Find Academic Supervisors

Laszlo BöszörmenyiView profile

Professor

• Distributed Multimedia Systems
• Self-Organizing Content Delivery
• Video Content Analysis
• +2 more

Laszlo Böszörmenyi is Full Professor of Computer Science and Head of the Institute of Information Technology at Alpen-Adria University Klagenfurt, Austria. His research specializes in distributed multimedia systems with emphasis on self-organizing architectures and intelligent video content analysis. Research focuses on: Self-organizing content delivery networks Interactive video browsing and exploration Medical video analysis (endoscopic and surgical) Video content summarization and retrieval Distributed systems for multimedia applications He leads several research projects in medical multimedia and traffic surveillance systems. Dr. Böszörmenyi has published extensively on video analysis techniques, particularly for medical applications including: Surgical phase recognition in laparoscopic videos Endoscopic video compression and archiving Instrument detection in surgical workflows Specialized browsers for medical video exploration He serves as Associate Editor for Multimedia Systems Journal and participates in numerous program committees for multimedia conferences.

Dena BazazianView profile

Lecturer

• Robotics
• Machine Vision
• Artificial Intelligence
• +3 more

Dr. Dena Bazazian is a Lecturer in Robotics and Machine Vision at the University of Plymouth's School of Engineering, Computing and Mathematics. Previously, she held positions as a Senior Research Associate at the University of Bristol's Visual Information Laboratory, Research Scientist at CTTC (Barcelona), and postdoctoral researcher at Universitat Autònoma de Barcelona, where she earned her PhD in 2018. Her international research includes visiting positions at Barcelona Supercomputing Center (2024), NAVER LABS Europe (2019), and University of Florence (2017). Her research focuses on robotic vision, underwater technologies, and AI-driven applications. Key interests include: 3D reconstruction and neural rendering techniques Anomaly detection using graph neural networks Underwater perception systems for marine environments Intergenerational co-design of immersive technologies Point cloud processing and geometric deep learning Publication analysis reveals strong emphasis on computer vision applications across robotics, industrial inspection, heritage preservation, and marine technology. Recent works increasingly integrate human-centered approaches with XR technologies and underwater systems. She currently supervises 8 PhD students across robotics, healthcare, and environmental monitoring domains: Offshore wind turbine maintenance drones (EPSRC-UDLA funded) Autonomous sea vessel swarms for climate forecasting (ARIA funded) AI for marine biodiversity monitoring (Cefas funded) Elderly fall detection systems (URS General funded) Healthcare anxiety detection through visual analysis (EPSRC DTP HMT funded) Dr. Bazazian leads modules including Artificial Vision and Deep Learning and Computer Vision . She co-organizes workshops including the Deep Learning for Geometric Computing series (CVPR/ICCV) and Women in Computer Vision workshops (CVPR/ECCV).

• Machine Learning
• Artificial Intelligence
• Stochastic Games
• +5 more

Xian Chen is an active academic researcher with an extensive publication record spanning over 30 years from 1994 to 2025. Their research demonstrates significant contributions across multiple disciplines within computer science, engineering, and applied mathematics. The publication pattern indicates sustained scholarly activity at a high level, with numerous papers in top-tier journals and conferences. Chen's research interests span a remarkably diverse range of fields, with particular emphasis on machine learning, artificial intelligence, stochastic processes, and their applications across various domains. Their work bridges theoretical foundations with practical applications, as evidenced by publications in both theoretical journals like SIAM Journal on Control and Optimization and applied venues like IEEE Access . The research portfolio shows evolution from early work in parallel computing and wireless networks toward contemporary AI and machine learning applications. The publication record reveals significant trends in Chen's research trajectory. Early work focused on parallel computing, wireless networks, and database systems. Over time, there's a clear shift toward machine learning applications, optimization techniques, and interdisciplinary work connecting computer science with fields like biomedical engineering, renewable energy, and education technology. Recent publications (2024-2025) show strong focus on large language models, federated learning, hate speech detection, and educational AI applications, reflecting current trends in artificial intelligence research. The consistent output across multiple domains suggests a highly collaborative research approach with numerous co-authors across different institutions. Chen has established productive collaborations with researchers worldwide, as evidenced by the diverse author lists across publications. The research demonstrates both theoretical depth in areas like stochastic games and Markov decision processes, as well as practical applications in fields ranging from medical diagnostics to renewable energy systems. The interdisciplinary nature of the work indicates versatility in applying computational methods to solve domain-specific problems.

