-systems frameworks to analyze complex interactions in industrial and environmental contexts. His work includes requirements management and axiomatic design for holistic system decomposition. The 15 most recent articles highlight his focus on resilient cyber-physical production systems , sustainable mobility , AI-integrated MBSE , and multi-agent optimization for wildfire management. These works span disciplines like industrial engineering , computational intelligence , and urban planning , with subfields covering digital twins , dynamic system modeling , and automated routing . Scientific Awards: TOWARDS South Tyrol International Postdoctoral Fellowship (2023) Young Scientist Award at ICQIS-2024 (2024) He leads the Sustainable Factory Design Decomposition (SFDD) project funded by the Autonomous Province of Bolzano , collaborating with Purdue University Fort Wayne . His research integrates graph database visualization for supply chain resilience and life cycle assessment (LCA) automation in fast-moving consumer goods. He also explores reinforcement learning for aerial vehicle design and robust control systems for drones.

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +4 more

Julian J. McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego. His research focuses on advancing the fields of Artificial Intelligence, Machine Learning, and Natural Language Processing, with a particular emphasis on Recommender Systems and Large Language Models (LLMs). His work addresses challenges in ethical AI, generative models, and the integration of knowledge-driven approaches into recommendation systems. Key research interests include responsible AI, lifelong learning, and the development of agentic recommender systems. He explores techniques such as retrieval-augmented generation, causal reasoning, and multi-objective alignment in LLMs. His contributions span foundational theories and practical applications, including the design of scalable systems and frameworks for evaluating generative models. McAuley’s publications highlight advancements in areas like music generation, chain-of-thought reasoning, and knowledge transfer in recommendation systems. His work often bridges theory and practice, addressing real-world challenges in personalized systems and ethical AI deployment.

Jorrit J. OlthuisView profile

Researcher

• Robotics
• Cybersecurity
• Machine Learning
• +3 more

Jorrit J. Olthuis is a doctoral candidate in the Data Protection group at Eindhoven University of Technology (TU/e), within the Faculty of Mathematics and Computer Science. His research focuses on cybersecurity, robotics, and formal methods in safety-critical systems. He is actively involved in the INTERSECT project (NWA.1160.18.301), which aims to develop secure IoT systems through modular architectures and forensic expertise. His work bridges theoretical computer science with practical applications in autonomous systems and machine learning. Olthuis has contributed to interdisciplinary projects combining robotics and AI, as evidenced by his participation in the Tech United Eindhoven Middle Size League, where his team won competitions in 2021–2024. These efforts emphasize multi-agent systems, real-time decision-making, and safety protocols. His publications address topics like autonomous navigation security, vision-based machine learning, and cryptography on constrained devices. He holds a master’s degree with theses on pairing-based cryptography performance and formal requirement verification. His research aligns with trends in secure IoT, autonomous systems, and AI ethics. Current projects explore cyberattack defense strategies and resilient system design.

Wei WangView profile

Assistant Professor

• Marine Robotics
• Autonomous Systems
• Control Systems
• +4 more

Wei Wang is an Assistant Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison, leading the Marine Robotics Lab (MRL). His research focuses on designing and controlling autonomous robots in complex aquatic environments, with expertise in marine robotics, bioinspired systems, and underwater perception. He joined the university in January 2024 and teaches courses such as E M A 599, M E 351, and M E 376. Education: PhD in Mechanical Engineering from Peking University (2016), Bachelor of Engineering from University of Electronic Science and Technology of China (2010). Notable academic distinctions include being a finalist for the 2022 MIT Water Innovation Prize and a champion in the 2014 International Underwater Robot Competition. Research Interests: The MRL explores innovative robotics concepts for aquatic monitoring, ocean exploration, and urban transportation. Key areas include autonomous navigation, control algorithms, and bioinspired design. Recent work emphasizes deep reinforcement learning, hybrid actuation systems, and robust model predictive control for surface and underwater vehicles. Publications: Over 25 peer-reviewed articles since 2013, with a focus on marine robotics, control systems, and bioinspired mechanisms. Notable contributions include Roboat III’s urban transportation system and AI-driven underwater glider design. Awards: Extensive recognition for innovation in robotics, including the NOW Awards Silver Winner (2019) and multiple academic excellence awards from Peking University and UESTC. Lab & Teams: The Marine Robotics Lab collaborates on projects like Roboat (autonomous urban vessels) and underwater electrocommunication systems. Current research integrates AI, bioinspired engineering, and environmental adaptation for next-generation autonomous systems.

