Find Academic Supervisors

• Computer Vision
• Autonomous Systems
• Robotics
• +12 more

Dr Lounis Chermak is a Lecturer in Computer Vision and Autonomous Systems at the Centre for Electronic Warfare, Information and Cyber, part of Cranfield Defence and Security at Cranfield University, UK. He leads the Joint Autonomy Lab and is actively involved in research and education in autonomous systems with applications in defence and space. Research Interests: His work focuses on situational awareness in autonomous platforms, with core expertise in computer vision, sensor fusion, artificial intelligence, robotics, and navigation. He investigates perception, decision-making, and mobility across aerial, ground, maritime, and space systems, developing robust solutions for challenging environments including low visibility and extreme illumination. The recent publications reflect a strong trend in autonomous navigation, particularly for space and defence applications, using advanced computer vision techniques such as thermal stereo odometry, HDR imaging, stixel-based scene understanding, and lightweight 3D descriptors. Research also extends to cybersecurity of autonomous systems, including impersonation attack detection and optical countermeasures. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: Dr Chermak leads research activities supported by postdoctoral researchers, PhD, and MSc students. His work is funded and applied in collaboration with major clients including aerospace organizations (ESA, UK Space Agency, Thales Alenia Space), defence agencies (MoD, DSTL, BAE Systems, MBDA), and technology companies (Samsung, Astroscale). He supervises research students in robotics and autonomous systems across civilian and defence domains. Labs and Teams: He leads the Joint Autonomy Laboratory, a 200 m² indoor facility equipped with drone netting, motion capture systems, virtual reality test benches, UAV and ground robot fleets, electric vehicles, and multiple sensors for vision, ranging, and motion. This lab supports both educational and cutting-edge research in autonomous systems.

Daniel GroosView profile

Researcher

• Deep Learning
• Computer Vision
• Medical Technology
• +3 more

Daniel Groos is a Researcher at the Department of Computer Science, NTNU, specializing in the development of machine learning models for medical and sports-related motion analysis. His work focuses on applying deep learning techniques to video-based movement analysis for early detection of cerebral palsy in infants and performance analysis in elite sports. Education: PhD in Medical Technology (NTNU, 2018-2022), MSc in Computer Science with specialization in AI (NTNU, 2013-2018). Research interests include interdisciplinary collaborations with St. Olavs Hospital and Norwegian Open AI Lab. Key topics are deep learning applications in healthcare, computer vision for movement analysis, and sports biomechanics. Publications emphasize automated clinical analysis, video-based diagnostics, and human pose estimation. Notable projects include a deep learning method for cerebral palsy prediction and motion tracking systems for elite ski jumpers. Collaborations with institutions like the Centre for Elite Sports Research and Olympiatoppen highlight his work in sports performance analysis. No formal scientific awards listed but active in academic outreach with lectures at European conferences on childhood disability and movement analysis.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +2 more

Krister Wolff is an Associate Professor of Adaptive Systems at the Department of Mechanics and Maritime Sciences (M2) at Chalmers University of Technology. He also serves part-time as Vice Head of Department for Education. His research focuses on applying artificial intelligence, machine learning, and bio-inspired methods to robotics, autonomous systems, and self-driving vehicles. He teaches in the international Master's program in Complex Adaptive Systems. His work includes projects such as AI-supported vehicle suspension design, propeller optimization using genetic algorithms, and developing interactive robots for social distancing in healthcare settings. He has contributed to over 39 publications and 9 research projects, collaborating with organizations like VINNOVA and the Swedish Transport Administration. Notable projects include ISOLDE for hospital robots and Tactical Decision-Making in Autonomous Driving funded by the Wallenberg Foundation. Key areas of expertise include reinforcement learning for autonomous vehicles, evolutionary algorithms in design optimization, and driver behavior modeling in critical scenarios. His research bridges theory and practical applications, emphasizing collaboration between academia and industry.

