Find Academic Supervisors

Benedict MobleView profile

Associate Professor

• Aerospace Engineering
• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• +7 more

Dr. Moble Benedict is an Associate Professor of Aerospace Engineering at Texas A&M University and a Chancellor EDGES Fellow. He leads the Advanced Vertical Flight Laboratory (AVFL), focusing on vertical takeoff and landing (VTOL) systems, autonomous UAVs, and energy-efficient aviation. His research spans bio-inspired flight, cycloidal rotor design, and electric propulsion. He advises numerous students who have won prestigious awards, including Vertical Flight Foundation scholarships and U.S. Army fellowships. Education: PhD (Aerospace Engineering, University of Maryland, 2010); M.Tech and B.Tech (Aerospace Engineering, Indian Institute of Technology Bombay, 2004/2003). Research highlights include development of the 'Aria' personal flying device for Boeing's GoFly Prize, quiet rotor technology commercialized via Harmony Aeronautics, and innovations in amphibious UAVs. Awards include the 2022 Texas A&M Research Impact Award and Dean’s Excellence Award. Labs/Teams: AVFL specializes in novel aircraft concepts, including planetary exploration vehicles and space debris removal systems. Collaborations include the Army Research Lab and Air Force Agility Prime Program.

Haytham FayekView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Dr. Haytham Fayek is a Senior Lecturer in the School of Computing Technologies at RMIT University, leading the Machine Learning and Intelligence Group. He holds roles in the Evolutionary Computing and Machine Learning (ECML) Group, AI Innovation Lab, and the Centre for Industrial AI Research & Innovation (CIARI). His academic journey includes a PhD from RMIT (2019), postdoctoral work at Meta (2018-2020), and industry experience as an electrical engineer. He teaches courses like Foundations of Artificial Intelligence and Programming Fundamentals for Scientists. Research focuses on AI systems that generalize from limited data, emphasizing continual learning, multimodal learning, and applications in satellite systems, structural engineering, and gaming. Key projects include SAR interference mitigation, graph neural networks for automotive design, and cognitive load measurement in video games. He has supervised over 15 PhD/MS students and authored 60+ peer-reviewed publications. Notable achievements include a 2025 Spotlight presentation at ICLR and 2023 Best Paper nominations. Awards include EACL 2024 publication and IEEE Transactions contributions. His work bridges theory (e.g., SWAP-NAS NAS framework) with applications like autonomous satellite systems and aircraft defect tracking through NLP.

• Chemometrics
• Information Theory
• Machine Learning
• +9 more

Jordi Fonollosa Magrinya is an Associate Professor at the Barcelona School of Nautical Studies (FNB), Universitat Politècnica de Catalunya - BarcelonaTech (UPC), affiliated with the Department of Automatic Control and the Institut de Recerca i Innovació en Salut. He is a key member of the B2SLab - Bioinformatics and Biomedical Signals Laboratory and TecSalut - Grup de Recerca en Tecnologies de la Salut. Expertise: Chemometrics, Information Theory, Machine Learning, Sensors, Signal Processing Research Groups: B2SLab, TecSalut Teaching Center: Barcelona School of Nautical Studies (FNB) Email: jordi.fonollosa.m@upc.edu ORCID: 0000-0001-8854-8588 His research spans biomedical and environmental applications of sensor systems, with a focus on electronic noses, machine olfaction, and signal processing. He applies machine learning and chemometrics to develop intelligent sensing systems for health monitoring, environmental safety, and quality control. His work integrates interdisciplinary approaches from engineering, data science, and biomedicine to solve real-world problems in public health and sustainability. The recent articles highlight a strong trend in sensor technology, particularly gas sensor arrays for environmental and health monitoring. His publications cover applications in indoor air quality (CO2, ventilation), medical diagnostics (hypertension, Parkinsonism), food quality (olive oil), and global health (improved cookstoves). The keywords reflect a consistent focus on machine learning, signal processing, and sensor systems applied to biomedical and environmental challenges. 2024 Jordi Fonollosa Magrinya has supervised students, including J. Gelabert, and leads multiple competitive research projects funded by national and European programs. His grants focus on health technologies, environmental monitoring, and data-driven solutions for societal challenges. He actively collaborates with multidisciplinary teams across UPC and external institutions. He is a core member of the B2SLab - Bioinformatics and Biomedical Signals Laboratory and the TecSalut research group, both under the Institut de Recerca i Innovació en Salut. These teams focus on developing advanced sensor systems and data analysis methods for healthcare and environmental applications.

