Find Academic Supervisors

Hans-Peter SeidelView profile

Professor

• Computer Graphics
• Neural Rendering
• High Dynamic Range Imaging
• +7 more

Hans-Peter Seidel is a leading researcher in computer graphics at the Max Planck Institute for Informatics, part of the Max Planck Society. His work focuses on advancing the frontiers of image synthesis, neural rendering, and computational photography, with a strong emphasis on high dynamic range imaging, inverse rendering, and perception-aware graphics techniques. His research interests span a broad spectrum of computer graphics and vision, including neural radiance fields, Monte Carlo denoising, image deblurring, and visual perception modeling. He has made significant contributions to real-time rendering, HDR image generation, and uncertainty-aware AI for scientific applications. His lab collaborates closely with experts in rendering, perception, and machine learning, pushing the boundaries of what is possible in digital image creation and manipulation. The most recent publications reveal a strong trend toward integrating deep learning with traditional graphics pipelines, particularly through differentiable rendering, neural fields, and adversarial training. His work frequently appears in top venues such as SIGGRAPH, ACM Transactions on Graphics, and Computer Graphics Forum, reflecting sustained impact and innovation in the field. Hans-Peter Seidel has not been publicly associated with any formal scientific awards in the provided text. However, his extensive publication record and leadership at a premier research institute underscore his influential role in the academic community. While there is no explicit mention of student advising or grant funding in the provided material, his collaborative publications with junior researchers suggest active mentorship. His work is likely supported by institutional funding from the Max Planck Society, enabling long-term, high-risk research in computer graphics and AI. He is part of a vibrant research group at the Max Planck Institute for Informatics, specializing in computer graphics. The team works on cutting-edge problems in rendering, perception, and machine learning, often bridging the gap between theoretical innovation and practical applications in virtual reality, computational photography, and scientific visualization.

Themos StafylakisView profile

Associate Professor

• Speaker Recognition
• Audiovisual Speech Recognition
• Machine Learning
• +3 more

Themos Stafylakis is an Associate Professor at the Department of Informatics of Athens University of Economics and Business (AUEB), a role he assumed in 2023. He also serves as the head of the Machine Learning and Voice Biometrics departments at Omilia (Cyprus and Greece) since 2018. Additionally, he collaborates as a researcher at the Archimedes unit of the "Athena" Research Center. His academic journey includes a PhD in voice applications from the School of Electrical and Computer Engineering of NTUA (2011), a master's from Imperial College London (2005), and a bachelor's from NTUA (2004). Professional experience includes postdoctoral research at ÉTS University of Montreal (2011-2013) and CRIM research center (2011-2016), followed by work at the University of Nottingham on audiovisual speech recognition under a Marie Sklodowska-Curie fellowship (2016-2018). His research focuses on speaker recognition, audiovisual speech processing, and machine learning applications. Key contributions include advancements in speaker verification, diarization, and self-supervised learning techniques. He has contributed to datasets like KAN-AV and participated in challenges such as NIST-SRE and ASVspoof. Educations: Bachelor's: School of Electrical and Computer Engineering, NTUA (2004) Master's: Imperial College London (2005) PhD: NTUA (2011) His research interests span speaker recognition, audiovisual speech analysis, and machine learning innovations. Notable achievements include developing robust speaker verification systems and contributing to spoofing detection methodologies. He has received a Marie Sklodowska-Curie Individual Fellowship (2016-2018). His work bridges academia and industry, with applications in biometric systems, conversational AI, and multimodal data analysis. Collaborations include institutions like CRIM, ÉTS, and the Athena Research Center.

