جست‌وجوی استاد و پژوهشگر

Mark Hansenمشاهده پروفایل

استاد

• Computer Vision
• Machine Vision
• Machine Learning
• +۹ مورد دیگر

Professor Mark Hansen is a distinguished academic at the University of the West of England (UWE Bristol), holding a professorship in the Department of Engineering, Design and Mathematics within the Faculty of Environment and Technology. He is a key member of the Centre for Machine Vision at the Bristol Robotics Laboratory, where his work bridges theoretical computer vision with practical applications across multiple industries. His research portfolio spans academic and commercial projects, with a strong emphasis on translating laboratory innovations into real-world solutions through close industry partnerships. Professor Hansen earned his academic credentials from prestigious institutions, completing a BSc(Hons) in Psychology and an MSc in Computer Science at the University of Bristol before earning his PhD at UWE in 2012 with a thesis titled "3D Face Recognition Using Photometric Stereo." His educational background in both psychology and computer science has uniquely positioned him to develop biometric systems that incorporate human perception principles. Professor Hansen's research interests center around computer vision and machine learning, with particular expertise in photometric stereo techniques for 3D acquisition. His work spans multiple domains including agricultural technology (agri-tech), livestock welfare monitoring, microplastic detection, and precision farming systems. He has pioneered applications of photometric stereo for face recognition, plant phenotyping, and animal biometrics, demonstrating exceptional versatility in applying core computer vision techniques to diverse problems. His research consistently emphasizes practical implementation, with numerous projects resulting in commercialized technologies that address real-world challenges in agriculture and environmental monitoring. Analysis of Professor Hansen's recent publications reveals a strategic expansion of his core expertise in photometric stereo and 3D vision into increasingly diverse application domains. While maintaining his foundational work in biometrics and face recognition, he has successfully transitioned these techniques to agricultural contexts (pig and cow identification), environmental monitoring (microplastic detection), and sustainable food production systems (aquaponics optimization). His publication pattern shows a clear progression from fundamental computer vision research to applied interdisciplinary work addressing global challenges in food security, environmental sustainability, and animal welfare. Highly commended prize for innovation at the National Potato Industry Awards for the Harvesteye system Associate Editor for Elsevier's Computers and Electronics in Agriculture Featured on BBC Click for 3D Handprint Recognition research Featured on Netflix's "Connected" S1Ep1 for Pig Face Recognition work Professor Hansen has supervised six PhD students to completion with projects including "3D video based detection of early lameness in dairy cattle" and "3D plant phenotyping system using photometric stereo," and currently supervises seven additional PhD students through UWE, the Farscope CDT and SWBio schemes. His research is supported by substantial grant funding from diverse sources including InnovateUK, BBSRC, AHRC, EPSRC, JPIAMR, and international collaborations with institutions such as Imperial College, Notre Dame University, Bristol University, Manchester University, SRUC, and others. Current major projects include Intellipig (pig health monitoring), Mealworm protein production automation, FARM interventions to Control Antimicrobial Resistance, Pig ID tracking systems, and microplastic monitoring in home environments. Professor Hansen leads research within the Centre for Machine Vision at the Bristol Robotics Laboratory, a world-class facility that fosters interdisciplinary collaboration between computer scientists, engineers, and domain experts from agriculture, environmental science, and healthcare. His team includes three dedicated research staff working on 3D Face Recognition, Photometric Stereo, 3D acquisition technologies, reflectance mapping, and agri-technology applications. The collaborative nature of his work is evident in the extensive network of academic and industry partners spanning multiple continents, reflecting the practical impact and interdisciplinary relevance of his research.

Qunfen Qiمشاهده پروفایل

مدرس ارشد

• Knowledge modelling for manufacturing
• Smart information systems
• Category theory
• +۷ مورد دیگر

