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David ParkerView profile

Professor

• Formal Verification
• Probabilistic Model Checking
• Quantitative Analysis
• +2 more

David Parker is Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Trinity College. His research focuses on formal verification methods for checking system correctness, particularly quantitative verification techniques for probabilistic systems. As leader of the PRISM and PRISM-games projects, he develops tools for analyzing safety, reliability, and performance properties in complex systems. Current research explores verification of AI systems, robust decision-making under uncertainty, and multi-agent systems using stochastic games. His work bridges theoretical foundations with applications in autonomous systems, robotics, and healthcare technology. Recent publications demonstrate advancements in probabilistic temporal logic, robust policy learning, and bisimulation techniques for Markov models. These works consistently emphasize formal guarantees for safety-critical applications and novel approaches to model checking. ETAPS Test-of-Time Tool Award (2024) HVC Award (2016) Professor Parker mentors PhD students in verification, control synthesis, and AI safety, with research funded by ERC, EPSRC, and industrial partners. He serves on editorial boards for Formal Aspects of Computing and ACM Transactions on Autonomous Systems.

Magnus BångView profile

Associate Professor

• Artificial Intelligence
• Human-Computer Interaction
• Automation
• +3 more

Magnus Bång is a Senior Associate Professor at the Department of Computer and Information Science (IDA) at Linköping University, affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His research focuses on advancing human-AI collaboration, automation systems, and AI applications in domains like cyberphysical production, air traffic management, and process industries. He has contributed to interdisciplinary projects involving real-time human-automation interfaces, explainable AI dashboards for industrial processes, and safety-critical systems integration. His work bridges theoretical AI advancements with practical implementations in sectors such as aviation and maritime logistics. Notable collaborations include research with the Swedish Maritime Administration to enhance shipping efficiency through AI and interactive visualization. He actively participates in EU-funded initiatives like the Horizon 2020 projects targeting autonomous systems and air traffic control. Research interests span MLOps for industrial systems, glyph-based communication design for human-automation teams, and operator modeling across traffic management domains. His publications emphasize cross-disciplinary solutions to challenges in automation and human-centric AI design.

Sijia GengView profile

Assistant Professor

• Power Systems
• Control Theory
• Nonlinear Dynamical Systems
• +4 more

Sijia Geng is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at Johns Hopkins University (JHU) and a core researcher at the Ralph O’Connor Sustainable Energy Institute (ROSEI). She directs the Power and Energy Network Systems Analysis (PENSA) Laboratory and co-leads the NSF-funded Electric Power Innovation for a Carbon-free Society (EPICS) Center as co-PI. Her research focuses on integrating control theory, mathematical analysis, and optimization to enhance renewable energy utilization and grid resiliency. Education: Ph.D. and M.S. (ECE & Mathematics) from the University of Michigan-Ann Arbor (2016–2022), B.S. in Automation from Harbin Institute of Technology (2016). Postdoctoral work at MIT (2022) and visiting scholar roles at Purdue University (2015) and Pacific Northwest National Lab (2018). Research Interests: Dynamic analysis of inverter-based power systems, nonlinear control theory, data-driven decision-making, and multi-energy systems. Her work emphasizes achieving autonomous, resilient energy systems through advanced computational tools and theoretical frameworks. Awards: Best Paper Award at MIT/Harvard Applied Energy Symposium (2022), MIT Rising Stars in EECS (2021), Barbour Scholarship (2021), Towner Prize (2018), and Gerald and Esther Forrest Fellowship (2016). She is active in IEEE and INFORMS, organizing sessions at PES General Meeting and CISS conferences. Grants & Collaborations: Funded by NSF, DOE, MIT Energy Initiative, and industry. Leads global initiatives through EPICS, collaborating with UK, Australian, and international stakeholders. Co-leads ROSEI’s Grid pillar to advance fossil-free energy systems. Labs & Teams: Directs PENSA Lab, affiliated with JHU’s Data Science and AI Institute, Applied Mathematics & Statistics, and Computer Science departments.