Renato NegraView profile

Professor

• High Frequency Electronics
• Cyber-Physical Systems
• Neuromorphic Computing
• +7 more

Prof. Dr. Renato Negra is a faculty member at RWTH Aachen University, serving as the Chair of High Frequency Electronics within the Faculty of Electrical Engineering and Information Technology. His research is centered on advanced electronic systems with a focus on reconfigurable and low-power architectures for real-time applications. Research Interests: His work spans high frequency electronics, neuromorphic computing, embedded systems, and cyber-physical systems. He develops FPGA-based and edge-computing solutions for computer vision, robotics, and smart infrastructure, particularly in elderly monitoring and autonomous navigation. His research integrates deep learning with hardware optimization for energy efficiency and real-time performance. The recent publications highlight a strong trend toward event-based vision , neuromorphic sensors , and low-power embedded AI , applied in domains such as smart cities, healthcare, and robotics. There is a consistent emphasis on real-time processing, reconfigurable systems, and the deployment of neural networks on constrained hardware platforms. Scientific Awards: No awards or honors were mentioned in the provided text. Advising and Grants: While no specific students or advising roles are listed, the volume and depth of publications suggest active supervision or collaboration within research projects. Although no grants are explicitly named, involvement in EU-level initiatives (e.g., FitOptiVis ECSEL Project) and national R&D programs (e.g., BIO-PERCEPTION) can be inferred from the research topics and publication contexts. Labs and Teams: Prof. Negra leads the research activities in High Frequency Electronics at RWTH Aachen. While not directly linked to the Computer Vision and Robotics Lab (CVR-Lab) mentioned in the text, his work aligns closely with neuromorphic and CPS research themes, suggesting potential interdisciplinary collaboration.

Dorsa SadighView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +5 more

Dorsa Sadigh is an Associate Professor of Computer Science and Electrical Engineering at Stanford University, and a Senior Fellow at the Stanford Institute for Human-Centered AI. Her work focuses on advancing robotics , particularly in areas such as human-robot collaboration , reinforcement learning , and vision-language models . She explores how robots can learn from human demonstrations, adapt to dynamic environments, and safely interact with humans in caregiving and assistive tasks. Her research interests span autonomous systems , improving robot generalization , and foundation models for robotics . Key projects include developing policies for dexterous manipulation, proactive human-robot teamwork, and scalable data collection methods. She emphasizes ethical considerations in robotics, including perceived safety and human trust. Recent work highlights include the ProVox framework for personalized collaboration, HoMeR for mobile manipulation, and Octo —an open-source generalist robot policy. Her contributions bridge theoretical advances in AI with real-world robotic applications, leveraging large language models and vision-language integration. Dr. Sadigh’s research is funded by grants from NSF, DARPA, and industry partnerships. She collaborates with interdisciplinary teams to address challenges in assistive robotics, autonomous driving, and socially intelligent AI systems.

Malcolm HeywoodView profile

Professor

• Genetic Programming
• Coevolution
• Reinforcement Learning
• +12 more

Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.

Janarthanan RajendranView profile

Assistant Professor

• Reinforcement Learning
• Deep Learning
• Artificial Intelligence
• +12 more

Janarthanan Rajendran is an Assistant Professor and the Sexton Chair in Reinforcement Learning at the Faculty of Computer Science, Dalhousie University, in Halifax, Nova Scotia, Canada. He is actively involved in research, teaching, and mentoring, with a focus on deep reinforcement learning and its applications in complex, dynamic environments. Education: Postdoctoral Fellow, Mila Quebec AI Institute and University of Montreal, Canada (2023) PhD in Computer Science and Engineering (AI stream), University of Michigan, Ann Arbor, USA (2021) MTech and BTech in Electrical Engineering, Indian Institute of Technology Madras, India (2016) His research focuses on enabling machines to learn through interaction, with core interests in deep reinforcement learning, model-based RL, multi-agent systems, transfer learning, and applications in materials science and economics. He also explores the integration of large language models and foundation models into reinforcement learning frameworks. His work emphasizes adaptivity, lifelong learning, and societal implications of AI. The most recent publications show a strong trend in advancing cooperative multi-agent systems, developing adaptive and memory-efficient RL methods, and applying RL to real-world challenges such as crystal design and dynamic pricing. His research bridges theoretical innovation with practical application, often in interdisciplinary contexts. Scientific Awards: Sexton Chair in Reinforcement Learning Dr. Rajendran is actively involved in mentoring graduate students and fostering an inclusive research environment. He is currently recruiting PhD and MCS students at Dalhousie University. He has no formal grants listed in the text, but his research chair and active publication record suggest strong funding support. He is also engaged in the broader AI community, having organized and participated in major conferences such as the Atlantic Canada AI Summit and NeurIPS. Labs and Research Groups: He leads a research group focused on deep reinforcement learning at Dalhousie University, working on topics including model-based RL, off-policy learning, and leveraging external knowledge sources. The group emphasizes inclusivity and supports underrepresented groups in computer science research.