Dr. Qunfen Qi is a Senior Lecturer at the University of Bristol's School of Electrical, Electronic and Mechanical Engineering, where she leads groundbreaking research at the intersection of mathematics, informatics, and manufacturing. With over 13 years of experience, she specializes in developing smart decision-making tools for product design and inspection using category theory as a foundational mathematical language. Her work bridges multiple disciplines to create more intelligent and transparent manufacturing systems that can 'think' like human experts. Dr. Qi's research focuses on knowledge modeling for manufacturing, with particular emphasis on smart information systems, abstract mathematical theory (category theory), Geometrical Product Specifications and Verification (GPS), Additive Manufacturing, surface design, and metrology. Her unique approach applies category theory to simplify complex relationships in manufacturing data, enabling better decision-making in digital manufacturing environments. This work has become increasingly relevant as manufacturing embraces advanced digital technologies like cyber-physical systems, IoT, and AI, where managing multiple levels of abstraction is critical. Her publication record shows a clear trajectory toward more sophisticated applications of mathematical frameworks in manufacturing, with growing integration of category theory with AI approaches, particularly in explainable AI for manufacturing contexts. Recent work demonstrates increasing practical applications in additive manufacturing, quality control, and digital twin development, with strong emphasis on how these systems can explain their decisions at multiple levels of detail. Scientific Awards and Recognition UKRI EPSRC Innovation Fellow (2018-2022) EPSRC Peer Review College Member (ranked top 4% for participation in 2020/21) Project lead for ISO/TR 24331-2 Geometrical Product Specification development ISO Technical Committee Expert (TC 213, TC 184 WG26, JWG 21) BSI Technical Committee Member (AMT_4, TPR1/1, TPR1/8, TPR1/9) Co-Chair of CIRP CAT 2024 (18th International Conference on Computer-Aided Tolerancing) Dr. Qi actively recruits PhD students interested in explainable machine learning, mathematical AI foundations, and smart manufacturing applications. Her research portfolio includes four major projects totaling significant funding, including an EPSRC Innovation Fellowship for 'A semantic infrastructure for advanced manufacturing' and a Royal Society International Exchanges project on mathematical foundations for digital manufacturing systems. She maintains strong international collaborations, particularly with Italian institutions like Politecnico di Milano and CNR-STIIMA, reflecting her global impact in advancing the mathematical foundations needed for next-generation intelligent manufacturing systems.

• Geotechnical Engineering
• Soil Dynamics
• Granular Mechanics
• +۶ مورد دیگر

Dr. Yujie Qi is a Senior Lecturer at the University of Technology Sydney (UTS), specifically within the School of Civil and Environmental Engineering. She serves as Program Co-leader of the UTS Transport Research Center (TRC) within the Faculty of Engineering and Information Technology. Her academic journey began with Bachelor's and Master's degrees in (Civil/Geotechnical) Engineering at Zhengzhou University, China, followed by a PhD from the University of Wollongong (UOW), Australia, which she completed in 2018 with an 'Industry Engagement Award' and 'Special Commendation' for outstanding doctoral research. Dr. Qi's educational background includes: PhD in Geotechnical Engineering from University of Wollongong (2014-2018) Master's degree in Geotechnical Engineering from Zhengzhou University (2011-2014) Bachelor's degree in Engineering from Zhengzhou University (2007-2011) Bachelor of Art from Zhengzhou University (2009-2011) Dr. Qi's primary research focuses on soil dynamics, granular mechanics, geo-mathematical modeling, and large-scale dynamic testing , with particular emphasis on the innovative use of waste and marginal materials in transport infrastructure . Her pioneering work on energy-absorbing materials has revolutionized approaches to railway track design, specifically through the utilization of waste rubber to enhance rail substructure performance. She has conducted extensive investigations on three key waste materials: discarded coal wash from Australian coal mines, steel furnace slag from steel manufacturing plants, and rubber crumbs derived from off-the-road vehicle tires. For the first time, she identified optimal ratios of these waste materials to enhance railway longevity and developed energy-absorbing concepts to analyze and interpret track substructure deformation mechanisms. Dr. Qi has been instrumental in introducing energy-absorbing materials to enhance rail substructure performance, and her research has been recognized by national and international firms including Bridgestone and Sydney Trains. The recent publications by Dr. Qi demonstrate a strong focus on sustainable transportation infrastructure using recycled materials, particularly rubber-based solutions for railway engineering. Her work spans from fundamental material characterization to large-scale field testing, with particular emphasis on energy absorption properties, track performance improvement, and circular economy applications. The research consistently bridges theoretical modeling with practical engineering applications, showing how waste materials can be effectively repurposed to solve infrastructure challenges while reducing environmental impact. A notable trend in her recent work is the integration of machine learning methodologies with traditional geotechnical approaches to predict material behavior and optimize infrastructure design, as evidenced by her publications on hybrid ML methodologies for predicting rail ballast breakage. Dr. Qi's exceptional contributions have been recognized with numerous prestigious awards: John Carter Young Professional Award (2024) Sir M. Visvesvaraya Award from ICE (UK) (2023) Bright Spark Lecture Award from ISSMGE (2018) Best Paper Award at the 9th Asian Young Geotechnical Engineers Conference (2019) Award for Industry Engagement Impact from University of Wollongong (2017) Examiners' Commendation for Outstanding Thesis (2018) Dr. Qi has successfully secured significant research funding, including ARC Discovery Projects (DP220102862) titled 'The Role of Energy Absorbing Rubber Grid on Ballast Track Performance' (2022-2025) and ARC Linkage Projects (LP200200915) titled 'Field Data based Predictive Maintenance and Enhanced Track Design Procedure' (2021-2025). She actively supervises research students across multiple projects focused on energy-absorbing materials, track performance, and waste material applications in transportation infrastructure. Her teaching portfolio includes subjects such as Problematic Soils and Ground Improvement Techniques, Road Engineering Practice, Mechanics of Solids, and Soil Behaviour, where she has served as coordinator, teacher, and tutor. As Program Co-leader of the UTS Transport Research Center, Dr. Qi collaborates with a multidisciplinary team of researchers focused on innovative transportation solutions. Her laboratory work involves advanced testing facilities including large-scale process simulation testing apparatus (PSTA) and prototype cubic triaxial facilities for evaluating material behavior under cyclic loading conditions. The research group has conducted significant field trials, including a fully instrumented track section at Chullora, New South Wales, to validate laboratory findings in real-world conditions. Dr. Qi's team maintains strong industry connections with organizations such as Sydney Trains and Bridgestone, ensuring practical relevance and implementation potential for their research outcomes, while promoting sustainable and cost-effective practices in transportation infrastructure.