Julie WallView profile

Professor

• Artificial Intelligence
• Advanced Computing
• Deep Learning
• +12 more

Professor Julie Wall is a faculty member at the University of West London, serving as Professor of AI and Advanced Computing in the School of Computing and Engineering. She is actively engaged in research, teaching, and professional service, including her role as an expert at the British Standards Institution (BSI) in the domain of artificial intelligence. Research Interests: Julie Wall's research is centered on the design and application of intelligent systems for processing and modeling temporal data, particularly in speech and language. She leverages neural network architectures—ranging from biologically inspired models to computationally efficient deep learning systems—to analyze diverse data types such as audio, video, images, tabular data, and 3D features. Her work extends to developing production-grade deep learning and natural language understanding systems for immersive environments like virtual and augmented reality. Publications and Research Trends: Her body of work, comprising over 50 high-quality papers and multiple patents, reflects a strong trajectory in AI systems that integrate multimodal data with temporal dynamics. The research spans core areas of machine learning, natural language processing, and applied AI, with increasing focus on real-world deployment, efficiency, and intelligent interaction in extended reality platforms. Scientific Awards: US Patent UK Patent Advising and Grants: Julie Wall supervises research students across disciplines including forensic science and artificial intelligence. She contributes extensively to academic programs, teaching courses such as BSc and MSc in Computer Science, Artificial Intelligence, Data Science, and specialized AI programs in cybercrime and criminal justice. While specific grant details are not listed, her patent holdings and publication volume suggest sustained research funding and project leadership. Labs and Teams: As a leading researcher in AI and advanced computing, she is likely involved in or leads research groups focused on intelligent systems, deep learning, and multimodal AI at the University of West London, though specific lab names are not mentioned in the text.

• Signal Processing
• Passive Radar Systems
• Electromagnetic Sensors
• +3 more

María del Pilar Jarabo Amores is a Full Professor at the Department of Signal Theory and Communications, University of Alcalá. Her research focuses on passive radar systems, sensor networks, and signal processing for defense and surveillance applications. She leads the AES3 research group (Acoustic and Electromagnetic Smart Sensor networks and Signal processing) and has expertise in radar detection, clutter modeling, and target tracking. Education: PhD in Telecommunications Engineering from the University of Alcalá (2005), supervised by Dr. Francisco López Ferreras. Research Interests: Development of passive radar technologies using DVB-T/S signals, motion compensation algorithms, and AI-driven detection methods. Key application areas include drone surveillance, maritime monitoring, and urban traffic imaging. Her work emphasizes robust detection in cluttered environments and distributed sensor network architectures. Key Contributions: Pioneered motion compensation techniques for passive radar systems, designed smart antennas for improved coverage (e.g., Ku-band microwave video camera), and developed adaptive beamforming methods. Her group's IDEPAR demonstrator showcases passive radar capabilities for terrestrial traffic monitoring. Labs/Teams: Leads the AES3 group, collaborating on EU-funded projects involving SAR imagery analysis and oil spill detection. Active in developing sensor networks for critical infrastructure protection.

• Machine Learning
• Computer Vision
• Robotics
• +5 more

Roberto Javier López Sastre is an Associate Professor of Signal Theory and Communications at the University of Alcalá. He holds a Ph.D. from the same institution (2010), focusing on 'Visual vocabularies for category-level object recognition' under Dr. Saturnino Maldonado Bascón's supervision. His research spans machine learning, computer vision, robotics, and astrophysics. Key areas include Bayesian neural networks for stellar dating, semantic navigation in robotics, and AI-driven medical assistive technologies. Affiliation: Department of Signal Theory and Communications, University of Alcalá Research Group: GRAM (Multisensorial Recognition and Analysis Group) His work interfaces computational methods with real-world applications, such as assistive robotics for neurodevelopmental disorder patients and deep learning models for astrophysical parameter estimation. Recent contributions include semantic segmentation-driven navigation systems and Bayesian approaches to black hole solutions. Publications emphasize interdisciplinary applications of AI, including live video processing, continual learning frameworks, and physics-informed neural networks. Ongoing research explores the intersection of robotics, healthcare, and astrophysics through collaborative projects. López Sastre's GRAM group focuses on multisensory data analysis for recognition and navigation challenges. His technical expertise spans signal processing, probabilistic modeling, and embedded AI systems.

