جست‌وجوی استاد و پژوهشگر

Catherine Menonمشاهده پروفایل

عضو هیئت علمی

• Ethics of Artificial Intelligence
• Safety-critical autonomous systems
• Assistive and rehabilitative robotics
• +۳ مورد دیگر

Dr Catherine Menon is a Reader in the Department of Computer Science, School of Physics, Engineering & Computer Science at the University of Hertfordshire. She specialises in the ethics, safety, security and trustworthiness of AI-enabled systems, particularly assistive and rehabilitative robotics, and contributes actively to international robotics standards. Research Interests Menon’s work sits at the intersection of computer science, philosophy and engineering. She investigates how ethical hazards emerge in public-facing AI and how rigorous design can mitigate them. Her recent focus is the interplay between safety, security, ethical and trust requirements in assistive robots used in health and social care. She also explores creative approaches to assurance arguments for safety-critical autonomous systems. She has applied these interests in defence, nuclear and healthcare domains and holds leadership roles on several British and international standards committees (BSI AMT/10, ISO/TC 299, IEEE P7009, IET Safety & Security Code of Practice). Grants & Projects STRAWMAN (PI) – UKRI-funded scoping study for an industry safety standard for assistive robots in health and social care (2024-2025). CuPick (Co-I) – Innovate UK / Glinwell joint project developing robotic cucumber harvesting systems (2019-2022). SocCred (PI) – 5-month feasibility study on safety and social credibility of assistive robots (2019). Standards & Professional Service Member, BSI AMT/10 & AMT/10/1 – UK expert input to ISO/TC 299 (robotics standardisation). Member, IEEE P7009 Working Group – fail-safe design of autonomous systems. Founding member, Safety of Autonomous Systems Working Group. Programme Committee member, International Cross-Industry Safety Committee. Dr Menon has authored over 45 peer-reviewed articles and is regularly invited to speak on AI ethics and safety.

Massimo Callisto De Donatoمشاهده پروفایل

استادیار

• Internet of Things
• Digital Twin
• Machine Learning
• +۶ مورد دیگر

Massimo Callisto De Donato is an Assistant Professor specializing in Internet of Things, Digital Twin, and Machine Learning applications for real-world systems. His research addresses critical challenges in environmental management, workplace safety, and disaster response through advanced computing solutions. His primary research domains include: IoT infrastructure for real-time industrial and environmental monitoring Digital Twin development for process simulation and prediction Machine Learning frameworks for safety-critical applications Human behavior analysis using community detection algorithms ICT systems for earthquake rescue operations Analysis of his 2020-2025 publications reveals a consistent trajectory toward integrating Digital Twin and IoT technologies with machine learning for predictive capabilities. Key application areas include wastewater management (decanter centrifuge modeling), smart living environments, and earthquake disaster response systems, with increasing emphasis on real-time simulation and human activity recognition. No scientific awards are documented in the available information. Details regarding student supervision, research grants, and laboratory affiliations are not provided in the source materials.

• Mobile Healthcare Systems
• Artificial Intelligence in Medicine
• Machine Learning Applications
• +۵ مورد دیگر

Edward Sykes serves as an Adjunct Associate Professor in the Department of Computing and Software within the Faculty of Engineering at McMaster University. His academic work bridges computer science with healthcare applications, focusing on innovative technological solutions for real-world medical challenges. Dr. Sykes' research interests span mobile healthcare systems, artificial intelligence applications in medicine, and context-aware computing. His work demonstrates a strong commitment to developing practical mHealth technologies that address critical healthcare needs, particularly in elderly care, remote patient monitoring, and diagnostic support systems. His research trajectory shows a consistent evolution from earlier work in educational technology toward increasingly sophisticated healthcare applications of AI and mobile computing. Analysis of his recent publications reveals a strong emphasis on AI-driven healthcare solutions, with particular focus on fall detection systems, fracture risk assessment, and remote patient monitoring. His work integrates multiple technical domains including machine learning, computer vision, mobile computing, and human-computer interaction to create comprehensive healthcare technology solutions. The interdisciplinary nature of his research demonstrates strong connections between computer science fundamentals and practical healthcare applications. His scholarly contributions include numerous publications in healthcare technology venues, with recent work appearing in Communications in Computer and Information Science, Health Systems, and Digital Health journals. His research has been referenced in patents and has garnered attention across academic platforms. Dr. Sykes has maintained an active research program with consistent publication output, demonstrating particular productivity in recent years with multiple publications in 2023-2025. His work shows strong potential for real-world healthcare impact, particularly in the areas of elderly care technology and AI-assisted medical diagnostics.

