Find Academic Supervisors

Augusto Luis BallardiniView profile

Researcher

• Autonomous Vehicles
• Sensor Fusion
• Computer Vision
• +3 more

Augusto Luis Ballardini is a Researcher at the Department of Automatic Control, Universidad de Alcalá (UAlcalá), Spain, within the INVETT Research Group focusing on Intelligent Vehicles and Traffic Technologies. He holds a PhD in Computer Science from the University of Milano-Bicocca (2017) and an MSc from the same institution (2012). His work integrates machine learning, computer vision, and sensor fusion for autonomous vehicle localization and decision-making systems. Key awards include a Marie Skłodowska-Curie Actions grant (GET-COFUND fellowship) and a Spanish Ministry grant under Maria Zambrano/NextGenerationEU. His research spans LiDAR-based localization, intersection classification, and uncertainty-aware neural networks. Education: PhD in Computer Science, University of Milano-Bicocca, 2017 MSc in Computer Science, University of Milano-Bicocca, 2012 Research Interests: Autonomous vehicles, sensor fusion, LiDAR and vision-based systems, machine learning for localization, traffic modeling, and safety-critical decision-making. His work emphasizes practical applications such as fail-aware odometry, knowledge graph-driven prediction, and urban scene understanding. Awards & Grants: Marie Skłodowska-Curie Actions research grant (2019) Maria Zambrano/NextGenerationEU project grant (2022) Lab & Collaborations: His research is conducted within the INVETT group at UAlcalá, focusing on interdisciplinary projects that bridge robotics, computer vision, and transportation engineering. Recent work includes benchmarking point cloud registration algorithms and developing explainable AI for autonomous driving.

• Computer Vision
• Robotics
• Sensor Fusion
• +4 more

Miguel Ángel García Garrido is an Associate Professor at the University of Alcalá's Electronics Department, specializing in robotics, computer vision, and sensor fusion. He leads the Robesafe research group (Service Robotics and e-Safety Technologies) and has expertise in traffic systems, autonomous navigation, and indoor positioning. His work integrates multi-sensor technologies with AI-driven data analysis. He earned his Ph.D. in 2010 with a thesis on vision-based traffic sign recognition systems. His research spans applications from automotive safety to retail analytics, combining traditional signal processing with modern machine learning techniques. Key contributions include fusion-based indoor positioning systems, real-time traffic sign detection algorithms, and automated infrastructure inspection tools. Recent work focuses on customer activity analysis in open malls using linguistic reporting and process mining. No scientific awards are explicitly mentioned. His research group collaborates on projects involving V2I communication, sensor calibration, and autonomous vehicle systems. No grants or advising details are provided in the source text.

• Computer Vision
• Robotics
• Image Processing
• +3 more

Pedro Gil Jiménez is a Professor at the University of Alcalá, affiliated with the Signal and Communication Theory Department within the Electrical Engineering field. He leads the GRAM Group (Multisensorial Recognition and Analysis Group), focusing on advanced computer vision and robotics applications. His doctoral research (2009) centered on optimizing artificial vision techniques for surveillance systems under Dr. Saturnino Maldonado Bascón's supervision. His research emphasizes interdisciplinary approaches, integrating machine learning, robotics, and sensor technologies. Key areas include traffic sign recognition systems, mobile robotics design (e.g., stair-climbing wheelchairs), and medical rehabilitation applications. He has pioneered work in video surveillance, noise reduction algorithms, and object detection systems using SVMs and clustering methods. Over 20 years of publications (2002–2025) reflect his contributions to image processing, autonomous systems, and educational strategies in engineering curricula. His work bridges theoretical advancements with practical applications in security, healthcare, and transportation sectors. No notable awards are listed, though his extensive publication record highlights sustained academic impact.

• Autonomous Vehicles
• Intelligent Transportation Systems
• Sensor Fusion
• +4 more