Abdolreza AbhariView profile

Professor

• Artificial Intelligence
• Agent Systems
• Data Science
• +5 more

Dr. Abdolreza Abhari is a Professor and Undergraduate Program Director in the Department of Computer Science at Toronto Metropolitan University. His research focuses on Artificial Intelligence, Agent Systems, Data Science, Distributed Systems, and Network Simulation. He holds a PhD from Carleton University (2003) and has received notable grants including CFI, NSERC, and OGS funding. His professional roles include serving as Conference/Track Chair for Spring Simulation Conference and as an IEEE Senior Member. He leads the Data Science research group and the Distributed and Multimedia Processing (DSMP) lab. Awards include the Nortel Network Scholarship for Ph.D. studies and industry grants from AutoDesk Inc. His work spans simulation modeling, edge computing, and social network analysis. Education: PhD in Computer Science, Carleton University (2003). Research Interests: Dr. Abhari’s expertise includes developing simulation frameworks for distributed systems, optimizing network architectures, and applying machine learning to social networks. His work integrates theoretical models with practical applications in smart cities, video streaming, and fault-tolerant systems. Awards & Grants: Recipient of major research grants (CFI, NSERC) and industry partnerships. His contributions to simulation methodologies have been recognized through scholarships and collaborative projects. Advising & Labs: Leads the Data Science group and DSMP lab, focusing on edge computing, video analytics, and scalable data management. His research addresses real-world challenges in distributed systems and AI-driven solutions.

Gordon G. ParkerView profile

Professor

• Dynamic Systems Modeling
• Control Systems
• Robotics
• +7 more

Dr. Gordon G. Parker is the John and Cathi Drake Endowed Chair in Mechanical Engineering at Michigan Technological University (Michigan Tech). He holds a PhD from the State University of New York at Buffalo and has 4 years of experience at Sandia National Laboratories and 3 years at General Dynamics Space Systems. His research focuses on dynamic system modeling and control, with applications in land, air, space, and sea systems. Key areas include model predictive control for energy management, machine learning for wave energy converters, and survivability-optimal control of vessels. His expertise spans nonlinear and optimal control, modeling and simulation, and real-time embedded systems. Recent projects involve wave energy converter (WEC) modeling, vibration control, and military microgrid optimization. He has collaborated with organizations like the US Army Research Laboratory, Department of Energy, and industry partners such as John Deere and Cummins. Dr. Parker has authored numerous publications on microgrid control, energy storage requirements, SCR catalyst modeling, and crane control systems. His work emphasizes practical applications, including hardware-in-the-loop simulations and experimental validation. He teaches courses in dynamic systems, robotics, mechatronics, and nonlinear control theory at Michigan Tech's College of Engineering.

Christian Malaga ChuquitaypeView profile

Senior Lecturer

• Earthquake Engineering
• Structural Dynamics
• Applied Dynamics
• +6 more

Dr. Christian Malaga Chuquitaype is a Senior Lecturer in the Department of Civil and Environmental Engineering at Imperial College London, leading research in Structural Earthquake Engineering and Applied Dynamics. He directs the Emerging Structural Technologies Lab and serves as Director of Undergraduate Admissions for the Civil Engineering Department and Director of the MSc in Earthquake Engineering program. His academic roles include Departmental Postdoc Champion and editorial positions at journals like Structures and Engineering Structures . He holds a PhD from Imperial College London (2012), MSc from the ROSE School in Italy (2007), and a five-year engineering degree from the National University of San Agustin (2000). His research integrates advanced numerical methods with experimental techniques to address extreme structural challenges, including earthquake-resistant design and extraterrestrial architecture. Notable awards include the Best Research Paper Prize from the Institution of Structural Engineers (2019) and the Tso Kung Hsieh Award (2015). His work emphasizes innovative technologies like AI-driven structural modeling and digitally manufactured materials. He actively contributes to international standards committees (e.g., ISO/TC 165) and advises on engineering education through roles at the Engineering and Science Admissions Test (ESAT). The Emerging Structural Technologies Lab focuses on resilient structures, hybrid timber-GFRC systems, and lunar arch forms.