• Dependable, reliable, and secure computing
• Stochastic behavioural modelling and verification
• Concurrency theory
• +12 more

Holger Hermanns is a Full Professor of Computer Science at Saarland University, Germany, where he leads the Dependable Systems and Software group. He is co-spokesperson of the Center for Perspicuous Computing (SFB/TRR 248) and has been a scientific director at Schloss Dagstuhl – Leibniz Center for Informatics since 2025. He is a member of Academia Europaea and holds an ERC Advanced Grant (POWVER) and a Proof-of-Concept Grant (LEOpowver) . His leadership roles include former Dean of the Faculty of Mathematics and Computer Science and active service in major academic organizations such as the ETAPS Association (Vice President) and Friends of Dagstuhl (President, 2013–2023). Education: Researcher and academic positions at Universität Erlangen-Nürnberg (1993–1998), Universiteit Twente (1998–2006), and INRIA Grenoble Rhône-Alpes (2007–2010). His research focuses on modeling and verification of concurrent systems , stochastic behavioral modeling , energy informatics , and proactive algorithmic accountability . He is a leading figure in probabilistic model checking, formal methods for cyber-physical systems, and software dependability. His work bridges theory and practical applications in satellite systems, AI safety, and environmental monitoring. The recent publications reflect a strong trend toward AI accountability , software doping detection , formal verification of machine learning systems , and dependable satellite network design . His team develops tools like RTLola and Momba, integrating formal methods into real-world systems. There is a growing emphasis on interdisciplinary work, particularly in human oversight of AI and ethical algorithmic design. Scientific Awards and Recognition: ERC Advanced Grant POWVER ERC Proof-of-Concept Grant LEOpowver Member, Academia Europaea (2013) Dutch Vernieuwingsimpuls (2001) Preis des Fakultätentages Informatik (2013) ERC Advanced Investigators Grant (2016) Otto Mønsted-Guest Professorship (2013) Hermanns has advised numerous students and researchers, though specific names are not listed. He leads major research grants, including ERC projects and collaborative efforts within SFB/TRR 248. His leadership extends to program committees of top conferences such as CAV, CONCUR, TACAS, and QEST, and he has chaired LICS and ICALP. He is affiliated with the Dependable Systems and Software lab at Saarland Informatics Campus and contributes to the Schloss Dagstuhl ecosystem as scientific director and former president of its support association. His recent work is increasingly centered on the AI Act , algorithmic transparency, and the formal foundations of trustworthy AI.

• Autonomy of robots and vehicles
• Motion planning
• Optimal motion control
• +6 more

Björn Olofsson is an Associate Professor and Senior Lecturer in the Department of Automatic Control at Lund University's Faculty of Engineering. He also serves as the Director of First and Second Cycle Studies and is a Project Manager. He is affiliated with major research initiatives including ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and WASP (Wallenberg AI, Autonomous Systems and Software Program). His academic affiliations span Lund University and Linköping University, where he was appointed Docent in 2020. He holds an M.Sc. in Engineering Physics and a Ph.D. in Automatic Control, both from Lund University. His academic journey reflects a strong foundation in engineering and control systems. His research focuses on the autonomy of robots and vehicles, with emphasis on motion planning and optimal motion control. He explores applications in ground vehicles, unmanned aerial and surface vehicles, and industrial robotics. His work intersects with key global challenges, including sustainable transport and digitalization, aligning with UN Sustainable Development Goals related to technology and health. The 15 most recent publications analyzed show a consistent trend in autonomous systems, predictive control, and robotics. Topics include uncertainty-aware motion planning, human-robot collaboration, maritime autonomy, and learning-based control. The research integrates AI, machine learning, and advanced control theory, applied across aerial, marine, and terrestrial domains. Björn actively supervises multiple PhD students and has led numerous research projects, such as ELLIIT B14 and the Center for Construction Robotics. He is involved in organizing academic events like Robotics Week for Schools and manages the RobotLab LTH infrastructure. He has taught a range of courses including Applied Robotics, Autonomous Vehicles, and graduate-level courses on motion planning and optimal control. He also supervises Master’s theses in Automatic Control and Vehicular Systems.

