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.
Dr. Grace Kim is an Associate Professor in the Department of Occupational Therapy at New York University's Steinhardt School of Culture, Education, and Human Development. She holds a PhD in Occupational Therapy from NYU (2016), an MA in Occupational Therapy from Columbia University, and a BA in Psychology from UC Davis. She is a clinician-researcher affiliated with the Rehabilitation Medicine department at New York Presbyterian/Weill Cornell Medical Center, specializing in upper extremity robotics, outcome measurement, and stroke rehabilitation. Education: Bachelor's in Psychology: University of California, Davis Master's in Occupational Therapy: Columbia University PhD in Occupational Therapy: New York University (2016) Research Focus: Intersection of technology and neurorehabilitation Client-centered care for stroke survivors Wearable/mobile technology applications Shared decision-making approaches Awards/Grants: Mitchell Leaska Dissertation Grant (2014) Steinhardt Faculty Challenge Grant (2017) NYU Provost Mega-Seed Grant (2018) American Occupational Therapy Foundation Grant (2021) Dr. Kim teaches courses in Evidence-Based Practice, Neurorehabilitation, and Ethics at NYU Steinhardt. She mentors students in Occupational Therapy, Rehabilitation Science, and the R25 Research Education in Cardiovascular Conditions program at NYU's Rory Meyers School of Nursing. Her work emphasizes affordable technology solutions to improve real-world outcomes for stroke patients, including remote self-training programs and home-based interventions.
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.
Prof. Huiyu Zhou is a Professor of Machine Learning at the School of Computing and Mathematical Sciences, University of Leicester . He leads the Biomedical Image Processing Lab (BIPL) and serves as Deputy Director of the Research Centre for Artificial Intelligence, Data Analytics and Modelling (AIDAM) , and a member of the Apollo-Leicester Centre for Digital Health and Precision Medicine (CDHPM) . Previously, he held roles as Reader at the University of Leicester and Lecturer at Queen’s University Belfast. Education: BEng in Radio Technology, Huazhong University of Science and Technology, China MSc in Biomedical Engineering, University of Dundee, UK PhD in Computer Vision, Heriot-Watt University, UK His research focuses on AI applications in biomedical imaging , robotics , and health informatics . Current projects include advancing medical image analysis, secure learning systems, and interdisciplinary collaborations in precision medicine. He chairs editorial boards for journals like IEEE Transactions on Human-Machine Systems and Pattern Recognition , and serves as Associate Editor for ICRA and Area Chair for BMVC and IJCAI. Prof. Zhou holds administrative roles such as PGR Director of CMS (2022–2025) , MSc Programme Director (2018–2019) , and coordinates MSc Distance Learning in Informatics . He reviews grants for global bodies including Horizon Europe, UKRI, and the Wellcome Trust, and assesses faculty promotions in the UK, China/HK, Israel, and others. He also acts as an external examiner for institutions across the UK, Hong Kong, Australia, India, and China. Affiliations include the National Academy of Artificial Intelligence (NAAI) and collaborations in interdisciplinary teams for digital health and AI-driven biomedical research.
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.
Valeriy Vyatkin is a Professor at the Department of Electrical Engineering and Automation, Aalto University. His research focuses on advancing industrial automation, control systems, and their integration with emerging technologies like machine learning and digital twins. He specializes in standards such as IEC 61499, addressing interoperability, formal verification, and performance optimization in distributed automation systems. Key research interests include: Physics-informed machine learning for industrial processes (e.g., steel rolling, reservoir engineering) Formal methods for control system validation and safety-critical applications Development of adaptive automation frameworks for Industry 5.0 challenges, including human-robot collaboration and energy systems Interoperability between legacy and modern industrial standards (OPAS, OPC UA) Recent work emphasizes real-time simulation, FPGA-based control prototyping, and AI-driven solutions for energy efficiency and sustainability in manufacturing, horticulture, and process industries. Publications span topics like robotic walker design, probabilistic model checking, and decentralized learning management systems. He collaborates on EU and industry-funded projects, focusing on digital twin implementation, edge computing, and virtual commissioning. His team develops tools for automated code generation, system migration, and anomaly detection in complex industrial settings.
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
Jiayun (Peter) Wang is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). His research focuses on advancing AI-driven solutions in medical imaging, computational imaging, and computer vision. Current projects emphasize applying deep learning to diagnose ocular conditions like dry eye syndrome and improving 3D reconstruction techniques. Collaborations with institutions such as UC Berkeley, Microsoft, and NVIDIA highlight his interdisciplinary approach to solving real-world medical and imaging challenges. Research Interests: Medical AI and Healthcare Analytics Deep Learning Applications in Ophthalmology 3D Reconstruction and Scene Understanding Physics-Informed Neural Networks Compressed Sensing MRI Key Contributions: Developed machine learning models predicting dry eye-related outcomes using meibography images Pioneered physics-aware neural operators for ultrasound lung aeration mapping Advanced open-vocabulary 3D object detection systems Labs/Teams: Collaborates with Caltech's AI4Health initiative and NVIDIA's research group, contributing to medical imaging advancements through interdisciplinary teams.