Yufeng Geمشاهده پروفایل

استاد

• Precision Agriculture
• Remote Sensing
• Machine Learning in Agriculture
• +۳ مورد دیگر

Yufeng Ge serves as the Eberhard Professor of Agriculture in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln. His research program integrates cutting-edge technologies to address critical challenges in agricultural sustainability and productivity. Dr. Ge's work centers on precision agriculture with emphasis on UAV-based remote sensing and machine learning for real-time field applications. Key focus areas include plant phenotyping for crop breeding programs, soil spectroscopy for rapid soil property assessment, and irrigation management systems enhanced by AI. His research leverages edge computing to deploy lightweight models directly on agricultural devices, enabling on-farm data processing for weed detection, biomass evaluation, and drought response monitoring. Analysis of his 2023-2025 publications reveals a strong trend toward scalable agricultural monitoring systems combining satellite imagery, UAV data, and proximal sensors. Notable advancements include generative AI for irrigation decisions, zero-shot segmentation for phenotyping, and global soil spectral grids. His work consistently addresses practical implementation challenges in field conditions while advancing theoretical frameworks for crop-water-soil interactions.

• Precision Agriculture
• Agricultural Machinery
• Viticulture Mechanization
• +۴ مورد دیگر

Pietro Catania serves as a Full Professor in the Department of Agricultural, Food and Forestry Sciences at the University of Palermo's Polytechnic School. His office is located in Room No. 135 of the SAAF Department, where he holds regular office hours on Mondays and Wednesdays from 11:00 to 13:00, as well as at the Viticulture and Enology Degree Course Headquarters. Professor Catania has maintained a consistent academic presence since at least 2009, teaching specialized courses in agricultural mechanization with particular expertise in viticulture systems and precision farming technologies. Professor Catania's research spans several critical areas of modern agricultural engineering: Precision Agriculture and Smart Farming Systems Drone and Remote Sensing Applications in Agriculture Viticulture Mechanization and Oenological Plants Agricultural Machinery Design and Ergonomics Sustainable Resource Management in Mediterranean Agriculture Post-harvest Technology and Processing Systems His recent scholarly output demonstrates a clear focus on cutting-edge technological applications in agriculture, particularly the integration of drones, sensors, and AI in vineyard and olive orchard management. Professor Catania's work bridges traditional agricultural practices with modern technological innovations, addressing practical challenges in irrigation management, precision spraying, yield mapping, and sustainable resource use across Sicilian agricultural landscapes. His research methodology often combines field studies with advanced data analytics to develop practical solutions for farmers. Professor Catania maintains an active teaching schedule for the 2025/2026 academic year, instructing courses such as 'Viticulture Mechanisation and Oenological Plants,' 'Mechanics and Agricultural Mechanisation,' 'Use of Drones and Machines for Precision Agriculture,' and 'Green Landscape Systems and Maintenance.' His student evaluations over multiple academic years (2014-2024) consistently reflect strong engagement with students across various agricultural technology programs.

Adil Khanمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۷ مورد دیگر

Adil Khan is a Professor of Machine Learning and Artificial Intelligence at the University of Hull, affiliated with the School of Computer Science within the Faculty of Science and Engineering. His research focuses on advancing machine learning techniques, including deep learning, domain adaptation, adversarial robustness, and fairness in AI, with applications in remote sensing, medical image analysis, and natural language processing. He has led significant projects funded by organizations like Huawei and the Analytical Center of Artificial Intelligence of Innopolis University, addressing challenges such as improving model generalization and mitigating catastrophic forgetting in neural networks. Recent work includes developing novel attention mechanisms and Mamba models for hyperspectral image classification, as well as exploring cross-modal retrieval using large language models (LLMs). His theoretical contributions span foundational questions in ML, such as training with limited data, adversarial robustness, and ethical AI. Notable projects include the £10 million 'Fair, Robust and Life-long Machine Learning' initiative, advancing representation learning for fair and robust models. Additionally, he serves as an external examiner for the London School of Innovation's MSc in Applied AI and Machine Learning. His publications emphasize innovative solutions in computer vision, domain adaptation, and healthcare applications, with a focus on practical industrial implementations and rigorous evaluation methodologies. Key grants include the £500,000 Wolfson Foundation grant for advanced imaging equipment and the Innovate UK-funded 'AKT TRUST-LLM' project on trustworthy LLMs. His research bridges theoretical advancements with real-world applications, reflecting a commitment to both academic rigor and societal impact.