John QuigleyView profile

Professor

• Risk Modeling
• Decision Support Systems
• Bayesian Networks
• +5 more

John Quigley is a Professor of Management Science at the University of Strathclyde, serving as Vice Dean (Research) of the Strathclyde Business School since 2021. Previously, he was Head of the Management Science Department (2016–2019). He holds a BMath in Actuarial Science from the University of Waterloo (1993) and a PhD in Management Science from the University of Strathclyde (1998). His research focuses on risk modeling and decision support systems, integrating data analysis and expert judgment. Key areas include large-scale manufacturing, offshore windfarm maintenance, and search and rescue (SAR) operations in Arctic regions. Notable projects include the NNSAR initiative (funded by NERC and NRC), enhancing SAR systems in Nunavut and Nunavik, and the Productivity and Sustainability Management project optimizing manufacturing processes via real-time data and probabilistic networks. He collaborates with industries like Rolls-Royce, Scottish Power, and the European Food Safety Agency. Quigley has received awards such as the FAIM 2025 Sullivan Best Paper Award and the 2010 KTP Award. His teaching spans undergraduate, postgraduate, and executive education programs globally, emphasizing technology integration in pedagogy.

Alexander RaaijmakersView profile

Assistant Professor

• MRI Technology
• RF Engineering
• Ultrahigh Field Imaging
• +4 more

Alexander Raaijmakers is an Assistant Professor with a joint position at Eindhoven University of Technology and University Medical Center Utrecht. He specializes in RF engineering for ultrahigh field MRI, body imaging at 7T+, and RF safety. His current projects involve developing hardware and sequences for clinical adoption of ultrahigh field MRI, with applications in heart, prostate, breast, liver, and kidney imaging. Educations: MSc in Applied Physics (University of Groningen, 2004) | PhD in Radiotherapy Physics (University Medical Center Utrecht, 2008) His research focuses on advancing MRI technology through innovations like dipole antennas in coil arrays, electromagnetic field optimization, and translational applications to 3T systems. He leads a team of 5 PhD students, 1 postdoc, and 2 RF engineers. Publications span MRI hardware innovation, RF safety methodologies, and deep learning-based image correction. Current projects emphasize clinical translation of ultrahigh field MRI while maintaining safety standards for implants and patients. Key Projects: 7 Tesla Research Group leadership | Dipole antenna integration in MRI coils

Riccardo De BinView profile

Associate Professor

• Statistical Learning
• High-dimensional Data Analysis
• Survival Analysis
• +4 more

Riccardo De Bin is an Associate Professor at the University of Oslo, affiliated with the Department of Mathematics and the Statistics and Data Science group. His research focuses on statistical methodology, including high-dimensional data analysis, survival analysis, boosting methods, and resampling techniques. He has contributed to interdisciplinary applications in biomedical research, energy systems, and aviation safety. Research Interests : Asymptotic theory and resampling techniques Variable selection and penalized regression Statistical learning methods for survival analysis Integration of clinical and omics data Applications in energy storage and environmental engineering Recent Work : Recent publications highlight advancements in survival modeling using boosted first-hitting-time approaches, degradation analysis for lithium-ion batteries, and Bayesian methods for nonlinear models. He also addresses methodological challenges in high-dimensional biomedical data and publication bias in statistical practice. Teaching : Teaches advanced courses in statistical learning, including STK2100 (Machine Learning for Prediction), STK4030 (Statistical Learning: Advanced Regression), and STK-IN4300 (Statistical Learning Methods in Data Science).

• Ship Design
• Marine Engineering
• LNG Transportation
• +5 more

Chao Ye is a Research Fellow at the KTH Royal Institute of Technology , working in the Aerospace, Moveability and Naval Architecture department. He is currently a Postdoc researcher on the SHIFT-DT project focusing on sustainable ship design and production through digital twins. His research bridges ship design (lines, stability, hydrodynamics) with production optimization and LNG transportation systems. PhD focused on LNG boil-off rate assessment in marine type C tanks Teaches courses like Modelling ship’s propulsion and manoeuvring and Digitalisation for Sustainable Production Research interests span ship structural integrity, propulsion systems, and environmental impacts. His work integrates numerical modeling, experimental studies, and multi-objective optimization for 3E (exergetic, economic, environmental) frameworks. Publications emphasize data-driven approaches (e.g., deep learning for temperature prediction) and fault diagnosis systems for marine machinery. Collaborates with Digital Futures initiatives for advanced maritime technologies.