Quek Ser TongView profile

Professor

• Structural Engineering
• Structural Reliability
• Structural Health Monitoring
• +7 more

Quek Ser Tong is a Professor of Civil and Environmental Engineering at the National University of Singapore (NUS), holding the Keppel Chair Professorship from 2018–2020. He has served as Head of the Department of Civil and Environmental Engineering (2016–2020), Vice Dean of the Faculty of Engineering (2010–2015), and Inaugural Director of the Global Engineering Programme (2009–2012). His academic career began at NUS in 1982, following early roles as a systems engineer and civil engineer in Singapore’s Ministry of Defence. Quek holds a BEng (1st Class) from Monash University (1979), MEng from NUS (1984), and MSc/PhD from the University of Illinois at Urbana-Champaign (1986). He is a registered Professional Engineer (PEng, 1994) and has extensive consultancy experience in structural engineering, including in-situ concrete analysis, finite element simulations, and risk assessment. His research focuses on structural reliability, stochastic simulation, smart materials, structural health monitoring, and digital twin technology. Notable contributions include work on fatigue crack growth, seismic-tsunami risk modeling, and viscoelastic material behavior under aging. He has published over 250 papers and holds patents for optical fiber sensors and smart material systems. Quek has received awards such as the Public Administration Medal (Bronze, 2018), IES Prestigious Engineering Achievement Award (2024), and the IASSAR Distinguished Research Award (2025). He serves on editorial boards of journals like Structural Safety and International Journal of Structural Stability and Dynamics .

Law Wing Keung, AdrianView profile

Professor

• Coastal Engineering
• Hydraulic Engineering
• Environmental Hydraulics
• +5 more

Adrian Law Wing Keung is a Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS), serving since 2024. He holds a PhD and MSc from the University of California, Berkeley, specializing in coastal and hydraulic engineering, and a BEng (Civil) from the University of Hong Kong. His academic roles include Editor-in-Chief of the Journal of Hydro-environment Research and leadership in international committees like the IAHR/IWA Joint Committee on Marine Outfall Systems and ASEAN Hydroinformatics Data Centre. He has extensive consulting experience for water infrastructure projects and focuses on applying research to real-world solutions through his RDTA (Research-Development-Translation-Application) philosophy. Education: PhD in Civil and Environmental Engineering, University of California, Berkeley, USA (1991) MSc in Civil and Environmental Engineering, University of California, Berkeley, USA (1985) BEng (Civil Engineering), University of Hong Kong (1984) Research Interests: Coastal and hydraulic engineering, environmental hydraulics, remote sensing, data-driven process control, machine learning optimization in water systems, and coastal renewable energy. His work emphasizes translating fundamental research into practical applications, such as monitoring coastal environments under climate change and optimizing solar/wind energy systems. Awards: Top-2% Global Scientist by Stanford University (2023) Karl Emil Hilgard Hydraulic Prize (ASCE, 2014) UPS Foundation Visiting Professorship at Stanford (2001-2002) Wesley Horner Award (ASCE, 2000) Professional Activities: Council member of the International Association of Hydro-environment Engineering and Research, editorial board roles in technical journals, and advisory roles for major water infrastructure projects globally. His research outputs span 100+ peer-reviewed articles, with a focus on numerical modeling, remote sensing, and sustainable engineering solutions.

Charles CampView profile

Professor

• Structural Optimization
• Computational Mechanics
• Seismic Design
• +3 more

Dr. Charles Camp is a Professor of Civil Engineering at the University of Memphis, specializing in Structural Optimization and Computational Mechanics. He holds a Ph.D. in Civil Engineering from Oklahoma State University (1987), an M.S. from Auburn University (1986), and a B.S.C.E. from Auburn University (1981). His research focuses on advanced optimization techniques, machine learning applications in structural analysis, and seismic design methodologies. Teaching responsibilities include courses such as CIVL 1101 (Civil Engineering Measurements), CIVL 1112 (Civil Engineering Analysis), and graduate-level courses like CIVL 7111 (Computational Mechanics) and CIVL 7117 (Finite Elements in Structural Mechanics). His research spans structural health monitoring using CNNs, optimization of retaining walls, and probabilistic seismic loss analysis. Recent publications emphasize machine learning for defect detection in wooden structures and the application of swarm intelligence algorithms. His work bridges computational methods with practical civil engineering challenges, addressing sustainability and resilience in infrastructure systems. Dr. Camp has contributed to over 100 publications, including book chapters and edited volumes, and has collaborated on projects such as seismic instrumentation of bridges and GIS integration in engineering education. His research often explores innovative computational frameworks to enhance structural design efficiency and safety.

