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Mark StampView profile

Professor

• Information Security
• Machine Learning
• Malware Analysis
• +5 more

Mark Stamp is a Professor in the Department of Computer Science at San Jose State University (SJSU), part of the College of Engineering. His research focuses on information security, machine learning, malware analysis, and cryptography, with a strong emphasis on applying these fields to cybersecurity challenges. He has authored multiple textbooks, including Information Security: Principles and Practice and Introduction to Machine Learning with Applications in Information Security . Education: PhD in Mathematics (Texas Tech University, 1992), MS in Mathematics (Texas Tech, 1988), BS in Computer Science (Morningside College, 1983). Research interests include malware detection using machine learning, cryptographic algorithms, and adversarial attack analysis. His work spans theoretical research and practical applications, such as developing tools for malware clustering, steganography analysis, and secure communication protocols. Recent projects include AI-driven precision agriculture and detecting AI-generated content. Teaching includes courses on information security (CS 166), machine learning (CS 171), and cryptography. He advises numerous graduate and undergraduate students, many of whom contribute to his research in cybersecurity and AI. Publications span over 200 papers in journals like Journal of Computer Virology and Hacking Techniques and IEEE Transactions . He has also contributed to conferences and edited several Springer volumes on AI and cybersecurity. His work often bridges academic research with real-world security challenges.

Fabio TostiView profile

Professor

• Ground Penetrating Radar (GPR)
• Non-Destructive Testing (NDT)
• Civil Infrastructure Monitoring
• +7 more

Professor Fabio Tosti is a Professor of Civil Engineering and Director of the Faringdon Research Centre at the University of West London , School of Computing and Engineering. He holds a PhD and MSc from Roma Tre University, Italy, and has been a key figure in advancing Ground Penetrating Radar (GPR) and non-destructive testing (NDT) in civil and environmental engineering. PhD in Civil Engineering, Roma Tre University (2014) MSc in Road Transportation and Infrastructures, Roma Tre University (2010) His research centers on non-destructive evaluation of civil infrastructure using GPR, microwave tomography, and remote sensing (e.g., InSAR). He has led projects within the COST Action TU1208 and co-convener of GPR sessions at the EGU General Assembly . He teaches across BEng, MEng, and MSc programs in civil and environmental engineering. The articles listed span from 2020 to 2025, showcasing a consistent focus on infrastructure and environmental monitoring. Key research trends include bridge and runway deformation monitoring using satellite radar (InSAR), tree root and trunk assessment via GPR, data fusion of multi-sensor systems, and advanced signal processing techniques like reverse-time migration and joint inversion. His work bridges geophysics, civil engineering, and environmental science, with applications in transportation, urban forestry, and heritage conservation. Notable scientific recognition includes the GI Division Outstanding ECS Award Lecture at EGU 2017. Prof. Tosti actively supervises research, collaborates internationally, and contributes to editorial boards, including as Assistant to Editors for Advances in Transportation Studies . He has led and participated in numerous European and Italian research projects, demonstrating sustained funding and leadership. His work often involves interdisciplinary teams, including researchers from Italy, the UK, and the Netherlands. He is a recognized expert in transport infrastructure health monitoring , with a strong emphasis on smart, non-invasive methods for roads, railways, bridges, and airport runways. His lab and research group focus on developing integrated sensing systems combining GPR, InSAR, LiDAR, and machine learning for real-world engineering challenges.