Toktam MahmoodiView profile

Professor

• Communications Engineering
• Networking
• Edge Computing
• +5 more

Prof. Toktam Mahmoodi is a Professor of Communications Engineering at King's College London, affiliated with the Department of Engineering under the Faculty of Natural, Mathematical & Engineering Sciences. She leads the Centre for Telecommunications Research, focusing on advanced networking, edge computing, and 5G/6G technologies. Her academic journey includes a BSc from Sharif University of Technology and a PhD from King's College London. Education: BSc in Electrical Engineering, Sharif University of Technology PhD in Telecommunications, King's College London Research Interests: Her work spans ultra-low latency networking, network virtualization, edge intelligence, and sustainable communication systems. She explores integration of cloud and mobile networks, autonomous networks, and energy-efficient solutions. Key areas include federated learning, tactile internet, and telemedicine applications. Grants & Projects: Principal Investigator for the Native Sensing in 6G Networks - Capgemini project (2023-2027). Co-Investigator in the VERGE project for AI-driven edge architectures (2023-2025). Lead in the ANIARA initiative for connected car communications (2020-2022). Pioneered projects like SoftEdge Networking and Primo-5G for immersive telepresence. Labs & Teams: Director of the Centre for Telecommunications Research, a global leader in telecommunications and cybersecurity research.

Luis Carlos Garcia Peraza HerreraView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Healthcare Applications
• +3 more

Luis Carlos Garcia Peraza Herrera is an Assistant Professor (Lecturer) in Computer Vision at King’s College London’s Department of Informatics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He leads the Visual Understanding Research Group , focusing on healthcare applications of Computer Vision and Machine Learning, particularly visual understanding with limited supervision. His work spans surgical robotics, medical image segmentation, and AI-driven diagnostic tools. Affiliations : King’s College London (current), Siemens Healthineers (2018-2019), University College London (PhD), Imperial College London & University of La Laguna (undergraduate). Research Interests : Surgical phase recognition via transformers, robotic endoscope control, hyperspectral imaging for oral health, and interpretable neural networks for cancer detection. Recent publications include Medical Image Analysis (2025), Frontiers in Robotics and AI (2022), and Gastrointestinal Endoscopy (2021). He holds a US patent and has developed open-source tools like VideoSum. Collaborative projects include the EPSRC-funded Real-time panoptic segmentation of surgical instruments for autonomous endoscopy (2021-2022). Luis’s labs focus on algorithmic solutions for healthcare challenges, with active contributions to surgical video summarization and AI-driven medical diagnostics. His work aligns with UN SDG 3 (Good Health & Well-Being).

• 6G Communications
• Software Defined Networking
• Machine Learning
• +5 more

Matthew Robinson is a Research Fellow at the University of Hertfordshire within the Department of Engineering and Technology, School of Physics, Engineering & Computer Science. His work focuses on next-generation communication systems and security applications, primarily through European and commercially funded projects including those with the European Space Agency (ESA), Horizon 2020, and industry partners like Global Invacom and Meazon. Education: MEng in Digital Communications and Electronics, University of Hertfordshire (2010-2014) Research Interests: Robinson specializes in 6G communications infrastructure with emphases on Software Defined Networking (SDN) , Machine Learning for sensor systems, Software Defined Radio (SDR) , and Satellite Communication for video broadcasting. His fingerprint analysis reveals strong activity in Connected Vehicles (80%), Software Defined Networking (80%), Machine Learning (50%), and Heterogeneous Transport Networks (40%). Projects demonstrate practical applications in energy disaggregation, vehicle sensor reliability, and secure smart metering. Research Trends: Recent publications (2021-2025) show a strategic pivot from video broadcasting systems toward cybersecurity applications for connected infrastructure. His work increasingly integrates unsupervised machine learning with hardware-software co-design, particularly for real-time threat detection in smart grids and autonomous vehicles, while maintaining satellite communication expertise. Awards: GB Patent GB2612705A: "Improvements to video data distribution networks" Projects & Grants: Robinson has contributed to four major initiatives: (1) SAT>IP WiFi Live Mobile TV (ESA-funded, with BBC R&D/Global Invacom), (2) EDIoT (H2020-funded, with Meazon), (3) AutoTrust (ESA-funded, with RL Automotive/Surrey), and (4) CPU/GPU Satellite Modem (ESA-funded). These projects secured substantial European funding and yielded patents, conference papers, and deployable systems for video broadcasting, energy monitoring, and vehicle sensor networks. Laboratory Affiliation: Works within the Networks and Security Research Centre, leveraging facilities for Software Defined Radio experimentation, satellite communication testing, and machine learning validation for IoT systems.