• Quantitative Spatial Modeling
• Population Abundance and Stock Assessment
• Disease Ecology
• +4 more

Brad Harris, Ph.D., is an Associate Professor of Marine and Environmental Sciences and Director of the Fisheries, Aquatic Science, & Technology (FAST) Laboratory at Alaska Pacific University. He holds adjunct appointments at the University of Massachusetts School of Marine Sciences and an honorary fellowship at Ulster University. Ph.D. in Fisheries Oceanography, University of Massachusetts School of Marine Science M.Sc. in Fisheries Oceanography, University of Massachusetts School of Marine Science B.Sc. in Fisheries and Wildlife Science, Texas A&M University Brad Harris's research spans multidisciplinary studies in marine ecosystems, focusing on spatial dynamics of aquatic species, population ecology, and the impacts of anthropogenic and natural disturbances on biodiversity. His work integrates physical oceanography, habitat mapping, and conservation engineering to address fisheries sustainability and ecosystem management. He also contributes to natural resource law and policy through empirical research and stakeholder engagement. His 15 most recent publications address topics such as spatial closures on Georges Bank, shell growth dynamics of Pacific razor clams, stable isotope trophic analysis, and innovative approaches to fisheries data integration. These works reflect his expertise in quantitative modeling, habitat conservation, and fisheries technology. Honorary Fellow, Ulster University Brad Harris collaborates extensively with commercial fisheries, government agencies, and international councils like ICES and FAO. He leads the FAST Lab, mentoring graduate students and conducting fieldwork across marine systems from the Gulf of Alaska to the Bering Sea. His teaching portfolio includes marine biology, fisheries ecology, and natural resource policy courses.

Darren DanceyView profile

Professor

• Artificial Intelligence
• Deep Learning
• Neural Networks
• +3 more

Professor Darren Dancey is the Head of Department for Computing and Mathematics at Manchester Metropolitan University (MMU). His academic background includes a PhD in Artificial Neural Networks and expertise in Artificial Intelligence, Deep Learning, and Software Development. He has led major research projects funded by Innovate UK, the European Research Council, and the Digital R&D Fund for the Arts, focusing on collaborations between academia and industry SMEs. Education and Research Interests: Professor Dancey's research spans neural networks, medical imaging applications, cybersecurity, and robotics. He has pioneered interdisciplinary projects such as a biologically inspired deep learning model for vegetation recognition and a spike transformer network for event-based vision systems. His work emphasizes explainable AI and practical industry applications. Grants and Collaborations: He has secured significant funding for initiatives like malware detection frameworks, hyperspectral image processing, and IoT security solutions. His projects often bridge academic innovation with real-world SME needs. Professional Engagement: He actively contributes to academic communities through roles such as organizing the Manchester Raspberry Pi Jam and serving on the BCS Manchester branch committee. His teaching focuses on software development, algorithms, and applied programming courses.

• Resource-efficient machine learning
• Data-efficient machine learning
• On-device learning
• +5 more

Olga Saukh is an Associate Professor at TU Graz's Institute of Computer Engineering, leading the Embedded Learning and Sensing Systems (ELSS) group. Her research focuses on resource-efficient AI, on-device learning, and robust sensing systems for IoT and environmental monitoring applications. She holds a Dr.rer.nat. and MSc in Computer Science, with expertise in embedded systems and wireless sensor networks. Her work integrates machine learning with hardware constraints, addressing challenges in energy efficiency, real-time adaptation, and adversarial robustness. Notable projects include PCDCNet for air quality forecasting and SensorFormer for sensor calibration. She has contributed to OpenSense Zurich's air pollution monitoring and automated pollen sensing systems. Her research spans over 50 publications since 2006, emphasizing practical deployments in structural health monitoring, smart agriculture, and urban environmental sensing. She leads interdisciplinary projects combining AI, embedded hardware, and data-driven decision-making.