• Agricultural Automation
• Robotics
• Control Systems
• +5 more

Timo Oksanen is a Visiting Professor at Aalto University's Department of Electrical Engineering and Automation. His research focuses on agricultural automation, robotics, and control systems, with a strong emphasis on ISO 11783 vehicle network standards and autonomous systems. He collaborates with industry partners to develop next-generation agricultural machinery solutions, including automated guidance systems, sensor integration, and cybersecurity protocols. Oksanen leads the Autonomous Systems research group and has authored over 100 publications since 2009. Key research areas include precision agriculture technologies, machine learning applications in farming, and the integration of OPC UA with agricultural communication standards. Recent work addresses challenges in autonomous vehicle navigation, dust-resistant vision systems, and safety-critical perception architectures for automated field operations. Notable projects include the AGRIX initiative for ISOBUS-compatible implements and the development of a next-generation task controller using OPC Unified Architecture. Oksanen has also contributed to international standards for tractor-implement communication and four-wheel steering systems. His work bridges academic research with industrial applications, addressing both technical innovations (e.g., LIDAR-based object detection) and operational efficiency improvements in farm machinery fleets.

Zhiyi HuangView profile

Associate Professor

• Theoretical Computer Science
• Algorithmic Game Theory
• Online Algorithms
• +3 more

Zhiyi Huang is an Associate Professor of Computer Science at the University of Hong Kong, leading the Computer Science Division within the School of Computing and Data Science. He holds a PhD from the University of Pennsylvania (2013) and completed a postdoctoral fellowship at Stanford University (2013–2014). His research focuses on Theoretical Computer Science, Algorithmic Game Theory, Online Algorithms, and Differential Privacy, with notable contributions to Machine Learning and Computer Networks. Education: PhD in Computer and Information Science, University of Pennsylvania (2013) Postdoctoral Researcher, Stanford University (2013–2014) Bachelor's Degree from the Yao Class at Tsinghua University (2008) Research interests span foundational areas including algorithmic game theory, online optimization, and privacy-preserving mechanisms. He has pioneered work on revenue maximization in single-parameter settings and developed novel frameworks for analyzing price of anarchy in game theory. Key Awards: Early Career Award (Research Grant Council of Hong Kong, 2014) Best Paper Award at ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2015) Morris and Dorothy Rubinoff Dissertation Award (2013) Simons Graduate Fellowship in Theoretical Computer Science (2012–2013) Recent grants include studies on algorithmic foundations of Bayesian mechanism design (HK$675,647), online primal dual techniques (HK$496,028), and privacy-preserving mechanisms (HK$931,737). His work bridges theoretical advancements with practical applications in healthcare, autonomous systems, and cybersecurity. Notable Projects: Medical predictive systems for acute cardiopulmonary events AI-driven maritime navigation using AIS data Secure federated learning frameworks with blockchain

Eduardo AlonsoView profile

Professor

• Artificial Intelligence
• Reinforcement Learning
• Computational Neuroscience
• +2 more

Professor Eduardo Alonso is Director of the Artificial Intelligence Research Centre (CitAI) and Department Research Director at City St George's, University of London. His research bridges novel AI techniques with Explainable AI and Artificial General Intelligence, with significant focus on legal and ethical implications. Research spans: Computational neuroscience and evolutionary biology modeling Deep learning architectures for reinforcement learning Mathematical models of emergence in complex systems Industrial AI applications with societal impact Professor Alonso has secured over £1M in funding from Innovate UK, EU EIT-Digital, and US NSF grants. He currently supervises 13 PhD students working on ethical AI, reinforcement learning, and cybersecurity. Recent publications focus on transformer architectures for multi-agent systems, power grid optimization via GNNs, and adversarial robustness in security systems. Awarded the IEEE Computational Intelligence Society Spotlight Paper Award in 2013.

• Computer Vision
• 3D Reconstruction
• Perceptual Information Processing
• +7 more

