Find Academic Supervisors

Ruwan TennakoonView profile

Senior Lecturer

• Artificial Intelligence
• Computer Vision
• Medical Imaging
• +3 more

Dr. Ruwan Tennakoon is a Senior Lecturer in Artificial Intelligence at RMIT University's School of Computing Technologies in Melbourne, Australia. He holds a BSc (Hons) in Electrical and Electronics Engineering from the University of Peradeniya and a PhD in Computer Vision from Swinburne University of Technology (2015). Prior to his current role, he held post-doctoral positions at RMIT School of Engineering and IBM Research. His research focuses on robust computer vision, particularly in medical imaging and industrial automation. Key areas include distribution shift challenges, large vision foundation models, and robust algorithms for low-level vision tasks. His work spans applications like medical image analysis, autonomous systems, and defect detection in manufacturing. Ruwan has supervised numerous research projects, including those on threat detection, medical imaging AI, and industrial automation. He teaches courses in computer vision and machine learning, emphasizing practical applications. His research has been published in top-tier venues such as ACM Transactions on Intelligent Systems and Technology and Medical Image Analysis .

• Turbulence
• Unmanned Systems
• Sensors
• +5 more

Dr. Abdulghani Mohamed is a Senior Lecturer in the School of Engineering at RMIT University, specializing in aerospace engineering with a focus on unmanned aerial systems, turbulence research, and biomimetic flight technologies. His research integrates principles from biology with engineering applications to advance drone technology and flight control systems. His research interests span Turbulence , Unmanned Systems , Sensors , Biomimetics , Aerodynamics , Experimental fluid dynamics , Computational fluid dynamics , and Energy harvesting systems . Dr. Mohamed's work demonstrates a strong connection between natural flight mechanisms observed in birds and engineered solutions for drone technology. His recent publications (2018-2024) reveal consistent research productivity with a clear trajectory toward increasingly sophisticated drone control systems, turbulence adaptation mechanisms, and biomimetic applications. The research shows a progression from fundamental aerodynamic studies toward practical implementations in urban air mobility and advanced drone systems. Open to Masters Research or PhD student supervision Active research projects in drone propeller optimization, turbulence mitigation, and biomimetic flight Dr. Mohamed maintains an active research program with numerous projects related to unmanned aerial systems and their applications in complex environments. His work bridges theoretical aerodynamics with practical engineering solutions for next-generation flight systems.

• Intelligent monitoring and maintenance of civil infrastructure
• Optimum asset maintenance and management
• Structural reliability analysis and safe life prediction of structures and infrastructure
• +4 more

Dr. Mojtaba Mahmoodian is a Senior Lecturer in Civil and Infrastructure Engineering at RMIT University's School of Engineering, City Campus, Australia. He is a Chartered Engineer in both Civil Engineering and Asset Management with Engineers Australia. His work spans diverse infrastructure types including bridges, pipelines, railways, buildings, ports, and offshore platforms. Dr. Mahmoodian's research focuses on intelligent monitoring and maintenance of civil infrastructure, optimum asset maintenance and management, structural reliability analysis and safe life prediction of structures, and deterioration modeling of structures and infrastructures. His work integrates advanced technologies including digital twins, artificial intelligence, and machine learning to enhance infrastructure monitoring and management systems. He has developed innovative approaches for corrosion modeling, structural health monitoring, and real-time structural integrity assessment. His recent publications demonstrate a strong trend toward the integration of digital twin technology with artificial intelligence for infrastructure monitoring. The research spans civil infrastructure domains including bridges, railways, ports, and pipelines, with emphasis on real-time monitoring, predictive maintenance, and optimal sensor placement. His work shows increasing sophistication in applying machine learning techniques to accelerate finite element analysis and improve structural assessment accuracy. RMIT Vice-Chancellor's Award for Outstanding Contributions in Learning and Teaching (2019) Chartered Engineer in Civil Engineering and Asset Management (2019) Chair of special sessions at major international bridge conferences (IABMAS 2018, 2020) Conference chair for ICWSS 2017: International Conference on Water Supply Systems Kharazmi Festival Award for innovation in concrete testing (2004) Dr. Mahmoodian actively supervises Masters and PhD research students, with current projects focusing on AI-enabled health monitoring of bridge structures through digital twin technology, computer vision for damage detection, automatic condition assessments of civil infrastructure, and intelligent monitoring and maintenance systems. His research has significant practical applications for infrastructure asset management and maintenance optimization. His work involves collaboration with various research teams focusing on structural health monitoring, digital twin development, and AI applications in civil engineering. These teams work on developing innovative monitoring systems that integrate sensor networks, data analytics, and visualization tools to provide comprehensive infrastructure assessment capabilities.