Hatem Abou-ZeidView profile

Assistant Professor

• AI for 6G
• Wireless Sensing
• Extended Reality Networking
• +5 more

Dr. Hatem Abou-Zeid is an Assistant Professor in the Department of Electrical and Software Engineering at the University of Calgary, directing the WAVES Research Group. He holds adjunct positions at Queen’s University, Carleton University, and Ontario Tech University. His research focuses on AI-driven 6G networks, wireless sensing, extended reality (XR), and brain-computer interfaces (BCI). Prior to academia, he spent 7 years in industry at Ericsson and Cisco, leading projects in 5G radio access and network intelligence, resulting in 20+ patents and $3.5M+ collaborative research projects. Education: PhD in Electrical and Computer Engineering (Queen’s University), M.Sc. and B.Sc. in Electronics and Communications Engineering (Arab Academy, Egypt). Research interests span trustworthy AI for 6G, joint sensing & communication systems, and AI for immersive networking. Notable work includes foundational models for 6G radios, self-supervised learning for spectrograms, and safe reinforcement learning for network slicing. His lab explores pediatric BCI, IoT edge computing, and low-power wireless prototyping. Publications highlight contributions to AI-driven resource allocation, 5G/6G slicing, and federated learning in tactical networks. Awards include the 2023 Early Research Excellence Award and 2023 Software Engineering Professor of the Year. Advising involves 15+ students and postdocs, with collaborations spanning academia (e.g., Hotchkiss Brain Institute) and industry (Ericsson, Telus). Current openings exist for PDF/PhD researchers in AI/6G wireless systems and pediatric BCI. Labs/Teams: Director of WAVES Lab; collaborates with Ericsson Canada, Alberta Innovates, and Canadian Space Agency on applied research projects. Teaching includes advanced networking, machine learning systems, and IoT courses.

• Cybersecurity
• International Relations
• Military Strategy
• +3 more

Jon Lindsay is an Associate Professor at the Sam Nunn School of International Affairs, part of the Ivan Allen College of Liberal Arts at Georgia Institute of Technology. His research focuses on cybersecurity, international relations, and military strategy, with an emphasis on cyber warfare, deterrence theory, and the impact of technological advancements like AI and quantum computing on global security. He explores topics such as cyber conflict dynamics, the role of deception in defense strategies, and the institutional challenges of military automation. His work often bridges theoretical frameworks from international relations with empirical analyses of modern cyber threats and geopolitical tensions. Notable contributions include studies on Stuxnet's implications for secret statecraft, the stability-instability paradox in cyberspace, and the strategic ambiguity of quantum computing. Lindsay has also engaged in policy debates on cross-domain deterrence and the evolving nature of intelligence operations in the digital age.

Vijay K. ShahView profile

Assistant Professor

• Networking
• Communications
• Controls
• +7 more

Vijay K. Shah is an Assistant Professor in the Electrical and Computer Engineering Department at North Carolina State University, leading the NextG Wireless Lab. His research focuses on advancing wireless communication and network technologies for beyond 5G/6G systems, including O-RAN architecture, spectrum management, and AI-driven network optimization. Education: Ph.D. in Computer Science, University of Kentucky (2019) Bachelor's in Computer Science and Engineering, National Institute of Technology, Durgapur (2013) Research emphasizes open radio access networks (O-RAN), mmWave testbeds, and cross-layer optimization. Recent work highlights include ORAN-Bench-13K (LLM benchmarking), ZT-RIC (zero-trust security frameworks), and Milli-O-RAN (reconfigurable mmWave networks). His contributions span O-RAN applications (xApps/rApps), satellite-terrestrial coexistence, and AI-driven positioning systems. Experimental validations include 3GPP-compliant 5G positioning and adversarial attack defenses. Publications reflect expertise in O-RAN architecture evolution, spectrum policy tools (ASCENT), and UAV-based network coordination (GLIDE). Current projects explore LEO satellite constellations and resilient disaster response networks. Labs/Teams: Head of the NextG Wireless Lab at NC State, focusing on prototype development in O-RAN, 6G, and secure AI-driven networks.

• Robotics
• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• +9 more

Nabil Aouf is a Professor of Robotics and Autonomous Systems in the Department of Electrical and Electronic Engineering at City, University of London, a position he has held since January 2019. Previously, from 2006 to 2018, he was Professor of Autonomous Systems at Cranfield University’s Defence and Security campus, where he also served as Head of the System and Autonomy Group and Research Lead of the Centre of Electronic Warfare, Information and Cyber. He earned his PhD in Electrical Engineering from McGill University Faculty of Engineering between 1999 and 2002. His research focuses on Robotics, Autonomous Systems, UAV Navigation, Computer Vision, and Fault-Tolerant Control . Key areas include visual odometry, sensor fusion (vision/IMU, RGBD, thermal-visible), robust control for UAVs, fault diagnosis in inertial systems, 3D perception, and autonomous landing. His work integrates theoretical control methods with real-time implementation in aerospace and defense contexts. His recent publications reflect a strong emphasis on robust optimization, multispectral vision, and real-time autonomous navigation. Trends indicate a focus on enhancing autonomy under uncertainty—through illumination-invariant stereo matching, L∞ optimization, and robust feature matching—particularly for UAVs operating in challenging environments. Nabil Aouf has collaborated extensively with researchers such as M. Richardson, O. Araar, T. Mouats, and M. Boulekchour across numerous projects in UAV control, sensor fusion, and autonomy. While no scientific awards are listed in the provided text, his leadership roles and sustained publication record in high-impact journals and conferences underscore his academic contributions. He has supervised or collaborated with several advisees including S.H. Almutairi, L. Chermak, I. Vitanov, and D. Nam, contributing to both theoretical developments and practical implementations in autonomous systems. His work has applications in aerospace, defense, planetary exploration, and critical infrastructure inspection.