SATO Jun holds the position of Professor at the Department of Information Engineering (メディア情報分野) within the Faculty of Engineering at Nagoya Institute of Technology. He received his Ph.D. in Information Engineering from the University of Cambridge (1993–1996) and previously served as a Research Assistant at Cambridge (1996–1998). His research focuses on perceptual information processing and intelligent informatics, with specializations in computer vision, 3D reconstruction, and optical engineering applications. He has authored influential books like Computer Vision - Geometry of Vision (1999) and Computer Graphics (2017), and contributed to international publications such as Springer's Computer Vision: A Reference Guide (2020). Key professional roles include serving as President of the IEEE Nagoya Branch since 2023, Associate Editor of the International Journal of Computer Vision (Springer, 2010–present), and committee member for various organizations including Japan's Ministry of Education (2015–present) and the Nagoya City Business Potential Evaluation Committee (2008–2015). He has been recognized with prestigious awards including the BMVC Best Science Paper Prize (1994, 1997) and ITE Niwa-Takayanagi Prize (2015). His research extends to industrial collaborations, evidenced by patents like the "3D Information Presentation Device" (2014–2017) and "Position Detecting Device" (2016–2019). Recent work emphasizes applications in automotive safety, occluded object reconstruction, and novel imaging systems using advanced optical configurations and neural networks.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +3 more

Prof. Felix Bießmann holds a professorship in Computer Science and Media at Berlin University of Applied Sciences' Department VI. His research focuses on machine learning applications in diverse fields including healthcare, urban planning, environmental science, and robotics through his Cognitive Algorithms Lab. He teaches courses such as Machine Learning, Deep Learning, and Data Science Workflows, alongside roles at TU Berlin and Korea University. Education: PhD (Dr. rer. nat.) in Natural Sciences Research interests span machine learning theory and practical implementations across domains like computer vision, generative AI, and sensor data analysis. His work addresses challenges in automated systems, healthcare monitoring, and sustainable technologies. Recent student theses explore topics like license plate recognition, adaptive game soundtracks, and bird song detection using TinyML. Collaborations include projects with the Charité Berlin and Robert-Koch Institute. Lab: Cognitive Algorithms Lab (developing machine learning methods/applications) Contact: felix.biessmann@bht-berlin.de | Office D138, Berlin University of Applied Sciences.

Chunming QiaoView profile

Professor

• Cyber-Physical Systems
• Optical Networks
• Wireless Networks
• +6 more

Dr. Chunming Qiao is a SUNY Distinguished Professor and Chair of the Department of Computer Science and Engineering at the University at Buffalo (SUNY) , leading the Lab for Advanced Network Design, Evaluation and Research (LANDR) since 1993. His work spans cyber-physical systems , optical networks , and Internet of Things (IoT) , with a focus on safety, reliability, and protocol design. Education: PhD in Computer Science from the University of Pittsburgh (1993) BS in Computer Science and Engineering from the University of Science and Technology of China (1985) Dr. Qiao’s research interests combine theoretical and applied network design, including autonomous vehicles , quantum computing , and cloud services . He pioneered optical burst switching (OBS) and iCAR systems for wireless convergence, cited in BusinessWeek and Wireless Europe . His recent publications emphasize quantum networking , federated learning , and autonomous driving security , with projects on entanglement routing , edge inference optimization , and LiDAR adversarial attacks . Articles span IEEE and ACM venues , and include best paper awards . Scientific Awards: TC-CSR Distinguished Technical Achievement Award (2015) SUNY Chancellor's Award for Excellence (2013) IEEE Fellow (2009) UB Exceptional Scholar-Sustained Achievement Award (2005) Dr. Qiao has secured over two dozen NSF grants and collaborations with Google , Cisco , and NEC Labs . His 7 US patents and consulting experience highlight his industry impact, while his editorial roles and conference leadership underscore academic influence. He actively contributes to multi-disciplinary research through the New York State Center of Excellence in Bioinformatics and Life Sciences and CEDAR , advancing high-performance computing and document analysis .