Michel Beaudouin-LafonView profile

Professor

• Human-Computer Interaction
• Interactive Systems
• Collaborative Computing
• +7 more

Michel Beaudouin-Lafon is a Professor of Computer Science at Université Paris-Saclay, affiliated with LISN (Laboratoire Interdisciplinaire des Sciences du Numériques), a joint lab between Université Paris-Saclay, CNRS, and Inria. He is also a member of the French Academy of Sciences, a senior fellow of the Institut Universitaire de France, and an ACM Fellow. He currently serves as deputy director of LISN and chairs the ACM Europe Technology Policy Committee. He previously directed LRI, the former computer science laboratory of Université Paris-Sud. His research interests are centered on human-computer interaction, with a focus on instrumental interaction , generative theories of interaction , interaction substrates , and collaborative computing . He has pioneered novel interaction techniques involving gesture, sound, and multi-surface environments, notably through the WILD project. His theoretical work aims to unify principles of interaction beyond traditional application boundaries. The recent publications of Michel Beaudouin-Lafon reflect a consistent focus on foundational aspects of interaction, including interaction histories, cross-document workflows, and the design of substrates that support flexible and powerful user tools. His work bridges theoretical modeling with practical system design, often exploring how users reason with and through digital tools. Themes of collaboration, consistency, and cognitive support run through his recent research. His scientific honors include: CNRS Silver Medal ACM Fellow (2022) ACM SIGCHI Lifetime Service Award ERC Advanced Grant and Proof-of-Concept Grant Senior Fellow, Institut Universitaire de France Member, French Academy of Sciences Michel Beaudouin-Lafon has advised over 35 PhD students, including Ignacio Avellino, Germán Leiva, and Wanyu (Abby) Liu. He has led major research infrastructure projects such as Digiscope and CONTINUUM , and is a co-director of the 38M€ eNSEMBLE project on the future of digital collaboration. He founded the Francophone HCI association AFIHM and has held numerous leadership roles in ACM and SIGCHI, including Technical Program Co-chair of CHI 2013 and editorial board membership for ACM TOCHI and ACM Books. His research is conducted within the Ex-Situ group, a joint research team between LISN, CNRS, and Inria, which focuses on situated and instrumental interaction. The group explores how users interact with complex systems across multiple devices and contexts, emphasizing continuity, traceability, and cognitive support.

• High Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +12 more

Anne C. Elster is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She is the founder and director of the HPC-Lab, a leading research group in heterogeneous and parallel computing. She also maintains a long-standing affiliation with the Oden Institute at the University of Texas at Austin as a Senior Visiting Scientist until Summer 2025. Research Interests: Her work spans high-performance computing (HPC), GPU computing, parallel algorithms, auto-tuning, performance optimization, and machine learning applications in scientific computing. She leads research in heterogeneous architectures and has contributed significantly to compiler and runtime systems for GPUs and accelerators. Publications Trends: Her recent publications (2021–2024) focus on GPU acceleration, auto-tuning frameworks (e.g., BAT, LS-CAT), performance modeling (Roofline), machine learning integration in HPC, and applications in geophysical and scientific computing. There is a strong emphasis on empirical evaluation, benchmarking, and practical optimization techniques. Scientific Awards and Recognition: IEEE Senior Member (2000) IEEE Computer Society Distinguished Contributor Charter member, NTNU's Board (2021) Distinguished Speaker, IEEE Computer Society (2019–2022) Advising and Grants: She has advised over 100 master’s students and several PhD students. She has led major funded projects including the RCN SFI Centre for Geophysical Forecasting, EU H2020 CloudLightning and TICOH, and NFR FRINATEK on Computational Microscopy. She has served on numerous international program committees and evaluation boards. Labs and Teams: She leads the HPC-Lab at NTNU, which includes postdocs, PhDs, and master’s students, and collaborates with international researchers. The lab is a hub for innovation in GPU computing, auto-tuning, and HPC applications.

