Ferenc Huszár is an Associate Professor of Machine Learning at the University of Cambridge, affiliated with the Department of Computer Science and Technology. His research focuses on foundational aspects of deep learning, including optimization, generalization, representation learning, and causal reasoning. He co-founded Magic Pony Technology, where he contributed to super-resolution and compression techniques, later acquired by Twitter. Education: PhD in Bayesian Machine Learning from the University of Cambridge (supervised by Carl Rasmussen, Máté Lengyel, and Zoubin Ghahramani), followed by roles in tech/startups. Research Interests: Theoretical underpinnings of deep learning, neural network behavior analysis, LLM theory, causal inference, and AI safety. His lab explores algorithmic reasoning in neural networks and implicit Bayesian inference in LLMs. Selected Contributions: Co-authored influential papers on super-resolution (CVPR 2016) and GAN-based image enhancement (CVPR 2017). Active in advising 9 PhD students and mentoring research assistants. Grants & Collaborations: Collaborates with institutions like the Max Planck Institute and ELLIS. Supervises projects on causal representation learning, geometric deep learning, and federated learning.
Aurora Maccarone serves as an RAEng Research Fellow within the Institute of Photonics and Quantum Sciences at Heriot-Watt University's School of Engineering & Physical Sciences. Her work focuses on advanced photonics applications for challenging environments, particularly underwater and obscured conditions. Her research expertise spans: Single-photon LiDAR systems for underwater 3D imaging Photon-counting detector arrays for depth profiling Real-time reconstruction algorithms for obscurant-penetrating imaging Quantum sensing applications in marine environments Recent publications demonstrate consistent innovation in single-photon imaging techniques, with emphasis on underwater applications (2023-2024) and obscurant penetration (2022). Her work shows strong interdisciplinary connections between optical engineering, computational imaging, and environmental sensing. Key publications reveal growing impact in underwater LiDAR technology, with multiple high-citation papers on photon-efficient imaging systems. Dr. Maccarone actively supervises PhD students and has created significant research datasets. Her collaborations span international institutions, with notable contributions to sensor hardware development and computational imaging algorithms. Recent work shows increasing integration of machine learning techniques with traditional photon-counting approaches.
Zhao Guoying is an Academy Professor at the Academy of Finland and holds a tenured Full Professorship at the University of Oulu, Finland. His research focuses on human behavior understanding, emotion AI, and computer vision. He has held visiting positions at institutions including Stanford University and Aalto University. He earned his PhD (2005) in Computer Science from the Chinese Academy of Sciences. His work has led to pioneering contributions in facial expression analysis, micro-expression recognition, and remote physiological signal measurement. Zhao has secured over €19.8 million in research grants as PI, including the prestigious Academy Professor Grant (2021-2026) and Profi-7 Hybrid Intelligence funding. He has supervised 22+ PhD students and 16+ postdocs, many of whom hold academic and industry leadership roles. His awards include IEEE Fellow (2022), IAPR Fellow (2020), and Finland’s Most Publishing AI Researcher (2017). His research interests span machine learning, affective computing, and feature representation. Notable contributions include the first systems for spontaneous micro-expression analysis, novel methods for face anti-spoofing, and remote health monitoring via video. He actively organizes conferences (e.g., Arctic AI Days) and chairs committees such as the Finnish AI Society board.
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, leading the van der Schaar Lab. She holds dual affiliations with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Centre for Mathematical Imaging in Healthcare. Her research focuses on healthcare AI, machine learning, and operations research. She has authored over 250 journal articles and 275 conference papers, with notable contributions to synthetic data for privacy, causal inference, and clinical decision-making. Her work has led to 35 U.S. patents, including foundational innovations in streaming video compression (MPEG-4 standards). Awards include the Oon Prize (2018), IEEE Fellow (2009), and recognition as the UK's most-cited female AI researcher (2019). Leadership roles include Director of the Cambridge Centre for AI in Medicine and Co-Director of the European Laboratory for Learning and Intelligent Systems. She has mentored global academic leaders and pioneered initiatives like the Inspiration Exchange for early-career researchers. Key projects include predictive models for hospital resource allocation during pandemics and AI tools for personalized medicine. Publications span machine learning theory, healthcare applications, and interdisciplinary fields like network science. Her lab's impact includes tools like AutoPrognosis (automated ML for clinical prediction) and SynthCity (synthetic healthcare data generation).
Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.
Matteo Magnani is a Professor in the Division of Computing Science at the Department of Information Technology, Uppsala University. He leads the Uppsala University Information Laboratory and is a founding member of the Uppsala University Computational Social Science Lab. His research spans network science, artificial intelligence, data science, and computational social science, with a focus on social data mining and multilayer networks. PhD in Computer Science, University of Bologna, 2006 Graduated with honours in Information Sciences, University of Bologna, 2002 Studies in Computer Science at University of Marne la Vallée and Imperial College London Matteo Magnani's research interests include social network analysis, multilayer and probabilistic networks, community detection, visual analytics, and the application of AI to digital media and climate communication. His work bridges computer science and social sciences, particularly in analyzing online discourse and digital intermediaries. He has contributed significantly to the understanding of network structures, uncertainty in networks, and the ethical dimensions of algorithmic analysis. His recent publications highlight trends in fairness in community detection, visual saliency in network layouts, emotional reactions to climate visuals online, and deep learning applications in social media. Topics frequently involve YouTube, Twitter, and online public debates, using advanced network and machine learning methods. Rotary Prize for best student of the Science Faculty Best Paper Award Funniest Presentation Award Best Poster Award Pedagogical Prize from UTN Distinguished University Teacher (Sweden) Docent title (Sweden) Magnani has supervised numerous students and collaborated widely, particularly with Luca Rossi, Alexandra Segerberg, and Davide Vega. He has secured funding from major sources including VR, H2020, STINT, and MIUR. He leads active research labs focused on information systems and computational social science, fostering interdisciplinary collaboration and innovation in network-based research.
Bo Chen is a postdoctoral researcher at the Siebel School of Computing and Data Science and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. His work focuses on AI-system co-design for immersive computing, particularly in extended reality (XR) and multi-modal content delivery over wireless networks. Ph.D. in Computer Science (2022), advised by Klara Nahrstedt B.S. in Computer Science from Shanghai Jiao Tong University (2016) His research integrates AI techniques with system-level optimizations to address challenges in XR infrastructure , including multi-view video streaming , NeRF-based content delivery , and uncertainty management in video transmission . He has pioneered methods like Loose Frame Referencing for learned codecs and Context-Aware NeRF Serving for mobile XR applications. Bo Chen's recent publications span top venues like ACM MobiSys, ACM SenSys, and USENIX NSDI. Key themes include AI-driven compression , dynamic 3D rendering , and reliable streaming over mobile networks . He has received recognition including the Rising Star Best Presentation Award (ACM MobiSys 2025) and Best Student Paper Award (ACM MMSys 2022). Bayesian optimization for XR systems Multi-view video aggregation at edge networks 3D Gaussian Splatting for immersive media
Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.
Jun-Yan Zhu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, affiliated with the Robotics Institute and Computer Science Department. His research focuses on generative models, computer vision, and graphics. He holds a B.E. from Tsinghua University and a Ph.D. from UC Berkeley, with postdoctoral work at MIT CSAIL. Zhu leads the Generative Intelligence Lab, exploring human-creator collaboration with generative models. Affiliations: Robotics Institute, CMU Graphics Lab, CMU Computer Vision Group Education: B.E. (Tsinghua), Ph.D. (UC Berkeley) Research Interests: Generative AI, image/video synthesis, neural rendering, tactile sensing integration Notable contributions include CycleGAN, pix2pix, and GAN compression techniques. His work has been commercialized in Adobe's Firefly and NVIDIA's Canvas tools. Awards: ACM SIGGRAPH Dissertation Award, David J. Sakrison Prize, CVPR Best Paper Finalist Lab Members: 10+ PhD students and researchers Current projects include LEGO design synthesis, tactile-driven 3D generation, and generative model personalization.
Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.