Eunsik KimView profile

Professor

• Ergonomics
• Biomechanics
• Human Factors Engineering
• +5 more

Eunsik Kim is a Professor at the University of Windsor , holding a position in the Department of Mechanical, Automotive, and Materials Engineering within the Faculty of Engineering . His research focuses on ergonomics, occupational safety, and biomechanics, with applications in automotive design, workplace safety, and human-machine interaction. He leads the Occupational Safety and Ergonomics research lab , where students explore real-world solutions for challenges such as driver fatigue, musculoskeletal risks in manual labor, and automation in agriculture. Key research interests include: Ergonomics : Optimizing workstation designs, automotive seating, and anti-vibration tools Biomechanics : Analyzing human posture, musculoskeletal loading, and workplace ergonomics Machine Learning : Developing AI-driven systems for posture recognition, workload prediction, and task automation Autonomous Vehicles : Investigating human factors in driving automation and takeover scenarios Education Innovation : Gamification in engineering labs to enhance student motivation and learning Collaborations include projects with Ewha Womans University (South Korea) on autonomous vehicle comfort and a prototype robotic system for mushroom harvesting. His work often bridges theoretical research with practical applications, addressing both industry and academic challenges. Awards and Recognition: Students Ilfeoma Michael and Elnaz Akhavan Rezaee received accolades at the Association of Canadian Ergonomists conference for their lab research Advising and Grants: Professor Kim mentors students in interdisciplinary projects, focusing on ergonomics, robotics, and AI. His research has led to innovations in workplace safety and automated systems, supported by institutional and collaborative funding. Labs/Teams: The Occupational Safety and Ergonomics Lab and partnerships with South Korean institutions drive cutting-edge studies in automotive safety, robotics, and human factors engineering.

• Robotics
• Underwater Robotics
• Computer Vision
• +7 more

Claire Dune is an Assistant Professor at the University of Toulon, affiliated with the COSMER Laboratory (Mechanical and Robotic Systems Design Laboratory). Her research focuses on robotics, computer vision, and underwater systems, with applications in environmental monitoring and human-robot interaction. She teaches computer science, numerical methods, image processing, and visual servoing. Institution: University of Toulon Laboratory: COSMER (Mechanical and Robotic Systems Design Laboratory) Academic Rank: Assistant Professor Email: claire.dune@univ-tln.fr Her research centers on perception for robot control, particularly in underwater robotics and computer vision. Key interests include visual servoing, SLAM, gesture recognition for diver-robot interaction, and autonomous capabilities in real-world marine environments. She applies deep learning and sensor fusion techniques to enhance underwater visual perception and navigation. The recent publications demonstrate a strong trend in underwater robotics, with focus areas including tether dynamics (catenary modeling), ROV localization using umbilicals and IMUs, long-term visual localization in deep-sea environments, and color restoration in underwater imagery. Her work bridges theory and real-world application, contributing datasets like 'Eiffel Tower' for benchmarking and advancing multi-agent SLAM systems. Claire Dune has contributed to leading journals such as IEEE Robotics and Automation Letters, Ocean Engineering, and The International Journal of Robotics Research. Her editorial and survey work highlights her leadership in the domain of deformable object manipulation. Retrieval of benthic habitat abundance and bathymetry from hyperspectral data (DESIS) in shallow waters ROV localization using ballasted umbilical equipped with IMUs MAM3SLAM: Towards underwater robust multi-agent visual SLAM Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization Challenges and Outlook in Robotic Manipulation of Deformable Objects Claire Dune actively collaborates with researchers such as Vincent Hugel, Juliette Drupt, and Andrew Comport. She has supervised or co-supervised numerous research projects and publications, particularly in underwater robotics and assistive technologies. Her work involves experimental robotics and system integration, often validated in real marine environments. She leads research in the COSMER laboratory focused on underwater robotics, including projects on tethered ROVs, diver-robot communication via gesture recognition, and environmental monitoring using visual and hyperspectral data. Her team develops practical solutions for marine science and offshore operations, emphasizing robustness and autonomy.