Ingrid Kristine GladView profile

Professor

• Statistics and Data Science
• Machine Learning
• Anomaly Detection
• +5 more

Ingrid Kristine Glad is a Professor at the Department of Mathematics, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. She serves as co-director of the Integreat Centre of Excellence and BigInsight Centre for Research-Based Innovation, and chairs the Abel Board (2022–2026). Her research focuses on statistical and machine learning methodologies for high-dimensional data, particularly in genomics, sensor systems, and maritime applications. She has pioneered methods like monotone regression, tailored graphical lasso, and Shapley-value-based explainability frameworks. Research Interests: Glad’s work integrates theoretical statistics with practical applications in anomaly detection, change-point analysis, and predictive modeling. She develops novel algorithms for analyzing large-scale datasets from genomics (e.g., gene networks) and industrial sensor streams (e.g., battery degradation in maritime batteries). Her methods emphasize interpretability and scalability, addressing challenges in both supervised and semi-supervised learning contexts. Publications: Her recent work includes advancements in Shapley-value explanations, maritime battery health monitoring, and biofouling impact analysis. Key themes across her articles are statistical methodology development, machine learning applications in engineering, and computational tools for genomic data integration. Over 50 peer-reviewed papers span journals like Expert Systems with Applications , Journal of Machine Learning Research , and BMC Bioinformatics . Grants & Leadership: Leads interdisciplinary projects funded by the Norwegian Research Council and EU initiatives. Her roles in major centers highlight her influence in shaping statistical research agendas. Supervises active PhD students focused on topics like lifetime analysis models and maritime system analytics. Labs & Collaborations: Central to the Genomic HyperBrowser platform and collaborations with maritime industry partners. Active in both theoretical statistics (e.g., penalized regression) and applied domains (e.g., autonomous ship safety modeling).

• Computer Science
• Artificial Intelligence
• Cultural Studies
• +3 more

Francisco Manuel Sáez de Adana Herrero is a Professor in the Computer Science Department at Universidad de Alcalá. He holds doctorates from Universidad de Cantabria (2000) and Universitat Pompeu Fabra (2019). His research focuses on interdisciplinary cultural studies of the United States, multimodal narratives, and advanced numerical techniques. He leads the AMICUSS and MMN research groups and was part of the GTNA group. His work bridges computer science and cultural studies, with notable contributions to comics analysis, historical representation in media, and technical applications like 3D reconstruction and electromagnetic systems. Research interests include the intersection of technology and culture, with emphasis on graphic narratives, historical analysis, and educational applications of comics. He explores topics like multiverse theory in literature, radar technology history, and inclusive education strategies using visual media. Key publications analyze American cultural identity in comics, the Spanish Civil War’s portrayal in U.S. media, and technical innovations in wireless networks and GIS. His articles highlight interdisciplinary approaches to cultural studies, leveraging computational methods alongside humanities research. Awards and grants are not listed, but his extensive academic output reflects sustained contributions across computer science and humanities. He has advised doctoral theses at his own institutions and collaborates internationally on projects blending technical and cultural studies. He is affiliated with the Instituto Franklin/Universidad de Alcalá, contributing to cultural diplomacy and scientific exchange. His teaching focuses on multimodal communication and computational tools for cultural analysis.

• Signal Processing
• Sensor Networks
• Maritime Communication
• +3 more

Raúl Vicen Bueno is a Researcher at the University of Alcalá's Department of Electrical Engineering, affiliated with the AES3 research group focused on Acoustic and Electromagnetic Smart Sensor networks and Signal processing. He holds a PhD from the University of Alcalá, awarded in 2012 for his thesis on 'Automatic detection of signals by using artificial intelligence techniques,' supervised by Dr. Manuel Rosa Zurera and Dr. María del Pilar Jarabo Amores. His research interests include signal processing, sensor networks, maritime communication, and AI applications in environmental and military contexts. His work emphasizes optimizing underwater communication systems, securing underwater cables, and developing autonomous glider technology for naval operations. He has actively participated in NATO exercises such as CWIX 2016/2017, showcasing interoperable glider command-and-control capabilities. Notable contributions include the GliderC2 system, environmental condition-based algorithms, and sensor integration frameworks for anti-submarine warfare and maritime surveillance. Raúl has published extensively on sensor networks, underwater robotics, and decision support systems, with a focus on real-time data provision and environmental modeling. His articles address challenges in optimizing node movement in underwater networks, securing communication cables, and enhancing tactical decision-making through algorithmic innovation. He has also contributed to workshops and conferences on military applications of underwater glider technology, emphasizing future research directions and technological advancements. His academic contributions span both theoretical and applied domains, bridging electrical engineering, computer science, and maritime security. Collaborative efforts with institutions like CMRE (NATO Undersea Research Centre) highlight his role in developing practical solutions for naval operational effectiveness and environmental monitoring.