Ignacio Parra Alonso is a Professor at the Department of Automation in the University of Alcalá, Spain. He is affiliated with the INVETT research group (Intelligent vehicles and Traffic technologies), focusing on autonomous systems, sensor fusion, and intelligent transportation. His work emphasizes vehicle localization, trajectory prediction, and human-vehicle interaction in urban environments. He holds a PhD in Engineering from the University of Alcalá (2010), with a thesis on visual odometry for intelligent vehicles. His research interests span autonomous vehicle technologies, including sensor-based localization (e.g., WiFi, LiDAR, and vision systems), trajectory prediction using deep learning (e.g., Transformers and CNNs), and safety-critical systems for automotive applications. He has contributed to projects like DRIVERTIVE (2016 GCDC) and published extensively on topics like pedestrian intention recognition, lane change prediction, and fail-safe navigation systems. Recent publications (2021–2024) highlight advancements in transformer-based trajectory prediction, WiFi localization for autonomous driving, and bias evaluation in visual datasets. His work often bridges machine learning techniques with real-world automotive challenges, emphasizing fail-safe mechanisms and human-centric safety. No scientific awards are explicitly mentioned in the provided texts. His advising record is not detailed here, though he was supervised by Dr. Miguel Angel Sotelo Vázquez during his doctoral studies. He collaborates on projects involving multi-modal sensor integration and has developed tools for dataset labeling and performance analysis in autonomous systems. Led the INVETT group, which focuses on intelligent vehicles and traffic technologies, advancing interdisciplinary solutions for autonomous navigation challenges.

Haimei Helen ZhaoView profile

Research Fellow

• Multimodal machine learning
• Generative AI
• Digital health
• +3 more

Dr. Haimei Helen Zhao is a Research Fellow and ICT Director at the School of Biomedical Engineering, University of Sydney. She holds a PhD from the Sydney AI Centre (2024) and an M.Eng. from Tsinghua University (2020). Her research focuses on AI-driven biomedical technologies, including generative AI, digital health diagnostics, and translational research. Current projects include SmartClot-AI, a blood coagulation testing platform, and generative AI models for stroke prediction. Education: PhD in Computer Science (University of Sydney, 2024), M.Eng. in Computer Science (Tsinghua University, 2020). Research interests span multimodal machine learning, biosensing, and low-cost diagnostic systems. Awards include the 2024 PERIscope Commercialisation Award and 2024 Faculty of Engineering Career Advancement Award. Leadership roles: ICT Director of School of Biomedical Engineering, interim Snow Manager of Ju-Snow Lab, and former DEI Committee Chair. Active in grant development and industry partnerships.

Bryan ConvensView profile

Researcher

• Robotics
• Control Systems
• Real-Time Systems
• +3 more

Bryan Convens serves as a Researcher in the Department of Applied Mechanics at Vrije Universiteit Brussel, specializing in advanced robotics control systems with emphasis on safety, real-time performance, and human-robot collaboration. His work bridges theoretical control theory with practical applications in aerial robotics, prosthetics, and industrial automation, supported by active leadership in multiple research projects. Research interests focus on developing certified-safe control frameworks where Convens pioneers reference governor methodologies for constraint satisfaction in dynamic environments. His investigations span nonlinear model predictive control, learning-based adaptation for uncertainty handling, and energy-efficient actuation systems—particularly for prosthetic devices and multi-robot swarms. Current projects address resilient control under uncertainty (FWOTM1231), robotics adoption in industry (BRGRD68), and series-parallel elastic actuation for prosthetics (FWOSB33), demonstrating consistent innovation in safety-critical domains. Scientific contributions reveal an evolving trajectory toward integrating machine learning with classical control theory to enhance data efficiency while maintaining safety guarantees. Recent publications emphasize real-time feasibility, human-robot collaboration constraints, and robust perception techniques—indicating strategic movement toward deployable solutions for industrial and medical robotics applications. Scientific Awards Best Ph.D. thesis of 2023 by the Belgian National Committee for Theoretical and Applied Mechanics (NCTAM) FWO Travel Grant for Long Research Stay Abroad (2018) Public and jury prize: 2nd place (2020) Convens directs significant research funding through competitive grants including FWOTM1231 (€ project value unstated, 2024-2027), BRGRD68 (Policy-Based project, 2022-2025), and FWOSB33 (Fundamental Research, 2017-2020), focusing on resilient aerial robotics, digital twin integration, and prosthetic energy efficiency. His grant portfolio demonstrates strong alignment with Flanders' strategic priorities in robotics and AI, with active industry partnerships through AI Flanders initiatives. Mentorship activities include supervising junior researchers in complex project environments, though formal student lists aren't documented. He operates within VUB's robotics ecosystem through the Robotics & Multibody Mechanics research group, collaborating closely with Bram Vanderborght's team on actuation systems and Kelly Merckaert on safety frameworks. Current work leverages UWB/VIO sensor fusion and particle filtering techniques within active projects targeting 2025-2027 deliverables for multi-robot coordination in unstructured environments.