Maia FraserView profile

Associate Professor

• Machine Learning
• Computer Vision
• Neuroscience
• +4 more

Maia Fraser is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. She holds an MSc and PhD from the University of Chicago and a PhD from Stanford University. Her research spans interdisciplinary areas including machine learning, computational neuroscience, and applied mathematics. Notably, she explores the intersection of AI with structural engineering, focusing on automated damage assessment in concrete and neural mechanisms underlying continual learning. Her work also delves into theoretical aspects of contact geometry and reinforcement learning. Her academic contributions include pioneering methods for damage quantification using digital images, theoretical frameworks for temporal abstractions in neural systems, and algorithmic advancements in decision-making under uncertainty. Fraser’s research often bridges abstract mathematical theory with practical applications, such as optimizing nautical navigation and analyzing fish behavior through spatial learning models. Education: MSc in Mathematics, University of Chicago PhD in Mathematics, University of Chicago PhD in Computer Science, Stanford University Fraser’s recent publications highlight advancements in automated assessment techniques for civil infrastructure, the role of temporal hierarchies in neural organization, and foundational work in reinforcement learning theory. Her interdisciplinary approach reflects a commitment to solving complex problems through collaborative, mathematically rigorous methods.

Hua LuView profile

Professor

• Experimental Mechanics
• Reliability of Structures and Devices
• Solid Mechanics
• +5 more

Dr. Hua Lu is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. His research focuses on experimental mechanics and reliability analysis of microelectronic packaging and mechatronic systems. He is affiliated with the Laboratory for Experimental Mechanics Research and actively contributes to advancements in thermal-mechanical performance characterization. Education: BSc, Tianjin University, 1968 MSc, Tianjin University, 1982 PhD, Stony Brook University, 1989 Dr. Lu's research interests center on experimental solid mechanics, with emphasis on microelectronic packaging reliability, thermal-mechanical behavior of thin structures, and optical methods for deformation measurement. His work bridges mechanical engineering with electronics manufacturing, focusing on failure prediction and performance optimization of advanced packaging technologies. He applies inverse methods, computer vision, and fiber-optic sensing to real-world reliability challenges in microsystems. The recent publications highlight a strong trend in developing and applying advanced experimental techniques—particularly inverse modeling, optical diagnostics, and sensor integration—to assess reliability in microelectronics. These works span disciplines including mechanical behavior of materials, thermal analysis, and structural health monitoring, with consistent focus on flip-chip and PBGA technologies. Professional Affiliations: Surface Mounting Technology Association (SMTA) International Dr. Lu leads the Laboratory for Experimental Mechanics Research, where experimental techniques are developed and applied to structural and electronic system reliability. While specific grant details and student advising records are not listed, his publication record indicates active research supervision and project leadership in experimental mechanics and microelectronics packaging. His work supports innovation in durable, high-performance electronic systems.

Jaap van SchaikView profile

Researcher

• Bat Ecology
• Population Dynamics
• Host-Parasite Interactions
• +7 more

Dr. Jaap van Schaik is a researcher in the Department of Applied Zoology and Nature Conservation at the Zoological Institute and Museum, University of Greifswald, Germany. He conducts interdisciplinary research on European bat species, integrating ecology, behavior, and conservation science. His work is centered on understanding population dynamics, social systems, and host-parasite interactions in bats, with a strong emphasis on developing and applying innovative monitoring technologies. His research interests include bat ecology, movement ecology, population genetics, and host-parasite evolutionary dynamics. He employs cutting-edge methods such as infrared light barriers and camera traps to monitor bat populations non-invasively. Notably, he co-developed BatNet , a deep-learning tool for automated bat species identification from camera trap images, advancing the scalability and accuracy of long-term ecological monitoring. His work addresses critical conservation challenges posed by habitat fragmentation, climate change, and wind energy expansion. His recent publications highlight a strong trend in developing and validating technological solutions for wildlife monitoring, particularly focused on hibernacula. These studies emphasize non-invasive, high-resolution methods to estimate population trends, assess hibernation phenology, and identify species automatically, contributing significantly to conservation-oriented ecological research. Scientific Awards: Dr. van Schaik is actively involved in academic mentoring, supervising PhD students as a co-principal investigator, as well as Master’s and bachelor’s projects. He also contributes to teaching within his department. There is no mention of external grants in the text, but his collaborative publications suggest active research funding and project leadership. He works closely with colleagues such as Dr. Gabriella Krivek and Prof. Dr. Gerald Kerth. His research is conducted within the team structure of the Applied Zoology and Nature Conservation group at the University of Greifswald, which focuses on empirical and theoretical approaches to biodiversity conservation. The team utilizes field-based methodologies and technological innovations to study animal behavior and population dynamics, particularly in bats.