• Computer Vision
• Machine Learning
• Optimization

Adel Bibi is a Senior Researcher and Junior Research Fellow at the University of Oxford's Department of Engineering Science. His research focuses on fundamental problems in computer vision, machine learning, and optimization. Recent work addresses critical challenges in AI safety including adversarial robustness, model interpretability, and machine unlearning. His publications focus on developing certifiable methods for uncertainty calibration, mitigating risks in large language models, and creating intrinsically interpretable architectures. Dr. Bibi has been recognized with multiple outstanding reviewer awards at top computer vision and machine learning conferences. He received the Best Paper Award at the Optimization and Big Data conference and serves as Area Chair for AAAI. His research contributes to building more reliable and transparent AI systems.

Dimitrios Kanoulasمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dimitrios Kanoulas is a Professor in Robotics and AI at the Department of Computer Science, University College London (UCL). He holds the UKRI Future Leaders Fellowship and leads research in perception and learning for robotics, focusing on articulated robots navigating and manipulating in uncertain environments. His work emphasizes theoretical advancements in sensing, real-time mapping, and modeling of surface contact areas for locomotion on uneven terrain, leveraging SLAM systems and manipulation techniques. Research interests include robotics perception, SLAM, path planning, and reinforcement learning. His recent work explores adversarial attacks in path planning, high-fidelity simulators (e.g., Unreal Robotics Lab), and robust localization methods like AIR-HLoc and LiteVLoc. He also investigates humanoid robot stability, teleoperation systems, and autonomous mobile manipulators. Key contributions span legged locomotion, manipulation under uncertainty, and safety-critical control systems. His articles reflect trends in combining neural networks with traditional robotics frameworks, emphasizing real-world deployability and robustness. Beyond technical work, he emphasizes human-robot interaction through teleoperation interfaces and wearable technologies. Awards: UKRI Future Leaders Fellow Labs/Teams: RPL Lab at UCL Grants/Advising: No specific grants listed, but his research is supported by UKRI funding. Advising details not explicitly provided in text.

• AI Ethics
• Fairness in AI
• Text-to-Image Generation
• +۵ مورد دیگر

Felix Friedrich is a researcher at the Technical University of Darmstadt, Department of Computer Science, focusing on AI ethics, fairness, and safety in generative models. He collaborates extensively with Professor Kristian Kersting and other researchers in the AI field, contributing significantly to understanding and mitigating biases in text-to-image generation systems and multilingual AI models. His research interests center on ethical AI development, with particular emphasis on identifying and addressing gender stereotypes, cultural biases, and safety concerns in generative models. Friedrich's work bridges technical AI development with societal implications, examining how language and cultural contexts influence model behavior across different populations. His approach combines rigorous empirical evaluation with practical mitigation strategies to create more responsible AI systems. Friedrich's publication record shows a strong trend toward addressing critical challenges in AI safety and fairness, particularly in multimodal and multilingual contexts. His recent work develops benchmarks and evaluation frameworks to systematically assess issues like cross-linguistic safety gaps, cultural bias magnification, and emotional recognition accuracy in synthetic data. These contributions provide valuable tools for the research community to build more equitable and reliable AI systems. His work has contributed to several collaborative research initiatives focused on AI safety and ethics, working with international teams to develop comprehensive evaluation frameworks and mitigation strategies for emerging AI technologies. Friedrich's research has practical implications for AI developers seeking to create more responsible systems that account for diverse cultural contexts and user needs.

Janet Huangمشاهده پروفایل

استادیار

• Human-AI Interaction
• Creative AI
• Design of AI Systems
• +۳ مورد دیگر

Janet Huang serves as Assistant Professor in the Industrial Design department at Eindhoven University of Technology (TU/e), where she contributes to the Future Everyday group. Her research pioneers human-AI co-learning frameworks that empower designers to harness AI as a creative material for solving complex real-world problems across diverse contexts. Academic Background: PhD in Computer Science, National Taiwan University (2018) MSc in Computer Science, National Taiwan University (2008) Research Focus: Dr. Huang designs novel toolkits bridging data sensemaking and decision-making support, with emphasis on creative AI applications and ethical human-AI collaboration . Her work spans healthcare data visualization, urban play systems, and memory augmentation tools, consistently exploring how AI can enhance human creativity while addressing societal implications. She employs participatory design methods to develop systems that balance technical innovation with user-centered values. Publication Trends: Recent articles (2023-2025) reveal three dominant trajectories: (1) generative AI for domain-specific problem-solving in nursing and biodesign, (2) playful urban exploration systems examining citizen-AI tensions, and (3) memory and decision-making support tools analyzing anthropomorphism effects. These works demonstrate her signature approach of embedding technical AI research within rich human contexts. Awards & Recognition: Ph.D. Thesis Honorable Mention, TAAI 2018 MOST Postdoctoral Scholarship (Taiwan) Mingler Scholarship 2023 Best Poster Honorable Mention, CSCW 2023 Academic Contributions: Dr. Huang teaches specialized courses including Creativity and Aesthetics of Data & AI and Digital Craftsmanship , while supervising 19 student projects. Her research receives support from competitive grants including the MOST scholarship, with applications spanning healthcare innovation and sustainable community development. She actively contributes to UN Sustainable Development Goals through human-centered AI solutions. Research Environment: As core member of TU/e's Future Everyday group, she collaborates with interdisciplinary teams spanning computer science, design, and domain experts. Her lab environment emphasizes iterative prototyping and real-world deployment, particularly in healthcare settings and urban contexts where AI systems interface with complex human practices.