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +7 more

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

Riyadh BaghdadiView profile

Assistant Professor

• Compilers
• Programming Languages
• Deep Learning
• +9 more

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

• Artificial Intelligence
• Data Systems
• Large Language Models
• +9 more

Joseph E. Gonzalez is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, co-director of the Sky Computing Lab and RISE Lab, and member of the Berkeley AI Research (BAIR) group. His work bridges artificial intelligence and data systems with significant impact on large language model infrastructure and deployment. His research focuses on large language models (LLMs) including tool use, RAG, and agent systems; LLM deployment infrastructure; edge-based machine learning; cloud computing innovations; and computer vision applications. He addresses the full machine learning lifecycle from training to serving, emphasizing real-time decision systems and secure execution environments. Scientific Awards: Okawa Research Grant NSF Expedition Award NSF CAREER Award Professor Gonzalez mentors 17 current graduate students and numerous former students/post-docs across AI and systems research. His work is funded by the NSF Expedition grant for the RISE Lab, an NSF CAREER Award, and industrial sponsors including major technology companies. He co-directs the RISE Lab advancing real-time intelligent secure execution systems, and the Sky Computing Lab pioneering cloud computing abstractions. These initiatives tackle low-latency systems, online learning algorithms, and security frameworks for intelligent decision-making in physical environments.

• Sound and Music Computing
• Human-Computer Interaction
• Human-Centered Computing
• +6 more

Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.

• Industrial Automation
• Control Systems
• Industry 4.0 Technologies
• +7 more

Lisa Ollinger is a Professor of Production Automation at Ulm University of Applied Sciences (Technische Hochschule Ulm), where she has been serving since October 2019. She teaches courses in Automation Technology 1 and 2 for the Business Engineering program, Industrial Automation for the Digital Production program, and Flexible Automation for the Systems Engineering and Management Master's program. Her educational and professional background includes: Technology Leader for Engineering Projects in Automation and Digitalization at Procter & Gamble GmbH (2014-2019) Researcher at the German Research Center for Artificial Intelligence (DFKI) in the Innovative Factory Systems research area (2012-2014) Research Assistant at TU Kaiserslautern in the Production Automation department (2009-2011) Professor Ollinger's research focuses on the intersection of industrial automation and digital transformation. Her work explores how emerging technologies like Industrial Internet of Things, cyber-physical systems, and digital twins can revolutionize manufacturing and logistics processes. She investigates flexible production systems that can adapt to changing requirements through skill-based engineering approaches and novel communication architectures using OPC UA standards. Her research also extends to robotics applications, particularly industrial robotics and autonomous mobile robots, often leveraging ROS (Robot Operating System) frameworks. Her recent publications demonstrate a strong focus on practical implementations of Industry 4.0 concepts, particularly in warehouse management and production systems. She examines how digital twin technology can enhance logistics operations and how agent-based systems can improve manufacturing resilience. A common thread in her work is the application of OPC UA communication standards to create more flexible, interoperable industrial systems. Professor Ollinger holds significant administrative roles at THU: Dean of the Master's program in Systems Engineering and Management Member of the University Council Member of the Institute for Manufacturing Technology and Materials Testing (IFW) Founding Ambassador for Startup South She maintains active professional connections through ResearchGate, LinkedIn, and ORCID, reflecting her commitment to academic collaboration and knowledge sharing in the field of industrial automation and digital manufacturing.

• Road Safety
• Connected and Automated Vehicles
• Driver Behaviour
• +5 more

Evita Papazikou serves as a Lecturer in Transport Engineering at the School of Engineering, University of the West of England (UWE Bristol), where she contributes to the Centre for Transport and Society and collaborates with the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre. Her academic qualifications include: Civil Engineering (BEng and MEng) from Aristotle University of Thessaloniki MSc in Planning, Organisation, and Management of Transport Systems, Aristotle University of Thessaloniki PhD in Automated Systems and Driver Behaviour (Road Safety) from Loughborough University, sponsored by the Insurance Institute for Highway Safety with access to SHRP2 NDS data Dr. Papazikou's research focuses on road safety, connected and automated vehicles, driver behaviour analysis, and smart infrastructure. She investigates accident causation through statistical modeling, develops driver monitoring systems, and explores human factors in transportation. Her work integrates traffic simulation with mobility data fusion from vehicles, sensors, and infrastructure to enhance safety in future mobility systems, particularly in cooperative, connected, and automated environments. Her recent publications (2023-2025) reveal a concentrated research trajectory examining safety impacts of dedicated lanes for autonomous vehicles, parking policy implications in automated eras, and driver fatigue management. She consistently employs naturalistic driving data and traffic microsimulation to analyze driver-vehicle-environment interactions, with increasing emphasis on real-world intervention effectiveness and environmental sustainability in mobility systems. Scientific Awards: No specific awards were mentioned in the provided information. Dr. Papazikou has secured significant research funding through competitive programs including Horizon 2020, Innovate UK, and the Department for Transport. Her project portfolio demonstrates substantial industry collaboration, particularly with Ford, and includes: LEVITATE: Assessing societal impacts of Connected and Automated Vehicles SafetyCube: Developing an innovative road safety decision support tool i-DREAMS: Creating a smart driver and road environment assessment system DDRST: Building a data-driven road safety tool for hotspot identification TRIP: Developing a driver culpability assignment tool for road injury prevention She actively contributes to interdisciplinary research through her affiliations with the Centre for Transport and Society and the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre, where she bridges engineering, human factors, and policy development for next-generation transportation systems.