• Robot Vision
• Robot Navigation
• Computer Vision
• +7 more

Helder Araujo is a Professor at the Department of Electrical and Computer Engineering, University of Coimbra. His research focuses on Robot Vision , Computer Vision , and Medical Imaging , with applications in Capsule Endoscopy , Autonomous Driving , and Sensor Fusion . Recent publications highlight advancements in 3D Object Detection using transformer networks (e.g., RetSeg3D , DDet3D ), Medical Robotics (e.g., Self-supervised monocular pose estimation ), and Federated Learning (e.g., FAIR-FATE ). Key subfields include Transformer Models , Deep Learning , and Autonomous Navigation . His projects span Medical Imaging (e.g., Multi-Cam Capsule Endoscopy ), Autonomous Robotics (e.g., Learning to Navigate Endoscopic Capsule Robots ), and Edge Computing (e.g., Benchmarking CNN Inference on Low-Power Devices ). Grants include funding from Fundação para a Ciência e a Tecnologia (e.g., PTDC/EEI-ROB/1155/2020) and the European Commission (e.g., AdvanCed Hardware/Software Components for Embedded Vision). He has served as an invited researcher at Université Blaise Pascal (2012–2014). Peer review activities cover journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and Autonomous Robots . His work bridges Medical Imaging , Autonomous Systems , and AI Fairness .

Genda ChenView profile

Professor

• Structural Engineering
• Bridge Engineering
• Structural Health Monitoring
• +4 more

Genda Chen is a Professor and the Robert W. Abbett Distinguished Chair in Civil Engineering at Missouri University of Science and Technology. He serves as Director of the System and Process Assessment Research Laboratory, Director of the INSPIRE University Transportation Center, and Associate Director of the Mid-America Transportation Center. Dr. Chen's research spans multiple domains in civil and structural engineering with a strong emphasis on integrating advanced technologies for infrastructure assessment and resilience. His work prominently features structural health monitoring, smart infrastructure systems, bridge engineering, and the application of machine learning and computer vision techniques to civil infrastructure problems. Specific research interests include ultra-high performance concrete applications, structural connections, resilient infrastructure design for extreme events, unmanned vehicle systems for infrastructure inspection, and digital twinning of civil infrastructure. Analysis of Dr. Chen's recent publications reveals a strong trend toward integrating artificial intelligence and machine learning with traditional civil engineering practices. His work increasingly focuses on computer vision applications for infrastructure inspection, including crack detection, bridge element segmentation, and delamination detection using thermal imaging. There's also a significant emphasis on developing resilient infrastructure systems, particularly for bridges, with research on SMART shear keys for recovery after extreme events. His publications demonstrate a multidisciplinary approach that bridges civil engineering, materials science, robotics, and data science. Robert W. Abbett Distinguished Chair in Civil Engineering As Director of the System and Process Assessment Research Laboratory and the INSPIRE University Transportation Center, Dr. Chen leads research initiatives focused on transportation infrastructure assessment and innovation. His work with the Mid-America Transportation Center involves regional transportation research and technology transfer, with numerous projects addressing bridge inspection, structural monitoring, and resilient infrastructure design. Dr. Chen's laboratory work centers on structural assessment technologies, with particular emphasis on sensor development, unmanned systems for infrastructure inspection, and digital twin applications for civil infrastructure. His research team appears to be actively engaged in developing next-generation tools for bridge inspection, structural health monitoring, and resilient infrastructure design, with strong connections to transportation agencies and industry partners.

Chenchen KangView profile

Assistant Professor

• Precision Agriculture
• Autonomous Agricultural Machinery
• Computer Vision
• +4 more