• Glaucoma
• Age-related Macular Degeneration
• Ophthalmology
• +4 more

Dr. Jonathan Denniss is an Associate Professor at the School of Optometry & Vision Science, part of the Faculty of Life Sciences at the University of Bradford. He earned his MSc in Optometry from the University of Manchester (2007) and a PhD in Glaucoma Structure-Function Relationships (2010). His career includes postdoctoral roles at the University of Melbourne (Australia) and the University of Nottingham’s Visual Neuroscience Group. His research focuses on age-related eye diseases like glaucoma and macular degeneration, employing imaging, computer simulations, and psychophysical techniques. Key projects include improving glaucoma detection via en face OCT analysis and customizing perimetric tests for individual patients. He leads the College of Optometrists-accredited Professional Higher Certificate in Glaucoma and oversees postgraduate research in his department. Research interests span clinical diagnostics, ocular imaging innovations, and translating findings into automotive industry applications. He actively mentors prospective PhD students exploring age-related vision disorders. Publications emphasize structure-function correlations in glaucoma, OCT advancements, and patient-centric visual testing improvements. His work bridges clinical practice and technological innovation in ophthalmology.

Gianluca CiattagliaView profile

Assistant Professor

• Radar Signal Processing
• Automotive Engineering
• Aerospace Engineering
• +3 more

Gianluca Ciattaglia is an Assistant Professor (RTDa) in Electrical and Electronics Measurements at the Department of Electrical and Electronic Engineering, Università Politecnica delle Marche, Italy. Previously, he served as a Research Fellow at the same institution from 2022 to 2024 and worked at Ferrari S.p.A. as an Electronic Support Engineer for Formula 1 test teams (2018–2022). He holds a B.Sc. (2014), M.Sc. (2017), and Ph.D. (2021) in Electronic Engineering and Information Engineering from Università Politecnica delle Marche. His research focuses on radar-based measurement techniques for industrial, automotive, and aerospace applications, including radar signal processing, vibration analysis, and sensor fusion. He has contributed to projects involving drone detection, elderly telemonitoring, and automotive radar optimization. His work bridges theoretical advancements in signal processing with practical implementations in safety, health, and environmental monitoring. Publications span radar-based physiological sensing, autonomous drone identification, and mmWave radar applications in structural health monitoring. His recent work emphasizes machine learning integration with radar data for improved accuracy in activity recognition and environmental control systems. No scientific awards are explicitly listed, but his contributions to automotive and aerospace radar technologies reflect significant industry-academia collaboration. Professional activities include advising on radar sensor deployments and collaborating with automotive firms like Ferrari. His lab focuses on developing edge-node systems for real-time monitoring and smart environmental solutions. Current projects include optimizing radar configurations for energy efficiency and enhancing indoor air quality through smart ventilation systems.

Leon JololianView profile

Professor

• Component-based software architecture
• Mobile and cloud computing
• Internet of things
• +3 more

Leon Jololian is a Professor and Chair in the Department of Computer Science at the University of Alabama at Birmingham (UAB). He also serves as Undergraduate & Graduate Program Director. His research focuses on component-based software architecture, mobile and cloud computing, internet of things (IoT), and machine learning applications in healthcare and biomedical engineering. He holds a B.S. in Electrical Engineering from Manhattan College, M.S. degrees in Electrical Engineering (Georgia Tech) and Computer Science (Polytechnic University), and a Ph.D. in Computer Science from New Jersey Institute of Technology. His recent work includes developing frameworks for automated machine learning environments, predicting protein-ligand interactions using deep learning, and investigating convergence engineering across disciplines. He has contributed to healthcare system modeling, crowd trajectory prediction, and educational frameworks for machine learning courses. His publications span both foundational computer science topics and interdisciplinary applications in medicine and engineering. Dr. Jololian teaches advanced undergraduate and graduate courses in software engineering and machine learning. His research integrates computational methods with real-world challenges in healthcare, cybersecurity, and biomedical systems. Office hours are by appointment in Gorrie Hall 5251.