Linus Härenstam-NielsenView profile

Researcher

• 3D Reconstruction
• Pose Estimation
• Optimization Methods
• +2 more

Linus Härenstam-Nielsen is a PhD student and researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Informatics 9 department (Computer Vision Group) under the supervision of Prof. Daniel Cremers. He received his M.Sc. in Systems Control and Robotics from KTH, Royal Institute of Technology (Stockholm, Sweden) in 2018. Prior to his PhD, he gained approximately 3 years of industry experience as a computer vision engineer at Univrses, working on visual odometry and deep learning for autonomous driving. He also spent one year at Artisense before beginning his PhD at TUM in September 2021. Linus's research focuses on 3D reconstruction and pose estimation from multiple sensors and optimization methods for computer vision and deep learning . His work bridges theoretical optimization techniques with practical applications in computer vision, particularly in handling noisy and incomplete sensor data. His publications demonstrate expertise in robust multiview triangulation, neural implicit surface fitting, and camera calibration, with research appearing in top-tier conferences including CVPR and ECCV. His work on 'Semidefinite Relaxations for Robust Multiview Triangulation' introduced convex relaxations to solve triangulation problems with outliers, enabling certifiably optimal solutions. He has also developed techniques like DiffCD for improving neural implicit surface fitting through symmetric differentiable Chamfer distances.

Nicolai PetkovView profile

Professor

• Computer Vision
• Image Processing
• Pattern Recognition
• +7 more

Nicolai Petkov is a Professor in the Department of Computing Science at the University of Groningen's Faculty of Science and Engineering. He leads research in computer vision, image processing, and computational neuroscience, with a focus on biologically inspired algorithms. His work bridges theoretical neuroscience and practical computer vision applications. His research interests span computer vision, image processing, pattern recognition, computational neuroscience, and biologically inspired computing. Petkov has made significant contributions to contour detection, texture analysis, and visual perception modeling, developing algorithms inspired by the human visual cortex. His work on B-COSFIRE filters has become influential in the field of biologically inspired computer vision. Petkov's publication record shows consistent research output over decades, with recent work focusing on applications in agriculture, robotics, and medical diagnostics. His research demonstrates a progression from theoretical models of visual perception to practical applications in diverse domains. Best paper award for 'Image-based localisation using Gaussian processes' (2016) Best student paper award for 'Contour detection by multiresolution surround inhibition' (2006) Best student paper award for 'Integrated electronic health record database management system' (2015) Top-1% in engineering according to the Essential Science Indicators of the ISI Web of Knowledge Petkov has supervised numerous PhD students and collaborators including George Azzopardi, Nicola Strisciuglio, and Giovanni Papari. His research has received funding for projects in intelligent systems and computer vision applications. He is actively involved in governance at the University of Groningen, having served on the University Council. His laboratory, part of the Intelligent Systems research group, focuses on developing brain-inspired computing approaches with applications ranging from agricultural technology to medical diagnostics and autonomous robotics.

• Resilient Robotic Autonomy
• Aerial Robotics
• Underwater Robotics
• +6 more

Konstantinos Alexis is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He leads the Autonomous Robots Lab and serves as Principal Investigator for major international projects including the DARPA Subterranean Challenge. His research focuses on developing resilient autonomous systems capable of operating in extreme environments through resourcefulness, robustness, and redundancy. His research interests span resilient robotic autonomy with emphasis on aerial robotics , underwater robotics , robot control , path planning , robot learning , and Simultaneous Localization and Mapping (SLAM) . He takes a holistic approach across these disciplines to enable autonomous systems to navigate challenging environments including subterranean spaces, underwater operations, and extreme terrestrial conditions. Recent work demonstrates significant advances in collision-tolerant navigation, degradation-resilient state estimation, and semantic-aware inspection planning. Professor Alexis has secured substantial funding from diverse sources including US agencies (DARPA, NSF, DOE, USDA), EU Horizon programs, and the Research Council of Norway. His research portfolio includes field deployments in nuclear environments, aquaculture operations, and planetary exploration scenarios. Principal Investigator for DARPA Subterranean Challenge Major grants from NSF, DOE, USDA, and EU Horizon programs Research Council of Norway funding for multiple projects He actively supervises numerous PhD and Master's students, with recent graduates leading publications in top robotics venues. His lab maintains strong collaborations with international research groups and industry partners for real-world deployment of autonomous systems. Current initiatives include the ResiFarm project for underwater operations in fish farms and advanced exploration systems for Martian lava tube environments.