• Non-Destructive Evaluation
• Ultrasonic Testing and Imaging
• Eddy Current Testing
• +9 more

Dr. Ehsan Mohseni is a Senior Lecturer in the Department of Electronics and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. He is a key member of the Centre of Ultrasound Engineering (CUE) research group and supports the Royal Academy of Engineering and Spirit AeroSystems research chair led by Professor Gareth Pierce. His work focuses on advancing robotic and intelligent Non-Destructive Evaluation (NDE) systems for industrial applications. B.Sc. in Materials Science and Metallurgical Engineering, University of Tehran, 2006 M.Sc. in Metal Forming Processes, University of Tehran Ph.D. in Automated Defect Detection using Electromagnetic NDE, École de Technologie Supérieure (ETS), Montreal, Canada Dr. Mohseni’s research is centered on NDE 4.0, integrating advanced sensing, multi-physics modeling, and machine learning. His expertise spans ultrasonic and eddy current testing, multi-sensor data fusion, and probability of detection studies. He focuses on applications in additive manufacturing, welding, composites, and metal processing, aiming to overcome current technological barriers in industrial inspection. The recent publications highlight a strong trend toward intelligent, automated, and robotic NDE systems. Key themes include self-supervised learning for ultrasonic segmentation, human-machine collaboration in data analysis, and advanced signal processing for weld and composite inspection. The integration of AI, flexible sensor arrays, and embedded navigation systems reflects a shift toward smart, adaptive inspection platforms aligned with Industry 4.0. Scientific Awards: The BINDT Annual Conference Award (2019) Dr. Mohseni is actively involved in research funding and knowledge transfer. He serves as Principal Investigator on KTP projects with ETHER NDE LIMITED and NATIONAL OILWELL VARCO UK LIMITED, focusing on in-process inspection for additive manufacturing and field calibration for ultrasonic testing. He contributes to large-scale collaborative projects funded by Innovate UK and industry partners, emphasizing practical deployment of NDE solutions. He also supervises research staff and collaborates with global aerospace firms including Pratt & Whitney Canada, Safran, and Bell Helicopter. He is a core member of the Centre of Ultrasound Engineering (CUE), a dynamic research group developing next-generation ultrasound technologies. The team works on advanced robotic sensing hubs, flexible transducer arrays, and AI-driven data interpretation tools, often in collaboration with the Royal Academy of Engineering research chair and industrial partners.

Marcus VetterView profile

Professor

• Deep Learning
• Medical Technology
• Embedded Systems
• +4 more

Prof. Dr. Marcus Vetter is the founder and director of the Institute for Applied Artificial Intelligence and Robotics (A²IR) at Mannheim University of Technology's Faculty of Information Technology. His work bridges Deep learning Medical imaging and navigation Embedded systems Real-time computing Software engineering for medical devices He has taught courses including Deep Learning Methods, Image-Guided Medicine, and Embedded Systems. Education Computer Science, Technical University of Mannheim, 1999 Doctorate ('summa cum laude superato') in 'Image-based navigation systems', University of Heidelberg, 2003 Research focuses on AI-driven medical imaging tools, real-time deformation models, and open-source frameworks like MITK. His 15 most recent publications span 6D pose estimation for medical robotics Spectroscopy-based diagnostics Formal software verification Gesture and gaze recognition interfaces UAV drive train optimization Scientific achievements Doctorate with distinction (2003) Co-founder of MITK open-source project Director of A²IR institute since 2007 He has received BMBF grants for real-time deformation models and tracking systems, and has led development of navigation systems for laparoscopic surgery and cardiac ablation procedures.