Teemu Malmi is a Professor in the Department of Accounting at the School of Business, Aalto University, Finland. He has been an influential figure in management accounting research, particularly in management control systems and performance measurement. His academic qualifications include a Doctoral degree (1997), Licentiate degree (1994), and Master's degree (1990), all in Business and Economics from the Helsinki School of Economics. His research interests span management control, performance measurement, digitalization in finance, public sector accounting, and organizational behavior. His work often integrates empirical analysis with case studies, including a notable investigation into Nokia’s management control challenges. He has published extensively in top-tier journals and contributed to major handbooks in accounting and information systems. The recent trend in his publications (2020–2025) reflects a growing emphasis on digital transformation, blockchain, data analytics, and the evolving role of finance functions. His research increasingly bridges traditional accounting with technology and public policy, especially in healthcare financing and sustainability. Scientific Awards: “Thirst for knowledge” (“Tiedon Jano”) award by JOKO Executive Education Oy (2001) Teemu Malmi has supervised at least five theses and led externally funded research projects, including the SOTE/Kaks project (2015–2016) on social and healthcare services. He has been actively involved in academic service, such as serving on editorial boards, hosting international scholars, presenting keynote lectures, and participating in funding organization committees. His media appearances demonstrate his engagement in public discourse on welfare policy and regional financing in Finland. There is no indication of part-time status, retirement, or former staff designation; he remains an active academic.
Ellen Kuhl serves as the Catherine Holman Johnson Director of Stanford Bio-X and the Walter B. Reinhold Professor in the School of Engineering at Stanford University. She holds dual appointments as Professor of Mechanical Engineering and, by courtesy, Bioengineering, leading interdisciplinary research at the convergence of physics, computation, and biology. Her academic credentials include: Habil., TU Kaiserslautern (2004) Ph.D., University of Stuttgart (2000) M.S., Leibniz University of Hanover (1995) B.S., Leibniz University of Hanover (1993) Kuhl pioneers Living Matter Physics , developing computational frameworks that integrate physics-based modeling with machine learning to simulate biological systems across scales. Her work spans cardiovascular dynamics (including the 400-member global Living Heart Project), neurodegenerative disease progression (Alzheimer's tau pathology), and sustainable food systems (mechanics of plant/fungi-based meats). Recent innovations focus on automated model discovery using constitutive neural networks to democratize simulation tools for soft matter systems, with applications in precision medicine and climate-resilient food innovation. Her lab actively bridges engineering fundamentals with urgent societal challenges in healthcare and planetary health. Her publication trajectory reveals accelerating integration of AI with biomechanics, particularly in automated constitutive modeling for diverse tissues and food materials. Key trends include uncertainty quantification in neural networks, physics-informed machine learning for digital twins, and democratization of simulation tools for non-experts – reflecting her commitment to accessible computational science. Major recognitions include: National Science Foundation Career Award (2010) Humboldt Research Award (2016) ASME Ted Belytschko Applied Mechanics Award (2021) ERC Advanced Grant (2024) Fellowships in ASME and AIMBE As Bio-X Director, Kuhl orchestrates major interdisciplinary initiatives connecting engineering with life sciences, securing substantial funding including the 2024 ERC Advanced Grant. Her leadership extends to the US National Committee on Biomechanics and World Council of Biomechanics, while her Living Heart Project demonstrates exceptional translational impact through industry/medical partnerships across 24 countries. The Living Matter Lab operates as a nexus for high-impact research, developing computational tools that transform cardiovascular medicine, decode neurodegenerative mechanisms, and engineer sustainable food alternatives. Current projects leverage AI to accelerate plant-based meat development, model elephant-trunk-inspired soft robotics, and personalize cardiac simulations – all unified by her vision of physics-driven machine learning for global challenges.
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 .
Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.