Joshua Pulsipherمشاهده پروفایل

استادیار

• Optimization under Uncertainty
• Data Science
• Machine Learning
• +۷ مورد دیگر

Joshua Pulsipher is an Assistant Professor of Chemical Engineering at the University of Waterloo, Canada. He joined in 2023 and leads a research group focused on interdisciplinary solutions at the intersection of chemical engineering, computer science, mathematics, and statistics. His work addresses challenges in sustainability, energy systems, and data-driven decision-making under uncertainty. He holds a B.Sc. from Brigham Young University and a Ph.D. from the University of Wisconsin-Madison, with postdoctoral training at Carnegie Mellon University. Education: B.Sc. in Chemical Engineering, Brigham Young University (2017) Ph.D. in Chemical & Biological Engineering, University of Wisconsin-Madison (2022) Postdoctoral Research, Carnegie Mellon University (2022–2023) Research Interests: Optimization under uncertainty, machine learning, sustainable systems, infinite-dimensional optimization, energy systems, wildfire mitigation, and software development for accessibility in research. His projects span wildfire management frameworks, resilient infrastructure, and software tools like InfiniteOpt.jl and FlexibilityAnalysis.jl. Awards: Best Presentation Award, JuMP-dev 2024 Travel Award, FOCAPO/CPC (2023) Plenary Speaker at AICHE Annual Meeting (2022) Teaching: Courses include CHE 322 (Numerical Methods), CHE 341 (Process Control), and CHE 521 (Process Optimization). His philosophy emphasizes active learning and inclusivity. Labs/Software: Develops open-source software frameworks such as InfiniteOpt.jl for infinite-dimensional optimization and SAFE-OCC for computer vision sensor reliability. Active in promoting reproducibility and software-driven research.

Wesley Roozingمشاهده پروفایل

استادیار

• Robotics
• Mechatronics
• Actuation Systems
• +۵ مورد دیگر

Dr. Wesley Roozing is an Assistant Professor at the Robotics and Mechatronics (RaM) group and Robotics Centre of the University of Twente, Netherlands. He holds a PhD from the Italian Institute of Technology (2018) and was a visiting student at the Australian Centre for Field Robotics. His research focuses on compliant actuation, mechatronic co-design, energy-efficient systems, bio-inspired mechanisms, and control methodologies. He actively contributes to the robotics community through roles such as chair of the euRobotics Mechatronics Topic Group, associate editor for IROS and BioRob, and organizer of ICRA events. His work emphasizes high-performance robotics for applications like jumping, grasping, and athletic feats, with publications in top journals and conferences like IJRR, T-MECH, and ICRA. Education: PhD in Robotics, Italian Institute of Technology (2018) Visiting Student, Australian Centre for Field Robotics Research interests include advanced actuation systems, motor-gearing integration, and safety-critical control strategies. He leads national/international projects and teaches courses on energy-based modeling. His research bridges electric motor innovations with mechanical design to achieve dynamic robot capabilities. Notable awards include an honorable mention for the RA-L Best Paper Award (2021). His work contributes to UN Sustainable Development Goals related to Industry, Innovation, and Infrastructure (Goal 9) through advancements in energy-efficient robotics. Advising and Grants: Coordinates research projects, advises on robotics program development, and serves on examination boards. His grants fund collaborative efforts in mechatronics and robotics education. Labs/Teams: Part of the Robotics and Mechatronics group at University of Twente, contributing to the Robotics Centre’s interdisciplinary initiatives.