• Combinatorial Optimization
• Operations Research
• Artificial Intelligence
• +۳ مورد دیگر

Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.

Alfred Hößمشاهده پروفایل

استاد

• Autonomous Driving
• Automotive Electronics
• Electric Mobility
• +۶ مورد دیگر

Prof. Dr. Alfred Höß is a Professor of Electrical Engineering at the Amberg-Weiden University of Applied Sciences, where he has served since 1995. He chairs the examination committee for multiple engineering programs including Electrical and Information Technology, Software Systems Technology, and Industrial IT. His academic leadership extends through over a decade of service on the university senate and various planning committees. Dr. Höß completed his electrical engineering studies at Friedrich-Alexander-Universität Erlangen-Nürnberg (1983-1987), earning his diploma with distinction in 1988. He earned his PhD from Ruhr-Universität Bochum in 1991 with distinction, followed by industry experience at Siemens AG in both medical and automotive divisions before joining academia. Friedrich-Alexander-Universität Erlangen-Nürnberg: Electrical Engineering (1983-1987) Ruhr-Universität Bochum: PhD in High-Frequency Technology (1988-1991) His research spans cutting-edge automotive technologies with particular focus on autonomous driving systems, electric mobility solutions, and wireless communication architectures. Dr. Höß leads multiple EU-funded research projects including Archimedes, AI4CSM, AUTBUS, and Powerized, with emphasis on practical implementations for real-world transportation challenges. His work integrates artificial intelligence with edge computing to solve complex problems in vehicle communication, battery management, and autonomous navigation systems. His publication portfolio demonstrates strong emphasis on practical applications of machine learning in automotive contexts, particularly in range prediction for electric vehicles, federated learning for battery management, and communication systems for autonomous vehicles operating in challenging environments. His research shows consistent progression from fundamental electrical engineering principles to advanced AI integration in transportation systems. Dr. Höß has received notable academic recognition including the Diplompreis Elektrotechnik in 1988 and the Gebrüder-Eickhoff-Preis in 1992 for his doctoral work. Diplompreis Elektrotechnik (1988) Gebrüder-Eickhoff-Preis (1992) He actively mentors numerous graduate students across multiple research projects, supervising master's theses and research assistantships. His laboratory for electrical measurement technology serves as the foundation for hands-on student research. Dr. Höß secures substantial research funding through EU projects and industry collaborations, focusing on practical implementations of advanced automotive technologies. His administrative leadership includes chairing examination committees for multiple engineering programs, demonstrating his commitment to academic excellence and curriculum development. Dr. Höß directs the Electrical Measurement Technology Laboratory at Amberg-Weiden UAS, which serves as the primary research facility for his automotive electronics work. His research teams collaborate across multiple EU-funded projects including ADACORSA for drone communications, PRYSTINE for programmable automotive intelligence systems, and AUTBUS for rural autonomous transportation solutions. These interdisciplinary teams combine expertise in electrical engineering, computer science, and applied mathematics to tackle complex challenges in modern mobility systems.