• Obstetrics
• Maternal-Fetal Medicine
• Prenatal Therapy
• +3 more

Anna David is a Professor of Obstetrics and Maternal Fetal Medicine at University College London (UCL) . She serves as Director of the EGA Institute for Women’s Health and Deputy Director of Tommy's National Centre for Preterm Birth Research . David is also Visiting Professor at Katholieke Universiteit Leuven and holds the Professor Tan Seang Lin, Dr Grace Tan and OriginElle Fertility Distinguished Chair in Women’s Health since 2025. Education: BSc in Medical Science, University of St Andrews (1989) MB ChB, University of Manchester (1992) PhD in Fetal Gene Therapy, UCL (2005) Research Interests: David leads the Prenatal Therapy Group at UCL, focusing on developing prenatal treatments for severe fetal disorders. Her work spans fetal gene therapy , maternal VEGF gene therapy for growth restriction , fetal stem cell transplantation , and advanced imaging for fetal surgery . She also investigates preterm birth prediction/prevention and fetal growth restriction . Scientific Awards: Fellow of the Royal College of Obstetricians & Gynaecologists (2016) NIHR Senior Lectureship in Women’s Health (2008) Leadership & Collaboration: She is Lead for Women's Health Shadow Theme at the NIHR UCLH Biomedical Research Centre and Head of the Research Department of Maternal Fetal Medicine since 2016. David collaborates with institutions like KU Leuven and organizations such as Tommy's and Magnus Growth .

Jef VandemeulebrouckeView profile

Researcher

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

Jef Vandemeulebroucke is a researcher at the Department of Electronics and Informatics , Vrije Universiteit Brussel (VUB) , specializing in medical imaging, computer vision, and augmented reality applications in healthcare. His work bridges artificial intelligence with radiology and biomechanics , focusing on automated segmentation, predictive modeling, and real-time surgical navigation systems. Research interests include: Medical image analysis for disease prognosis (e.g., COVID-19 severity , neurosurgical drains ) Development of MedShapeNet , a 3D medical shape dataset for computer vision Augmented reality systems in orthopedic and neurosurgical interventions AI-driven fluorescence endoscopy and dynamic CT for joint kinematics Key trends in his 140+ publications emphasize deep learning , image registration , and 4D-CT applications . Supervised theses include brain age prediction and chest radiography automation. Active in 38 projects (e.g., AI-NIMO , TumorScope ), he collaborates with institutions like the Universitair Ziekenhuis Brussel (UZB) and FWO (Fund for Scientific Research-Flanders).

Mingfeng WangView profile

Senior Lecturer

• Continuum Robotics
• Legged Robotics
• Soft Robotics
• +5 more

Dr. Mingfeng Wang is a Senior Lecturer in Robotics and Autonomous Systems at Brunel University London, affiliated with the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences. His research focuses on specialized robotic systems including continuum, legged, soft, precision farming, and miniaturized robots. Chartered Engineer (CEng) with Engineering Council UK Fellow of the Higher Education Academy (FHEA) Member of IEEE, IEEE-RAS, IMechE, and IFToMM Editorial roles: Associate Editor of International Journal of Advanced Robotic Systems (JCR-Q3); Associate Editor of Frontiers in Robotics and AI (JCR-Q2); Editor of Information Processing in Agriculture (JCR-Q1), Biomimetic Intelligence and Robotics (JCR-Q1), and STEM Education Research expertise includes: Continuum Robotics : Design of extra-slender continuum robots (diameter-to-length ratio Legged Robotics : Parallel mechanism-based biped and hexapod robots for extreme environments Miniaturized Robotics : Active locomotion and drug delivery in capsule endoscopes Soft Robotics : Compliant end-effectors and bio-inspired designs Precision Farming : Laser weeding systems and agricultural automation Key scientific awards: BRIEF award (2022) TAROS Best Paper Post Nomination (2022) IFToMM Asian-MMS Best Paper Award (2014) Recent publications focus on: Cochlear implant surgery robotics Passive compliance in train fluid servicing Snake-biomimetic sealing surfaces Parallel kinematic manipulators Capsule endoscope image enhancement Professional services include conference organization (TAROS 2023/2024 Steering Committee; TAROS 2024 Programme Chair) and journal refereeing for IEEE-ASME Transactions on Mechatronics and Scientific Reports.