Mihai V. MICEAView profile

Professor

• Intelligent Robotic Environments
• Energy Management and Optimization
• Embedded and Real-Time Systems
• +5 more

Mihai V. Micea is a Professor and Head of the Department of Computer and Information Technology at the Politehnica University of Timisoara. He also serves as Director of the CCCTI Research Center and Coordinator of the DSPLabs. With a B.Sc., M.Sc., and Ph.D. (Cum Laude) from Politehnica University of Timisoara, he has been a faculty member since 1996. His research focuses on intelligent robotic environments, energy management, embedded systems, and real-time hardware/software systems. He has supervised 4 completed PhD theses and currently oversees multiple doctoral candidates. Micea has authored over 125 publications, 3 patents, and managed over 50 R&D projects worth €1.88M. His awards include the 'Eminent Young Researcher of Timisoara' (2006) and the IEEE Outstanding Reviewer Distinction (2017). Education: B.Sc. in Computer Engineering (Politehnica University of Timisoara, 1995) M.Sc. in Computer Engineering (Politehnica University of Timisoara, 1996) Ph.D. in Computer Engineering (Politehnica University of Timisoara, 2005) Habilitation Degree (2015) Research Interests: His work spans intelligent robotic systems, energy optimization, signal processing, and real-time embedded systems. Key areas include cyber-physical systems, sensor networks, and secure IoT communication protocols. His recent projects include RoNaQCI (Quantum Communication Infrastructure) and TEEFIOS (Time-Efficient Framework for Smart Devices). Grants & Projects: As Principal Investigator/Manager, he has led 29 projects with total funding exceeding €1.88M. Notable projects include CloudPUTing (High-Performance Cloud Platform) and MELISSEVS (Robotic-Sensor Collaboration Models). Awards & Recognition: 2006: Eminent Young Researcher of Timisoara (ANCS) 2017: IEEE T-IM Outstanding Reviewer Labs & Teams: Founder and Coordinator of DSPLabs (Digital Signal Processing Laboratories), focusing on real-time systems and smart sensing. Active in IEEE SSIT Romanian Chapter (2013–2022) and international conference organization (e.g., IEEE ROSE 2014).

Mey GohView profile

Academic

• Transdisciplinary Digital Manufacturing
• Intelligent Automation
• Industry 5.0
• +2 more

Dr. Mey Goh is a Reader in Transdisciplinary Digital Manufacturing at Loughborough University's School of Mechanical, Electrical and Manufacturing Engineering. She co-leads the EPSRC Centre for Innovative Manufacturing in Intelligent Automation and founded the Digital Automation Systems Design Lab. Her research bridges data-driven methods with cross-disciplinary insights to develop inclusive industrial systems (Industry 5.0), including leading Loughborough's contribution to the £20M Made Smarter Innovation Centre. Education: BEng (Hons) Mechanical Engineering, Universiti Tenaga Nasional PhD, University of Bristol Her transdisciplinary research focuses on human-digital technology interfaces, cognitive workload measurement, and sustainable manufacturing. Using mixed methodologies, she explores how human knowledge and behavior inform industrial digitalization, particularly in human-robot collaboration contexts. Recent publications (2021-2024) demonstrate strong focus on Industry 5.0 themes: human-automation interaction (36%), cognitive engineering (21%), manufacturing optimization (21%), and transdisciplinary frameworks (14%). Key trends include augmented reality interfaces, physiological sensing for workload assessment, and explainable AI for industrial systems. Awards: Overseas Research Students Award Scheme (UK, 2001-2005) Public Services Department of Malaysia Scholarship (1996-2001) She directs the Digital Automation Lab, collaborating with automotive, aerospace, and defense industries. As COMEH committee member, she organizes the International Conference on Manufacturing Research and maintains global industry partnerships.

Jos ElfringView profile

Assistant Professor

• World Modeling Techniques
• Artificial Intelligence
• Robotics
• +6 more

Jos Elfring is an Assistant Professor in the Robotics section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). His research focuses on world modeling techniques, AI, automated driving, and sensor fusion. He holds an MSc and PhD from TU/e, with his PhD thesis on Semantic World Modeling for Autonomous Robots (2014). He worked in industry roles at TNO, TomTom, and others from 2014-2024, balancing part-time academia until moving to a full-time faculty role in 2024. Education: MSc and PhD from TU/e, internship at University of Canterbury (2007). Research interests include motion prediction, localization, and state estimation. Notable achievements: Best Paper Award at RoboCup 2013 and contributions to RoboEarth, a seminal robotics knowledge-sharing project. Courses taught include 'Data Fusion & Semantic Interpretation.' His work spans automated driving systems, probabilistic modeling, and collaborative robotics. Key collaborations include EU projects and industry partnerships. Labs/teams: Member of Tech United RoboCup@Home team. Current focus: semantic world models for autonomous systems.