• Machine Vision
• Image Processing
• Laser Sensing
• +۵ مورد دیگر

Dr. Xin Wang is an Associate Professor at the Malaysia School of Engineering, Monash University Malaysia. She holds a PhD from Nanyang Technological University (NTU) and has held roles such as Research Fellow at NTU's Robotics Research Center (2007–2009) and Assistant Professor at Universiti Tunku Abdul Rahman (2009–2012). She became a Senior Lecturer at Monash in 2012 and promoted to Associate Professor in 2018. She also served as a Visiting Professor at the National Metrology Centre (Singapore, 2011) and the Centre for Optical & Laser Engineering, NTU (2016). Research Interests: Focus on machine vision, image processing, laser sensing, optical metrology, deep learning, AI in healthcare, non-destructive testing, and structural health monitoring. She has published over 70 peer-reviewed papers and 2 book chapters. Her work aligns with UN SDGs related to innovation and industrialization. Awards: Distinguished Woman Researcher (2025) IEEE SMC Volunteer Award (2020) ITEX 2018 Silver Medal (Team) ITEX 2021 Gold Medal (Team) Teaching: Monash courses include MEC3457 (Systems and Control) and MEC4801 (Non-Destructive Testing and Inspection).

Xiaoxiao Chengمشاهده پروفایل

مدرس

• Adaptive Control Systems
• Human-Machine Interaction
• Robotics
• +۵ مورد دیگر

Xiaoxiao Cheng is a Lecturer in Engineering Systems for Robotics at the University of Manchester's Department of Electrical and Electronic Engineering. Previously, he held roles as a Research Associate at Imperial College London (2020-2023) and a Research Fellow at Stanford University (2019-2020). He earned a Ph.D. in Electrical and Electronic Engineering from The University of Melbourne (2019), M.Phil. in Mechanical Engineering from Tsinghua University (2014), and B.Eng. in Mechanical Engineering from Beijing Institute of Technology (2011). His research focuses on intelligent autonomous systems and human-machine interfaces, integrating robotics, control theory, AI, and neuroscience. Key areas include adaptive control for human-robot interaction, sensory integration, intention detection, and multi-sensory feedback for applications in manufacturing and hazardous environments. He actively explores autonomous navigation and computational modeling to enhance human-machine collaboration. Recent publications emphasize haptic feedback systems, shared control in robotic surgery, and multimodal sensory datasets. His work contributes to SDGs related to industry innovation and infrastructure, and sustainable cities. Xiaoxiao Cheng welcomes prospective PhD students in robotics, control systems, and human-machine interaction. He is open to collaborations and can be contacted at Xiaoxiao.cheng@manchester.ac.uk .

Kenneth Rutherfordمشاهده پروفایل

مدرس ارشد

• Animal Welfare
• Assessment of Animal Stress
• Pain Assessment in Farm Animals
• +۷ مورد دیگر

Dr. Kenneth Rutherford is a Senior Lecturer in Animal Welfare at Scotland's Rural College (SRUC), specializing in the assessment of animal stress, pain, and emotional states in farm animals. His work focuses on developing behavioral tests and measurements to assess negative experiences in animals resulting from farming practices. He is actively involved in multiple research projects related to animal welfare improvement and has strong collaborations with the University of Edinburgh. His educational background isn't explicitly stated in the provided text, but his extensive research supervision of PhD students with Edinburgh University suggests a strong academic foundation in animal science or veterinary medicine. Rutherford's research primarily centers on animal welfare assessment methodologies, with special emphasis on pigs and other livestock species. His work investigates the impact of prenatal stress on animal welfare outcomes, develops behavioral tests for pain assessment, and explores technological solutions like facial recognition for emotion detection in pigs. He examines how stress and suboptimal nutrition affect production and welfare across various livestock species under commercial farming conditions in the UK. His recent publications demonstrate a clear trajectory toward increasingly sophisticated welfare assessment methods, combining behavioral observation with technological innovation. His work spans from fundamental research on stress physiology to applied interventions that directly impact farming practices. The research shows strong interdisciplinary connections between animal behavior, veterinary science, and agricultural technology. Principal Investigator for Evaluation of procaine HCL local anaesthetic for pain control in lambs (2022-2023) Co-Investigator for RESAS Strategic Research Programme on improved husbandry (2022-2027) Co-Investigator for EU Exit Challenges and Opportunities for Animal Welfare project (2022-2027) Principal Investigator for body condition scoring skills project (2018-2022) Co-Investigator for EmotiPig emotion detection project (2018-2022) Dr. Rutherford actively supervises PhD students, primarily in collaboration with the University of Edinburgh, focusing on critical welfare issues including pain mitigation, prenatal stress effects, and castration alternatives. His research has significant practical implications for farming practices, with several projects directly translating scientific findings into on-farm solutions. He maintains strong connections with industry partners including Zoetis and participates in knowledge exchange through media appearances and webinars.