• Image Compression
• Cryptography
• Privacy-Preserving Systems
• +5 more

Ja-Ling Wu is a distinguished Professor in the Department of Electrical Engineering at National Taiwan University's College of Electrical Engineering and Computer Science. With an extensive publication record spanning over four decades (1984-2025), Professor Wu has established himself as a leading researcher in multimedia systems, image processing, and security technologies. His academic career demonstrates consistent productivity with 255 indexed publications showing no signs of slowing down, with numerous papers published in 2024 and 2025. Professor Wu's research interests center on image compression, cryptography, privacy-preserving systems, and machine learning applications. His work bridges theoretical foundations with practical implementations, particularly in multimedia security and efficient data representation. Over his career, he has developed innovative approaches to image and video coding, data hiding techniques, and secure computation methods that have influenced both academic research and industry applications. Analysis of his recent publications (2023-2025) reveals a strategic evolution of his research toward contemporary challenges in AI security, with increasing focus on learned image compression, privacy-preserving machine learning, and the integration of cryptographic techniques with deep learning systems. His work demonstrates a consistent ability to adapt to emerging technological landscapes while maintaining core expertise in multimedia processing. Professor Wu has mentored numerous students who have become active researchers in their own right, with many continuing to collaborate with him on cutting-edge projects. His leadership in the field is evident through his sustained publication output across top venues including IEEE Transactions, ACM Multimedia, and specialized cryptography conferences.

Thomas BonseView profile

Professor

• Media Engineering
• Video Signal Processing
• High Dynamic Range (HDR) Imaging
• +3 more

Prof. Dr.-Ing. Thomas Bonse is a Professor in the field of Bildtechnik (Image Technology) at the Hochschule Düsseldorf (University of Applied Sciences Düsseldorf), Faculty of Media since 2000. His career includes roles as project leader at ITK Telekommunikation AG , group leader at the Forschungsinstitut für Telekommunikation , and R&D engineer at Grass Valley and Panasonic in the domain of advanced TV systems. Education: Diplom-Ingenieur (Dipl.-Ing.) and Doctorate (Dr.-Ing.) in Electrical Engineering from the University of Dortmund. Current Role: Teaching and research in image technology, supervising 15+ theses annually, and leading projects in HDR, broadcast workflows, and real-time video systems. His research focuses on media engineering , particularly in high dynamic range (HDR) imaging , video signal processing , and real-time broadcast systems . Key areas include: Quantisierung von Videoformaten ACES color workflow optimization Wireless video transmission AI-driven object detection IP-based broadcast infrastructures Photovoltaic soiling measurement via drones Recent research trends show emphasis on IP-based video transmission (2016-2023), HDR/SDR workflows (2021-2023), and automated studio systems (2018-2023). He has collaborated with institutions like the Institut für Rundfunktechnik (IRT) , Sportcast GmbH , and Ericsson . As a supervisor, he has guided students in: Automated camera control (Scheunert, 2020) Photovoltaic soiling detection (Mikulaschek/Scheunert, 2020) Sports broadcasting analytics (Mikulaschek, 2022) LED studio lighting systems (2023) Contact: thomas.bonse@hs-duesseldorf.de | Room 04.2.006, HSD Campus