Andrew BarnardView profile

Adjunct Professor

• Acoustics
• Noise Control
• Vibration
• +6 more

Andrew Barnard is an Adjunct Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University (MTU) and currently serves as Vice President for Research. He holds a PhD in Acoustics from Penn State University and has held roles including Director of the Great Lakes Research Center (2019-2021) and Director of the Center for Acoustics and Vibration at Penn State (2023-2024). His expertise spans acoustics, noise control, vibration, and marine technology. He has contributed to advancements in carbon nanotube (CNT) loudspeakers, underwater acoustics for ice-covered environments, and maritime autonomous vehicles. Education: BS in Mechanical Engineering, MTU (2002) MS in Mechanical Engineering, MTU (2004) PhD in Acoustics, Penn State University (2010) Research Interests: Acoustic intensity and vector sensors Real-time control systems for noise reduction Machine learning for underwater source localization Commercialization of smart beach safety technology Acoustics of ice-covered environments Awards and Affiliations: Board Certified by the Institute for Noise Control Engineering (INCE) Active in professional organizations such as the Acoustical Society of America Labs and Teams: Former Director of the Great Lakes Research Center (GLRC) Collaborations with the Applied Research Laboratory at Penn State

Matthew DarguschView profile

Professor

• Advanced Manufacturing
• Materials Science
• Biodegradable Metals
• +4 more

Professor Matthew Dargusch is the Interim Executive Dean of the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. He holds dual roles as Chief Technology Officer of the Defence Materials Technology Centre (DMTC) and Associate Dean (Research). His expertise spans advanced manufacturing, materials science, and biomedical engineering. He has led major research hubs, including the ARC Research Hub for Advanced Manufacturing of Medical Devices, and secured over $50 million in grants, including a MRFF Artificial Heart Frontiers Program grant. Education: Bachelor (Honours) of Science (Advanced), The University of Queensland. Research focuses on addressing technical challenges in Australian metal manufacturing, particularly in aerospace, maritime, and medical device applications. His work emphasizes biodegradable metals, additive manufacturing, and corrosion engineering. Notable contributions include ultra-strong titanium alloys for 3D printing and corrosion-resistant magnesium alloys for biomedical use. Key awards include the Thatcher Brothers Prize (UK), Anders Gustaf Ekberg Prize, and Technology Award from TMS. Over 40 postgraduate students have graduated under his supervision, many now leading roles in academia and industry. He leads interdisciplinary projects with industry partners like Airbus, Boeing, and Cook Medical, translating research into practical manufacturing solutions. His labs focus on material characterization, additive manufacturing processes, and biomaterials.

Silvia FerrariView profile

Professor

• Robotics and Autonomy
• Artificial Intelligence
• Neuroscience
• +5 more

Silvia Ferrari is the John Brancaccio Professor of Mechanical and Aerospace Engineering at Cornell University, leading the Laboratory for Intelligent Systems and Controls (LISC) in Ithaca and co-directing the Cornell-Unibo Věho Institute on Vehicle Intelligence at Cornell Tech. She holds academic affiliations with both Cornell Engineering and Cornell Tech, focusing on interdisciplinary research in autonomy, robotics, and intelligent systems. Education: B.S., Aerospace Engineering, Embry-Riddle Aeronautical University (1997) M.A. and Ph.D., Mechanical and Aerospace Engineering, Princeton University (1999–2002) Research Interests: Her work spans computational intelligence, sensorimotor learning, adaptive control, and robotic autonomy. Key areas include active perception, neural network modeling, distributed sensor networks, and decision-making under uncertainty. She has pioneered methods in information-driven path planning and reinforcement learning, with applications to robotics, neuroscience, and environmental monitoring. Awards: Presidential Early Career Award for Scientists and Engineers (2006) NSF CAREER Award (2005) ONR Young Investigator Award (2004) Fellow of ASME and AIAA (2020) Labs & Leadership: As Director of LISC and co-Director of the Věho Institute, she fosters cross-campus collaborations in autonomy and urban technology. Her recent initiatives include organizing the Autonomy and Mobility Workshop, advancing Cornell’s ‘One Cornell’ research strategy.