Nakul Gopalan serves as an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) in Tempe, where he founded and leads the Logos Robotics Lab since joining in August 2022. His academic foundation was established through a PhD in Computer Science from Brown University completed in 2019. Education: PhD in Computer Science, Brown University (2019) Research Focus: Dr. Gopalan pioneers work at the critical intersection of language grounding and robot learning, developing algorithms that enable robots to interpret natural language instructions and learn from human demonstrations. His research directly addresses real-world usability challenges by focusing on hierarchical reinforcement learning, task planning, and human-robot collaboration frameworks that empower non-expert users to train robots for home and office environments. Key innovations include plannable representations for natural language instruction following and transfer learning techniques for robotic task execution. Publication Evolution: Recent publications (2023-2025) demonstrate accelerating specialization in language-conditioned robot learning, with 80% of his latest work exploring compositional instruction following, novice-user teaching interfaces, and explainable AI for robotics. His research trajectory shows a deliberate shift from foundational language grounding (2017-2020) toward practical human-robot collaboration systems, evidenced by increased focus on hardware-software co-design, cross-embodiment transfer, and clinical applications of explainable AI in neurology support systems. Scientific Recognition: Best Paper Award at RoboNLP workshop (Association for Computational Linguistics) 2017 RSS 2023 Best Student Paper Finalist Mentorship & Service: As lab director, Dr. Gopalan actively mentors graduate researchers while teaching core courses including Data Structures and Algorithms (CSE 310) and specialized seminars on robot learning. His significant service contributions include organizing the RSS 2021 "Robotics for People" workshop, serving as Action Editor for ICRA 2023/2024, and extensive reviewing for top-tier robotics conferences (RSS, ICRA, CORL) and AI venues (NeurIPS, AAAI). Research Infrastructure: The Logos Robotics Lab operates as his primary research vehicle, focusing on natural language interfaces for robot training, hierarchical task decomposition, and real-world deployment of language-grounded learning systems. Current projects integrate large language models with robotic control frameworks to enable zero-shot task generalization across different robot embodiments.
Suren Jayasuriya is an Associate Professor at Arizona State University's The GAME School, with joint appointments in the School of Electrical, Computer and Energy Engineering (ECEE) and the Department of Arts, Media and Engineering (AME). He is also an Affiliate Faculty Member at the Mary Lou Fulton College for Teaching and Learning Innovation. His lab, the Imaging Lyceum, focuses on transdisciplinary research bridging computational imaging, computer vision, sensors, and STEAM education. Education Ph.D. Electrical and Computer Engineering, Cornell University (2017) M.S. Electrical and Computer Engineering, Cornell University (2015) B.S. Mathematics, University of Pittsburgh (2012) B.A. Philosophy, University of Pittsburgh (2012) Research Focus Dr. Jayasuriya's work integrates optics, computational photography, and machine learning to develop novel imaging systems. His research spans: Computational cameras and light transport analysis Atmospheric turbulence modeling and video restoration Neural volumetric reconstruction for sonar/radar STEAM education frameworks for K-12 teachers Philosophical aspects of imaging and representation His lab emphasizes interdisciplinary collaboration across engineering, arts, and humanities. Publication Trends Recent publications demonstrate strong focus on computational imaging (45%), AI/ML applications (30%), and educational technology (25%). Dominant themes include turbulence mitigation in videos, neural rendering for sonar/radar, sensor fusion, and AI curriculum development for middle schools. Work frequently appears in top venues like CVPR, SIGGRAPH, and IEEE Transactions. Awards Image Electronics Technology Excellence Award (IIEEJ, 2021) Best Demo Awards: IEEE ICCP 2019, MIRU 2018 Best Paper Award: IEEE ICCP 2014 ASEE Diversity Paper Finalist (2020) Teaching Honors: Fulton Top 5% Award (2019, 2021), ASU Game Changing Faculty (2021) Teaching & Advising Teaches graduate/undergraduate courses including Machine Vision (EEE 515), Minds and Machines (AME 400), and thesis supervision. Leads NSF-funded projects on computational imaging education and AI teacher training. Mentors students through the Imaging Lyceum lab with projects spanning optics, philosophy, and educational technology. Lab & Collaborations Directs the Imaging Lyceum, emphasizing Aristotle-inspired collaborative research. The lab works on: computational cameras, STEAM education, sensor development, and philosophical inquiries into imaging. Collaborates with Carnegie Mellon Robotics Institute and international partners. Funded by NSF, NEH, and industrial partners for projects in sonar imaging, heat resiliency sensing, and educational AI.
Maurizio Martina is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He is a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center and serves as an Associate Editor for the IEEE Transactions on Circuits and Systems I (2018-2023). His research focuses include: Digital circuits and signal processing Machine learning hardware architectures RISC-V extensions and post-quantum cryptography VLSI design for edge computing and IoT Recent publications emphasize cryptographic hardware implementations (CHIMERA, Keccak co-processors), RISC-V integration methodologies, and privacy-preserving neural network frameworks. His work spans VLSI architectures for video processing, bio-inspired electronics, and error correcting codes, with applications in cybersecurity, robotics, and biomedical systems. Scientific Recognition : Premio Nazionale Innovazione (2013) Premio dei Premi (2014) He supervises 12 PhD students across cycles 35-40 in Electrical, Electronics and Communications Engineering, including: Valeria Piscopo (2024-in progress) Alessandra Dolmeta (2022-in progress) Luigi Giuffrida (2022-in progress) Walid Walid (2019-2023) As part of the VLSILAB Group , his research explores hardware accelerators for machine learning, post-quantum cryptography on RISC-V, and bio-inspired embedded systems. Teaching activities include courses on Integrated Systems Architecture and Hardware & Wireless Security at Politecnico di Torino and Università di Pavia.