Gero MühlView profile

Professor

• Distributed systems
• Distributed algorithms
• Event-based systems
• +7 more

Prof. Dr.-Ing. habil. Gero Mühl is a W2-Professor at the University of Rostock, where he holds the chair for "Architecture of Application Systems" since October 2009. His academic journey includes positions as a Heisenberg Fellow at the Technical University of Berlin (2009), postdoctoral research at TU Berlin (2002-2009), and doctoral studies at TU Darmstadt where he received his Dr.-Ing. degree with distinction in 2002. He completed dual Diplomas in Computer Science (Dipl.-Inform.) and Electrical Engineering (Dipl.-Ing.) from FernUniversität in Hagen in 1998. Prof. Mühl's research focuses on Self-Organizing Distributed Systems , with particular expertise in distributed systems, distributed algorithms, event-based systems, middleware, energy-efficient systems, organic computing, sensor networks, web services, and electronic commerce. His work bridges theoretical foundations with practical implementations in real-world distributed environments. His recent publications show a strong trend toward time-sensitive networking, content-based publish/subscribe systems, and P4 programmable data planes. These works address critical challenges in industrial communication, real-time systems, and network reliability. His research group has made significant contributions to making distributed systems more autonomous, reliable, and efficient. Scientific awards and recognitions include: Nomination for the Berlin Science Award for Young Scientists (2008) Heisenberg Fellowship by the German Research Foundation (DFG) (2008) Best paper award in System Software and Security at SAC 2015 Prof. Mühl has been actively involved in numerous research projects and collaborations, particularly focusing on self-organizing and self-stabilizing systems. His work on the REBECA publish/subscribe middleware represents a significant contribution to autonomous distributed systems. He has supervised numerous students and researchers, contributing to the development of the next generation of computer scientists specializing in distributed systems. His laboratory at the University of Rostock focuses on practical implementations of self-organizing distributed systems, with current projects investigating time-sensitive networking, publish/subscribe systems, and energy-efficient distributed computing. The team combines theoretical analysis with practical system development to address real-world challenges in industrial and commercial applications of distributed systems.

Peng ZhouView profile

Assistant Professor

• Robotics
• Machine Learning
• Computer Vision
• +3 more

Peng Zhou is an Assistant Professor at the School of Advanced Engineering, The Great Bay University , and the Principal Investigator of the Embodied Manipulation Intelligence (EMAIL) Robotics Lab . His research integrates robotics, machine learning, and computer vision, with a strong focus on deformable object manipulation, robot perception, and task-motion planning. Education: Ph.D. in Robotics, The Hong Kong Polytechnic University (Supervised by Dr. David Navarro-Alarcon) Postdoctoral Research Fellow, The University of Hong Kong (Advised by Dr. Pan Jia) Exchange Ph.D. Student, KTH Royal Institute of Technology (Supervised by Prof. Danica Kragic) Research Interests: Dr. Zhou's work spans robotics , machine learning , and computer vision , with specialized expertise in deformable object manipulation , robot perception and learning , and task and motion planning . His lab, EMAIL, pioneers solutions for robotic manipulation of soft and deformable materials. Scientific Awards & Honors: 2024 : Track 3 Champion, Zhuhai International Dexterous Manipulation Challenge 2023 : IEEE R10 Outstanding Volunteer Award 2022 : Outstanding Young Researcher Award, National Engineering Research Center 2022 : Best AI Implementation Award, Hong Kong AI Open Competition 2022 : IEEE MGA Young Professional Achievement Award Editorial & Leadership Roles: Dr. Zhou serves as an Associate Editor for IEEE Robotics and Automation Letters and has organized key workshops like the IROS 2025 Workshop on Contact and Impact-aware Manipulation . He is also a Guest Editor for special issues in Electronics and Frontiers in Robotics and AI .