Chenchen Kang serves as Assistant Professor of Precision Agriculture in Tennessee State University's College of Agriculture, Department of Agricultural Science and Engineering, based at the Otis L. Floyd Nursery Research Center in McMinnville, TN. His work integrates cutting-edge technology with agricultural engineering to solve field-level production challenges. Education: Postdoctoral Scholar, The Pennsylvania State University (2023-2025) Ph.D., Biological and Agricultural Engineering, Washington State University (2018-2023) M.S., Agricultural Mechanization Engineering, China Agricultural University (2016-2018) B.S., Agricultural Engineering (Outstanding Graduate), China Agricultural University (2012-2016) Dr. Kang's research pioneers autonomous agricultural machinery and AI-driven field solutions , with core expertise in computer vision for crop monitoring, precision irrigation systems, and robotic harvesting. His work bridges electrohydraulic control systems and data science to optimize resource use in specialty crops, particularly viticulture and vegetable production. Key innovations include hyperspectral imaging for water stress detection and low-cost sensor networks for orchard management. Analysis of his 9 publications (2022-2025) reveals dominant trends in sensor fusion (hyperspectral/thermal/3D imaging) for real-time vineyard monitoring, robotic thinning/spraying systems, and IoT-based decision support. The research consistently targets practical implementation in controlled environments and open-field agriculture, with strong emphasis on cost-effective solutions for small-scale growers. Dr. Kang actively secures external funding as Co-PI on State Horticultural Association of Pennsylvania projects ($20,964 total) for orchard sensor systems. He has contributed to USDA proposals including AgShred robotic harvesting ($601,250) and precision crop load management. His research infrastructure leverages the Otis L. Floyd Nursery Research Center for field validation of agricultural robotics and sensor technologies.

Sam AmiriView profile

Lecturer

• Embedded Systems
• Hardware Design
• Signal Processing
• +3 more

Sam Amiri is a Lecturer in Microelectronics / Embedded Electronics at Loughborough University. He holds a BSc in Software Engineering (2007, Iran), M.Sc. in Embedded Systems (2010, Masaryk University), and Ph.D. in Electrical/Electronic Engineering (2014, Masaryk University). Prior to his current role, he worked as a Researcher at Queen’s University Belfast (2015-2017) and the University of Bristol (2017-2018). His research focuses on hardware design, embedded systems, signal/image processing, and FPGA-based solutions for real-time systems. Key research areas include intrusion detection systems for vehicular networks using binarized neural networks, RISC-V processor optimization for neural networks, and FPGA-based acceleration of CNN models. His work also explores reliability engineering in wind turbine drivetrains and heterogeneous computing architectures combining FPGAs with CPUs. Publications span topics like embedded cybersecurity, low-power edge computing, and hardware-software co-design for real-time applications. His recent work emphasizes lightweight neural networks on constrained hardware and fault tolerance in aerospace systems. No specific awards or grants are listed in the provided information.

Paul CrossView profile

Senior Lecturer

• Insect Pollinators
• Bee Tracking Technology
• UAV-based Environmental Monitoring
• +12 more

Dr. Paul Cross is a Senior Lecturer in the Environment and Senior Tutor at the School of Environmental and Natural Sciences, Bangor University, UK. He is actively involved in research, teaching, and public engagement, with a strong focus on environmental science, pollinator conservation, and sustainable agriculture. His work bridges ecology, engineering, and socio-economic modeling, contributing significantly to understanding pollinator health and agricultural sustainability. His research interests include UAV-based tracking of insect pollinators, beekeeping as a tool for poverty alleviation, zoonotic diseases (particularly E. coli O157), bee morphometrics, and socio-economic methodologies for estimating disease and illegal behaviors. He employs advanced technologies such as radar, machine learning, and UAVs to study pollinator behavior and environmental impacts. His recent publications highlight a strong trend in integrating engineering and ecological sciences, particularly through the development of energy-harvesting bee tracking devices and radar-based monitoring systems. His work spans disciplines from entomology and soil science to public health and climate change, reflecting a highly interdisciplinary approach to environmental challenges. Senior Lecturer in the Environment, Bangor University Senior Tutor Course Director for MSc Conservation and Land Management Member of SENRGy Teaching and Ethics Committees Dr. Cross has supervised multiple PhD students on topics ranging from beekeeping in Tanzania to UAV-based bee tracking and neonicotinoid impacts. He has led or contributed to numerous research projects funded by the Welsh Government, RCUK, and the British Academy, focusing on pesticide hazards, greenhouse gas emissions, and food safety. He is actively involved in public outreach, with media appearances on BBC Countryfile and contributions to The Conversation. His work contributes to UN Sustainable Development Goals related to poverty reduction, climate action, and life on land.