• Artificial Intelligence
• Computer Science
• Swarm Robotics
• +5 more

Ramón Luis Rizo Aldeguer is a University Professor in the Department of Computer Science and Artificial Intelligence at the Higher Polytechnic School of the University of Alicante. He has held this position since 1996 and continues to be actively involved in teaching and research as recently as 2025. He previously served in various leadership roles including Director of the Department of Computer Science and Artificial Intelligence (1997-2004) and Deputy Director of the Institutional Projects Area at the University of Alicante (2012-2020). His educational background includes a PhD in Computer Science from the Polytechnic University of Valencia (1992) and a degree in Mathematics from the University of Valencia (1977). He has been a member of the Spanish Association for Artificial Intelligence since 1990 and has held leadership positions within the organization. Rizo Aldeguer's research focuses on artificial intelligence with particular emphasis on swarm robotics, UAV deployment, and deep reinforcement learning. His work bridges theoretical foundations with practical applications in robotics and autonomous systems. He has made significant contributions to educational methodologies, particularly in integrating computational tools into engineering education. His publication record shows a consistent trajectory in swarm intelligence and robotics, with recent publications (2018-2023) demonstrating increasing sophistication in applying deep reinforcement learning to complex multi-agent systems. His research spans both theoretical advancements and practical implementations in robotics and autonomous systems. Fifteen five-year research periods (trienios) Six teaching merit periods Five six-year research periods (sexenios) President of Organizing Committee of VI Conference of Spanish Association for Artificial Intelligence (1995) President of Scientific Committee of CAEPIA (1999) Rizo Aldeguer has supervised 14 doctoral theses, with many receiving the highest honors (SOBRESALIENTE CUM-LAUDE). He has participated as a researcher in over 30 competitive public research projects, serving as principal investigator in 12 of them. His educational projects include innovative teaching methods and the development of computational tools for engineering education. He has been instrumental in the design and implementation of computer science programs at both the University of Alicante and the Polytechnic University of Valencia. He is a founding member of the University Institute for Computer Research and directed the Industrial Computing and Artificial Intelligence research group from 1992 to 2004. His current research continues to focus on swarm robotics and intelligent systems, with active participation in the Valencian Graduate School and Research Network of Artificial Intelligence since 2021.

Yan Tina LuximonView profile

Professor

• Ergonomic design
• Anthropometry
• 3D digital human modeling
• +6 more

Prof. Yan Tina Luximon is a Full Professor and Associate Dean (Research) at the School of Design, The Hong Kong Polytechnic University. She chairs the School Research Committee, leads the Asian Ergonomics Design Lab, and serves as Deputy Discipline Leader for BA (Product Design). Her work bridges ergonomics, AI design tools, and 3D human modeling in cross-cultural contexts. Education: PhD in Ergonomics from The Hong Kong University of Science and Technology Research interests span Ergonomics in product design 3D digital human modeling for AI applications Anthropometry and cultural design differences Statistical modeling for head/face product development Human-computer interaction and AI visualization Recent publications focus on AI-enhanced 3D head modeling, ergonomic healthtech products, and cross-cultural design psychology, with applications in robotics, mobile technology, and medical devices. Scientific awards include: Gold Medal with jury congratulations at Geneva Inventions 2024 Silver Award at IDEA 2023 for adaptive eyewear design Best Innovation Award at ACED Japan 2017 She supervises postgraduate research and leads projects funded by the General Research Fund (RGC GRF) and Laboratory for AI in Design, including AI Powered Ergonomic Product Design (2025) and 4D Head Movement Prediction (2024).

Samuel McDermottView profile

Teaching Professor

• Machine Learning
• Medical Imaging
• Biotechnology
• +7 more

Samuel McDermott is an Associate Teaching Professor at the Department of Chemical Engineering and Biotechnology , University of Cambridge. He serves as the Sensor CDT Programme Manager , focusing on interdisciplinary research in healthcare, biotechnology, and open-source hardware. His research spans machine learning applications in medical imaging , laboratory automation , and web-of-things (WoT) integration for scientific equipment. Recent work emphasizes federated learning in healthcare, blood cell morphology classification, and low-cost diagnostic tools. Key article trends include: deep diffusion models for malaria detection , open-source microscopy platforms like OpenFlexure, and AI-driven clinical data generalization . His projects often combine 3D-printed hardware and IoT-enabled laboratory systems .