Dan SchonfeldView profile

Professor

• Signal Processing
• Image Processing
• Video Processing
• +7 more

Dan Schonfeld is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago . His research spans signal, image, and video processing, with interdisciplinary applications in genomic signal processing and multimedia systems. Education: Ph.D. and M.Sc. in Electrical and Computer Engineering from The Johns Hopkins University (1990, 1988), B.Sc. in Electrical Engineering and Computer Science from UC Berkeley (1986). Research Interests: Schonfeld's work focuses on video communications, retrieval, and networks, integrating computer vision, pattern recognition, and stochastic optimization. His contributions include mathematical morphology for image processing and statistical methods for real-time scene change detection. Article Trends: His recent publications emphasize particle filtering for video tracking, hidden Markov models for activity recognition, and multi-camera systems for pose estimation. Applications in genomic signal processing and crowded scene tracking highlight his interdisciplinary impact. Scientific Leadership: He has been a Senior Member of IEEE since 2005 and received multiple Best Student Paper Awards at IEEE ICIP (2006, 2007) and SPIE VCIP (2006). Editorial Contributions: Schonfeld has served as Guest Editor for IEEE journals on video and genomic signal processing and as Associate Editor for key IEEE Transactions since the 1990s.

Örjan SandredView profile

Professor

• Computer-Assisted Composition
• Electro-Acoustic Music
• Rule-Based Composition
• +3 more

Örjan Sandred is a Professor of Composition at the University of Manitoba's Desautels Faculty of Music, where he founded Studio FLAT for computer music research and production. He teaches composition, computer-assisted composition, and electro-acoustic music while maintaining an active international composition career. Diplomi i Komposition, Royal College of Music Stockholm MFA, Royal College of Music Stockholm Additional studies at McGill University and IRCAM (Paris) His research focuses on Computer-Assisted Composition with emphasis on rule-based systems, live electronics integration, and artificial intelligence applications. Sandred developed the Cluster Engine constraint-solving system for music composition, exploring how computers can process musical ideas through formalized rules. His work bridges acoustic instruments with digital technologies, creating innovative performance systems where musicians interact with responsive computer environments. His recent compositions demonstrate evolving trends in interactive performance systems (Tilting Points, Sketches of Shifting Landscapes), climate change expression (Footprints), and pandemic-era multimedia installations (Sonic Trails: Lockdown). These works consistently integrate live electronics with acoustic instruments through sophisticated Max/MSP patches. Scientific awards include: Guggenheim Fellowship (2022) Fellow of the Royal Society of Canada (2023) DAAD Visiting Professorship at Hochschule für Musik Detmold (2016) Manitoba Arts Council support for major commissions Sandred maintains significant international collaborations with performers and institutions worldwide. His compositions receive regular premieres across Europe, North America, and Asia through commissions from organizations like the Swedish Concert Institute and Théâtre du Châtelet in Paris. He directs Studio FLAT as a hub for computer music research while developing educational resources including his book The Musical Fundamentals of Computer Assisted Composition . Current projects include an installation collaboration with the Royal Winnipeg Ballet and conductors from Sweden/Canada, plus ongoing development of the Cluster Engine system within Max environment through MOZlib.

Jim BlakleyView profile

Researcher

• Edge Computing
• Cloud Computing
• Artificial Intelligence
• +5 more

Jim Blakley is the Associate Director of the Living Edge Lab at Carnegie Mellon University, bringing over 30 years of industry experience from AT&T, Lucent, and Intel. His work focuses on commercializing emerging technologies at the intersection of cloud systems, AI, networking, and human-computer interaction. He leads research initiatives in edge computing infrastructure and real-world technology deployment. Blakley's research spans edge computing , distributed cloud systems , drone autonomy , and network optimization . He specializes in developing scalable solutions for decentralized infrastructure, latency-sensitive applications, and edge-native platforms, with an emphasis on practical implementation and systems engineering. His recent publications demonstrate a strong focus on edge computing advancements, including drone video streaming optimization, lightweight AI frameworks for wearables, decentralized cloud architectures, and cross-tier orchestration. This body of work highlights trends toward democratizing edge technologies and enhancing real-time processing capabilities for next-generation applications. At the Living Edge Lab, Blakley oversees projects advancing edge computing infrastructure and experimental testbeds, collaborating with industry partners to bridge academic research and commercial deployment.