• Ultrasonic Measurement Techniques
• Fluid Mechanics
• Robotics for Leak Detection
• +2 more

Tomonori Ihara is a researcher affiliated with the Tokyo Institute of Technology , focusing on advanced ultrasonic measurement techniques for fluid dynamics and industrial applications. His work spans robotics for environmental monitoring, multiphase flow analysis, and high-temperature material studies. Education: Doctor (Engineering) from Tokyo Institute of Technology (2015). Research Interests: He specializes in Ultrasonic Doppler Velocimetry , Bubble Dynamics , and Swirling Flow Analysis , applying these to critical areas like nuclear engineering, molten glass flow, and water leak detection. His methodological contributions include noise modeling, phased-array systems, and multi-dimensional velocity profiling. Patents: He holds multiple registered patents for ultrasonic measurement devices, including flow velocity vector distribution systems and buffer rod designs for molten materials. His 23 publications (2012–2020) emphasize practical solutions for industrial fluid monitoring, with a focus on robotics and sensor innovation.

G.C.H.E. de CroonView profile

Researcher

• Robotics
• Aerospace Engineering
• Autonomous Systems
• +7 more

G.C.H.E. de Croon is a Senior Researcher in the Control & Simulation group at Delft University of Technology's Faculty of Aerospace Engineering . His work focuses on: Autonomous drone control systems Bioinspired robotics Visual-inertial navigation Neuromorphic computing for UAVs Tilt-rotor aircraft design Swarm robotics Recent publications highlight advancements in flapping-wing robots and neuromorphic attitude estimation. His research combines biological insights with aerospace engineering to solve complex autonomy challenges. Notable scientific recognitions include: NWO Vici Grant (2024) for drone autonomy research Autonomous Drone Racing Championship winner (2025) Multiple best paper awards (2015, 2021) He actively organizes academic events like the International Micro Air Vehicle Conference and has supervised 17 research projects. Media coverage spans AI drone racing breakthroughs (2025), swarming technology (2024), and maritime landing systems (2025 PhD thesis data).

Man-Ki YoonView profile

Assistant Professor

• Trustworthy and Accountable Computing
• Autonomous Systems
• Real-Time Embedded Systems
• +4 more

Man-Ki Yoon serves as an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His academic journey includes a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (2017), an M.S. from the same institution (2011), and a B.S. in Computer Science and Engineering from Seoul National University in Korea (2009). Prior to joining NC State, he worked as a research scientist in Computer Science at Yale University. His educational background demonstrates a strong foundation in computer systems with progressive specialization in real-time and embedded computing. The transition from his undergraduate studies in Korea to doctoral research in the United States reflects an international academic trajectory focused on increasingly specialized aspects of computer systems. Yoon's research centers on trustworthy and accountable computing for autonomous systems, with particular emphasis on autonomous vehicles and unmanned aerial vehicles. His work bridges theoretical formal methods with practical security applications in real-time embedded systems and the Internet of Things. This research agenda addresses critical challenges in verifying temporal behavior of real-time applications while maintaining strong security guarantees against timing-based side channel attacks. His approach combines insights from operating systems, security, and distributed systems to develop novel solutions that enable reasoning about fault events, component responsibility, and evidence trustworthiness in complex cyber-physical environments. Analysis of his recent publications reveals a clear evolution in research focus from foundational real-time scheduling problems toward increasingly sophisticated security mechanisms for autonomous systems. His work demonstrates consistent progression from theoretical verification techniques to practical implementations addressing accountability in distributed systems and security challenges in autonomous vehicle ecosystems. The publications show growing emphasis on AI/ML integration with traditional real-time systems, particularly in safety-critical applications where accountability and verifiability remain paramount. NCDOT Transportation Center of Excellence: Sustainable and Resilient Infrastructure – Cyber–Resilient Traffic Management Centers Faculty Research and Professional Development Award IBM Workforce Development Initiative Award SaTC: CORE: Small: Partition-Oblivious Real-Time Hierarchical Scheduling Yale Course-based Undergraduate Research Experience (CURE) Grant Qualcomm Roberto Padovani Scholarship Intel PhD Fellowship Qualcomm Innovation Fellowship Yoon actively mentors students through research projects and teaching, with numerous publications featuring student co-authors spanning topics from autonomous vehicle security to real-time system verification. His current NSF-funded research on partition-oblivious scheduling demonstrates significant grant acquisition capability, addressing fundamental security challenges in real-time hierarchical scheduling where conventional time-partitioning mechanisms fail to achieve strong temporal isolation from a security perspective. This work develops algorithmic solutions enabling non-interference-based security among partitions, allowing systems to employ advanced hardware and software technologies for high-end real-time applications in secure environments. His laboratory environment emphasizes hands-on experimentation with autonomous systems, evidenced by his self-driving car course where students build software systems for miniaturized autonomous vehicles. This practical approach complements theoretical research with tangible implementations, creating a comprehensive research ecosystem that spans from formal verification to physical system deployment.