• Robotics
• Control Systems
• Automation
• +10 more

ABDERRAHIM FICHOUCHE, MOHAMED is an Associate Professor in the Department of Systems Engineering and Automation at the Carlos III University of Madrid (UC3M), where he also serves as Deputy Director of the Doctorate. He is affiliated with the Robotics Lab research group and the Pedro Juan de Lastanosa Institute of Technology Development and Innovation. His academic and research profile spans robotics, control systems, biomedical engineering, and power systems. His research interests include robotics , automation , control systems , biomedical signal processing , computer vision , and renewable energy integration . His work focuses on intelligent robotic systems, rehabilitation robotics, EEG-based brain-computer interfaces, fault detection in power systems, and advanced control strategies for industrial and autonomous systems. The recent publications reflect a strong trend in interdisciplinary research, combining machine learning with signal processing for applications in medical diagnostics , robotic control , and smart grid monitoring . His work increasingly integrates deep learning , wavelet analysis , and adaptive control across domains. He has led major research projects such as: HANDLE (EU and national funding): Focused on dexterous in-hand manipulation. SARAH: Enhancing robot autonomy with artificial hands. PAPREC: Automatic grasping of disordered parts. PROSAVE: Eco-efficient aircraft systems. GRAND-PA: Assistive technologies for the elderly. He currently participates in ongoing projects including: SEGVAUTO5G-CM (2025–2028): Future mobility innovation. RoboCity2030-DIH-CM (2019–2023): Madrid Robotics Digital Innovation Hub. HYPER: Neuroprosthetic and neurorobotic devices for rehabilitation. He has supervised several theses on topics such as transmission line fault detection , autonomous decision-making in robots , and 3D perception . His research is conducted primarily within the Robotics Lab at UC3M, a multidisciplinary team focusing on advanced robotic systems for industrial, medical, and service applications.

• Robotics
• Control Systems
• Simultaneous Localization and Mapping (SLAM)
• +7 more

Seyed Masoud Sotoodeh Bahraini is an Assistant Professor at Sirjan University of Technology and Senior Research Fellow at the University of Birmingham. His career spans multiple institutions including Loughborough University (Research Associate/Teaching Fellow) and City, University of London (Postdoctoral Research Fellow). Current Affiliations: Assistant Professor, Sirjan University of Technology Senior Research Fellow, University of Birmingham Past Positions: Research Associate & Teaching Fellow, Loughborough University (2022-2024) Postdoctoral Research Fellow, City, University of London (2019-2021) Dr. Bahraini's research bridges robotics, control theory, and fractional calculus applications. His work focuses on: SLAM and visual odometry in dynamic environments Human-robot collaboration gestural interfaces Fractional-order control for chaotic systems Viscoelastic material modeling in microbeams Multi-robot cooperation for planetary exploration Deep learning integration in robotic perception His publications since 2013 demonstrate expertise in applying fractional calculus to mechanical systems and advancing SLAM algorithms through machine learning. Key trends include: 2013-2014: Foundational work on fractional derivative models for viscoelastic beams 2018-2021: Development of ML-RANSAC SLAM techniques and planetary exploration robotics 2023-2025: Expansion into fuzzy fractional control and human-robot interaction gestures

Zhanping YouView profile

Professor

• Transportation Engineering
• Pavement Design and Materials
• Asphalt Materials
• +5 more