• Power Electronics
• Artificial Intelligence in Design Automation
• DC-DC Converters
• +7 more

Diego Gerardo Bernal Cobaleda is a researcher at KU Leuven , affiliated with the Electrical Engineering Department (ELEKTRA) and the EnergyVille research institute. His work focuses on power electronics design automation, integrating artificial intelligence (AI) and machine learning (ML) techniques to optimize converter efficiency and component selection. Research Highlights : AI-driven semiconductor datasheet analysis, variable transformer design for wide voltage ranges, reinforcement learning for converter parameter optimization. Collaborators : Fanghao Tian, Wilmar Martinez, Camilo Suarez Buitrago, Miguel Vivert. His recent publications address: Automated extraction of dynamic characteristics from MOSFET datasheets using computer vision. Development of resonant multi-input multi-output (MIMO) converters for aerospace applications. Integration of variable transformers in LLC and dual active bridge (DAB) topologies for improved efficiency. Application of deep reinforcement learning (DDPG) to optimize converter parameters. He has contributed to journals including IEEE Transactions on Power Electronics , Energies , and IEEE Access , as well as conferences such as IEEE APEC, ECCE Asia, and ISIE.

Melkior OrnikView profile

Assistant Professor

• Autonomous Systems
• Control of Systems in Complex Environments
• Safety-Critical Control
• +5 more

Melkior Ornik is an Assistant Professor in the Department of Aerospace Engineering at the University of Illinois Urbana-Champaign (UIUC), with cross-appointments to the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory. He also holds an affiliate position at the Discovery Partners Institute in Chicago. His research focuses on control, autonomy, and learning in complex systems, particularly addressing challenges in safety, resilience, and adaptability under uncertainty. Ornik earned a Ph.D. in Electrical and Computer Engineering from the University of Toronto (2017), an M.S. in Mathematics from Queen’s University (2013), and a B.S. in Mathematics from the University of Zagreb (2011). Prior to UIUC, he was a Postdoctoral Fellow and Research Associate at the University of Texas at Austin (2017–2018). His research interests include autonomous systems, resilient control design, learning-based control, and systems with partial or unknown dynamics. He leads the LEADCAT research group, which explores the intersection of control theory, data-driven methods, and autonomy. Key areas of application include aerospace systems, robotic navigation, and space exploration. Ornik’s work is supported by grants from the Air Force Office of Scientific Research, NASA, the Office of Naval Research, and DARPA. His contributions span guaranteed reachability analysis, resilient control under degraded conditions, and environment-aware decision-making. He has also contributed to educational initiatives, including project-based control courses and AI-assisted learning strategies. Teaching awards include multiple recognitions as an excellent instructor at UIUC, reflecting his commitment to student engagement and well-being. He currently teaches courses such as AE 353 (Aerospace Control Systems) and AE 598 AAO (Autonomy Against the Odds).

Marco LevoratoView profile

Associate Professor

• Distributed Computing
• Wireless Systems
• Autonomous Vehicles
• +5 more

Marco Levorato is an Associate Professor of Computer Science at the Donald Bren School of Information and Computer Sciences, University of California, Irvine. He holds a Ph.D. in Electrical Engineering from the University of Padova (2009) and has held postdoctoral roles at Stanford University and the University of Southern California. His research focuses on distributed computing, wireless systems, AI for autonomous systems, and healthcare applications. He has led multiple NSF-funded projects, including collaborations with Intel and Cisco, and chairs/co-chairs several IEEE and ACM conferences. His work has been recognized with awards such as the 2024 Emerging Innovator of the Year Award and the 2016 UC Hellman Fellowship. Key research themes include edge computing, split computing for real-time systems, and security in autonomous UAV networks. Levorato advises a team of graduate students and has pioneered technologies for resilient distributed systems, as seen in his keynote at IEEE HealthCom (2022) and leadership in the NSF/Intel MLWiNS initiative. Recent contributions include frameworks for 6G-enabled offloading in autonomous vehicles, semantic communications for sensor fusion, and context-aware stress monitoring using wearable devices. His interdisciplinary approach bridges machine learning, networking, and IoT to address challenges in smart cities and healthcare.

Pavol JancuraView profile

Assistant Professor

• Reinforcement Learning
• Video Coding
• Computer Vision
• +2 more

Pavol Jancura is an Assistant Professor in Electrical Engineering at Eindhoven University of Technology, affiliated with the Mobile Perception Systems Lab. His research spans reinforcement learning, video coding architectures, and edge AI systems. Current projects include 5G-MOBIX for connected automated mobility and NXP Smart Mobility. Recent publications focus on multi-agent reinforcement learning coordination, efficient object detection using sensor fusion, and quantization-aware neural networks for edge devices. His work integrates computer vision, multi-agent systems, and optimization for autonomous systems.

• Cloud Computing
• Edge Computing
• Reinforcement Learning
• +3 more

Hongyun Liu is a researcher involved in the HiCONNECTS project (2023-2025), focusing on heterogeneous integration for connectivity and sustainability. His research interests span cloud computing, edge computing, reinforcement learning, IoT data analysis, performance anomaly detection, and workflow offloading. Recent work emphasizes robust frameworks for cloud applications, meta-learning in scheduling, and anomaly detection using ensemble learning and GAN-based models. His publications address time-critical systems, edge-cloud continuum optimization, and statistical modeling of cloud resource performance. Liu contributes to projects like HiCONNECTS as a core member, exploring sustainable and efficient resource management strategies. No awards or grants are explicitly listed in the provided information.