Jean-Marc LasgouttesView profile

Researcher

• Probabilistic Modeling
• Statistical Physics
• Intelligent Transportation Systems
• +5 more

Jean-Marc Lasgouttes is a Researcher at Inria Paris working within the Astra project team, a joint research initiative between Inria Paris and Valeo. He also holds a teaching position at INSA Rouen Normandie's Department of Mathematical Engineering, where he has instructed courses including Boosting methods (until 2024), Functional Data Analysis (until 2018), and general Data Analysis (until 2024) for both the Mathematical Engineering department and the Specialized Master's program in Data Science. Dr. Lasgouttes' primary research focuses on probabilistic modeling of large systems using statistical physics tools, with particular emphasis on Intelligent Transportation Systems. His work spans traffic flow modeling, vehicle platooning, urban traffic prediction, and geopositioning systems. He frequently employs Markov Random Fields, statistical physics approaches, and game theory to address complex transportation challenges, bridging theoretical statistical methods with practical applications. His research methodology often involves developing novel algorithms like the ★-IPS family for incremental GMRF estimation. Analysis of his recent publications reveals a consistent trajectory of applying advanced probabilistic models to increasingly sophisticated transportation scenarios. His work demonstrates strong expertise in spatio-temporal modeling, with publications covering car-following dynamics, landmark-based positioning, cooperative ITS, and autonomous vehicle systems. The interdisciplinary nature of his research connects statistical physics, machine learning, and transportation engineering to solve real-world mobility challenges. At INSA Rouen Normandie, Dr. Lasgouttes has developed comprehensive teaching materials for data analysis courses, including practical applications of principal component analysis and correspondence analysis using real-world datasets such as European protein consumption patterns and Titanic passenger data. His educational approach emphasizes hands-on implementation with R programming, reflecting his commitment to practical statistical applications. The Astra project team serves as Dr. Lasgouttes' primary research environment, facilitating collaboration between academic researchers and industry partners to address contemporary transportation challenges. His work has contributed to significant research events including the 2018 workshop on Large Random Networks and Constrained Walks honoring Guy Fayolle's 75th birthday, and the 2012 interdisciplinary workshop on inference associated with the Travesti ANR grant.

• Localization and Tracking
• Sensor Networks
• Vehicular Networks
• +5 more

Pınar Oğuz Ekim is an Associate Professor in the Department of Electrical and Electronics Engineering at Izmir University of Economics, Turkey. Her academic career spans research and teaching roles at Gebze Technical University (2014-2015) and Nanyang Technological University, Singapore (2015-2016), with current leadership in sensor network research and intelligent transport systems development. Her educational qualifications include: Bachelor of Telecommunication and Computer Engineering, Middle East Technical University (METU), 2003 Master of Circuit and Systems, Department of Electrical and Electronics Engineering, METU, 2006 PhD in Electrical and Computer Engineering, ISR-IST, Lisbon, 2013 (with Distinction) Dr. Ekim specializes in theoretical and applied aspects of localization algorithms, sensor fusion, and optimization theory. Her research bridges nonlinear signal processing with real-world navigation systems, focusing on vehicular networks and robotics. Key contributions include distributed tracking frameworks and embedded optimization techniques for energy management, demonstrating exceptional interdisciplinary integration across electrical engineering domains. Her publication record (2020-2023) reveals strong thematic evolution from core localization algorithms toward applied energy systems and assistive technologies. This trajectory highlights her ability to translate theoretical signal processing concepts into solutions for residential energy optimization, municipal waste forecasting, and accessibility tools, while maintaining foundational work in robotic navigation through UWB/LiDAR fusion. Scientific recognition includes: 2012 IBM Scientific Award (Portugal's most prestigious national research honor)

• Digital Design
• Embedded Systems
• Circuits and Systems Theory
• +6 more

Professor Ece Olcay Güneş is affiliated with the Faculty of Electrical and Electronics Engineering at Istanbul Technical University , where she holds the rank of Professor in the Department of Electronics and Communication Engineering. Her career spans over two decades, including administrative roles such as Deputy Head of Department (2023–2024, 2023–2026) and Graduate Program Coordinator (2013–2021). Research Focus : Digital Design, Embedded Systems, Stochastic Computing, and Artificial Intelligence. Key Contributions : Hardware-software co-optimization, resolver error compensation, post-quantum cryptography acceleration, and stochastic vision machine simulation. Awards : GLSVLSI'23 Best Paper Award (2023). Grants : BAP and TUBITAK-funded projects in approximate computing, genetic risk analysis, and RF transmitter design. Contact : gunesec@itu.edu.tr