Zhanping You is a Distinguished Professor of Transportation Engineering and Materials in the Department of Civil & Environmental Engineering at Michigan Technological University (MTU). He holds the Donald and Rose Ann Tomasini Endowed Chair and has been actively involved in academic leadership roles, including directing the Michigan Department of Transportation (MDOT) partnered Transportation Materials Research Center (TMRC). His research focuses on pavement materials, micromechanics, and sustainable infrastructure, with over 300 peer-reviewed publications. Education: Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign (2003). Research Interests include design/construction of pavements, asphalt materials, recycled materials, and discrete element modeling. Notable projects involve rubberized asphalt, nanoclay-modified materials, and waste plastic utilization in pavements. He has led over 20 grants from NSF, FHWA, and others, totaling millions in funding. Awards include ASCE ExCEEd Fellowship, NSF grants, and the Michigan Tech Research Award. Editorial roles include Associate Editor for ASCE's Journal of Materials in Civil Engineering and multiple Special Issue Guest Editorships. Professional activities span TRB committees, ISAP, and international collaborations in China, Malaysia, and Europe. Teaching: Courses include CEE 3101 (Civil Engineering Materials), CEE 5101 (Advanced Bituminous Materials), and graduate seminars. Advises PhD students (e.g., Siyu Chen, Lingyun You) and mentors over 30 undergraduate researchers. Lab/Team: Active research groups focus on pavement materials innovation, with projects on cold-climate asphalt performance and 3D-printed repair solutions. Partnerships include industry and global institutions like Universiti Sains Malaysia.

• Control Systems
• Mechanical Engineering
• Offshore Engineering
• +5 more

Christian Holden is an Associate Professor at the Department of Mechanical Engineering and Manufacturing, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on control systems, mechanical engineering, and offshore engineering with applications in robotics, fluid dynamics, and marine systems. He holds a PhD in Modeling and Control of Parametric Roll Resonance (2011). Key research areas include adaptive control systems, drill-string dynamics, riser-tensioner systems, and hydrocyclone optimization. His work combines theoretical modeling with practical applications in offshore operations, robotics, and energy systems. Collaborations with industry and academia have yielded solutions for challenges in subsea engineering, drilling dynamics, and automation. Publications emphasize advanced control methodologies, simulation frameworks, and experimental validation. His recent work explores AI-driven trajectory planning for robotics and control-oriented models for industrial separation processes. Holden's contributions bridge mechanical engineering principles with cutting-edge control theory for real-world engineering systems.

• Signal Processing
• Acoustic Sensor Networks
• Machine Learning
• +3 more

Roberto Gil Pita is a Professor at the University of Alcalá (Spain), affiliated with the Department of Signal Theory and Communications. He leads the AES3 research group focusing on acoustic and electromagnetic smart sensor networks and signal processing applications. His doctoral work (2006) centered on radar target classification using statistical and AI methods under the supervision of Dr. Manuel Rosa Zurera. His research spans signal processing, machine learning, and their applications in aerospace, biomedical systems, and smart cities. Key areas include UAV detection, emotion recognition from speech, and acoustic localization using microphone arrays. His academic background includes a doctorate from the University of Alcalá and extensive contributions to wireless acoustic sensor networks, hearing aid signal processing, and bioimpedance spectroscopy. He has developed energy-efficient algorithms for real-time audio analysis, acoustic violence detection systems, and robust methods for speech enhancement in noisy environments. His work bridges theoretical signal processing with practical engineering solutions for defense, healthcare, and urban monitoring. Research interests extend to aeroelastic flutter analysis in aviation, wearable biomedical sensors for stress assessment, and data-driven approaches for sound environment classification. He has pioneered the use of deep learning in flutter testing and acoustic event classification, contributing to datasets like REALISED for benchmarking machine learning models. Notable projects include acoustic localization of drones using microphone arrays, real-time emotion detection systems, and collaborative research in smart healthcare technologies. His work emphasizes computational efficiency and energy conservation, particularly for embedded systems and battery-operated devices.

Thomas MaillartView profile

Senior Lecturer

• Collective Intelligence
• Complex Socio-Economic Systems
• Human Dynamics
• +12 more

Thomas Maillart is a Senior Lecturer and researcher at the Research Institute for Statistics and Information Science at the University of Geneva. His work bridges complex systems, collective intelligence, and cybersecurity, with a focus on modeling human dynamics and digital risks. Education: PhD in Science, ETH Zurich (2011) Master’s degree, EPFL (2005) His research centers on understanding how incentives, structures, and social interactions shape collective behavior in online and physical environments. He investigates topics such as cyber risks, privacy, resilience, and technological innovation. His work often applies statistical physics and data science to socio-technical systems, including open-source software development, cybersecurity policy, and human behavior modeling. His recent publications reflect a strong trend toward interdisciplinary research, combining insights from computer science, economics, psychology, and public policy. He explores how machine learning can forecast digitization labor needs, how collective action enhances cybersecurity, and how bio-sensors can aid in medical diagnosis. His work frequently appears in high-impact journals such as Science , PLOS ONE , and Physical Review . Scientific Awards: Zurich Dissertation Prize (2012) for pioneering work on cyber risks Thomas Maillart has advised and collaborated with numerous researchers and students, though specific names are not listed in the provided text. He has been involved in significant research grants and initiatives, including cybersecurity consulting for governmental and private organizations and co-founding a cybersecurity startup in 2005. His academic service includes organizing and presenting at international conferences such as WEIS and ACM conferences. He is associated with several research platforms, including ResearchGate, Google Scholar, LinkedIn, and Twitter, and has contributed to both peer-reviewed and practitioner-oriented publications. His recent editorial work includes co-editing a book on critical information infrastructure security.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +3 more

Tommaso Ruga serves as a Lecturer in the Department of Computer Engineering, Modellistica, Elettronica e Sistemistica (DIMES) at the University of Calabria, Italy. He teaches core courses including Data Base Systems for Management Engineering, Fundamentals of Computer Science for Mechanical Engineering, and Data Analytics for Business for Management Engineering programs in the 2025 academic year. His research centers on Artificial Intelligence applications across critical domains, with primary focus on healthcare innovation (cancer detection, pandemic forecasting, Alzheimer's diagnosis), sustainable agriculture (cassava disease detection, smart farming), and cultural heritage preservation . Key methodologies include federated learning, explainable AI frameworks, and deep learning architectures designed to address real-world challenges like dataset bias in dermatology and energy forecasting for sustainable communities. Analysis of his 15 most recent publications (2023-2025) reveals a cohesive trajectory: 60% target healthcare applications (particularly oncology and neurology), 25% focus on agricultural and environmental systems, and 15% address cultural heritage and tourism. His work consistently emphasizes technical explainability and ethical considerations , notably in mitigating racial bias in medical imaging datasets and developing transparent AI models for clinical decision support. Dr. Ruga operates within the University of Calabria's Department of Computer Engineering, Modellistica, Elettronica e Sistemistica, which provides the computational infrastructure for his AI research. His collaborations frequently involve co-authors Caroprese, Vocaturo, and Zumpano across multiple publications, indicating established research partnerships within the institution.

Jaime CamelioView profile

Professor

• Cyber-Physical Systems Security
• Smart Manufacturing
• Statistical Process Control
• +7 more

Jaime Camelio is a Professor at the University of Georgia's School of Electrical & Computer Engineering, specializing in cyber-physical systems security, smart manufacturing, and statistical process control. His work integrates advanced technologies like machine learning, reinforcement learning, and Bayesian inference to enhance manufacturing resilience and safety. He focuses on vulnerabilities in production systems, digital twin applications, and data-driven quality control. His research spans aerospace composites, IIoT-enabled worker well-being monitoring, and synthetic data frameworks for manufacturing simulations. Research interests include cybersecurity in additive manufacturing, occupational safety monitoring via statistical control charts, and innovative approaches to fault detection in assembly systems. He leads projects funded by NSF CPS initiatives, such as collaborative research on manufacturing security and cyber-physical vulnerability assessments. His work bridges theoretical advancements with real-world applications in aerospace, automotive, and industrial IoT sectors. Scientific contributions include frameworks for digital thread integration in product lifecycle management and NURBS-based statistical monitoring of manufacturing surfaces. Publications emphasize both technical innovations and systemic risk mitigation strategies for modern manufacturing ecosystems. His lab explores cutting-edge topics like LLM applications in manufacturing workflows and random sampling strategies to counteract cyber-physical attacks.

Marcin GrzegorzekView profile

Professor

• Medical Informatics
• Artificial Intelligence in Medicine
• Deep Learning
• +3 more

Prof. Marcin Grzegorzek is a Professor at the Institute of Medical Informatics , University of Lübeck, Germany. His research focuses on AI-driven medical imaging, deep learning applications in healthcare, and sensor data analysis for assistive technologies. Key areas include medical image processing, wearable sensor systems, and clinical decision support systems. Research Groups: Medical Data Science Lab, Medical Deep Learning Lab, and Junior Research Group on Movement Disorders. Key Projects: Development of AI algorithms for OCT-based retinal analysis, lymph node segmentation, and real-time health monitoring using wearables. His work bridges clinical needs with advanced computational methods, contributing to improved diagnostics and personalized medicine. Over 100+ peer-reviewed publications reflect his expertise in medical informatics and interdisciplinary collaborations.