David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)
Svetlana Lazebnik is a Full Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the Grainger College of Engineering. She holds a Ph.D. from UIUC (2006) and previously served as an Assistant Professor at the University of North Carolina at Chapel Hill (2007–2011). Her research focuses on computer vision, including generative models for virtual try-on, image stylization, scene understanding, and joint modeling of images and language. She has advised numerous Ph.D. students and postdocs, many of whom now hold prominent academic and industry roles. Education: Ph.D. in Computer Science, UIUC (2006); supervised by Jean Ponce. Research Interests: Her work spans generative adversarial networks (GANs), diffusion models, virtual try-on systems (e.g., Dressing-in-Order, Street Try-On), exemplar-based stylization, and large-scale photo analysis. She has pioneered spatial pyramid matching and contributed to binary code learning for image retrieval. Key Awards: NSF CAREER Award (2008), Microsoft Research Faculty Fellow (2009), Sloan Research Fellow (2013), IEEE Fellow (2021), and the Longuet-Higgins Prize (2016) for her CVPR 2006 paper. Teaching: Recent courses include CS 444 (Deep Learning for Computer Vision), CS 543 (Computer Vision), and a Ph.D. Job Search Seminar. She has also taught at UNC Chapel Hill. Grants & Funding: Supported by NSF, Amazon, AWS, Microsoft, Sloan Foundation, Google, ARO, and Adobe. Notable grants include CCF 2348624 and IIS 1718221. Labs/Groups: Leader in the Illinois CS Vision Group, contributing to collaborative projects on embodied AI, multi-agent systems, and visual-semantic reasoning.
Pavan Turaga is a Professor and Founding Director of The GAME School at Arizona State University (ASU), with a joint appointment in the School of Electrical, Computer and Energy Engineering (ECEE). They lead transdisciplinary research and education initiatives spanning gaming, esports, AI-enabled media creation, computer vision, and geometric modeling. Ph.D., Electrical Engineering, University of Maryland (2009) B.Tech., Electronics and Communication Engineering, IIT Guwahati (2004) Research focuses on integrating geometry and topology with machine learning , enabling advancements in: Computer vision for human activity recognition Generative AI for immersive media Health analytics and wearable rehabilitation systems AI ethics and pandemic prediction Key publications span CVPR (2023 spotlight paper PolyINR ), DLGC workshop (2023 best paper), and ICML (2019 work on GAN priors). Recent work explores LMMs , 3D human modeling , and AI for pandemic preparedness . Scientific accolades include: ASU Founders' Day Research Excellence (2025) NSF CAREER award (2015) CVPR 2023 Spotlight paper 2024 X-Prize (Rainforest Challenge) Directed research for students like Rajhans Singh and Ankita Shukla, securing grants from NSF , DARPA , and industry partners (Adobe, Google ATAP). Founded the Geometric Media Lab , emphasizing interdisciplinary collaborations with mathematicians, health scientists, and media artists.
Dr. Frederick Li is an Associate Professor in the Department of Computer Science at Durham University, UK. He holds editorial roles as Associate Editor of Frontiers in Education (Digital Education) and Editorial Board Member of Virtual Reality & Intelligent Hardware. His research focuses on Computer Graphics, Machine Learning, Geometric Modelling, Collaborative Virtual Environments, Visual Aesthetics, and Educational Technologies. He earned his B.A. (Hons) and M.Phil. from The Hong Kong Polytechnic University and his Ph.D. in Computer Graphics from City University of Hong Kong. Prior roles include Assistant Professor at HK PolyU and project manager of a Hong Kong Government ITF-funded project. **Education**: B.A. (Computing Studies) and M.Phil. from HK PolyU; Ph.D. in Computer Graphics (CityU Hong Kong). **Research Interests**: His work spans mesh saliency detection, human-object interaction recognition, cloud modeling, face beautification, and educational technology. Recent achievements include awards for papers (e.g., Best Paper at ITiCSE 2014) and recognition such as EPSRC Peer Review College membership. He leads Durham's Undergraduate Board of Examiners and has been an external examiner at Northumbria University. **Awards**: Best Paper (ACM ITiCSE 2014), Outstanding Paper (ICALT 2013), EPSRC Peer Review College (2024), Outstanding BMVC 2024 Reviewer. **Grants & Labs**: His research is supported by grants from EPSRC and others. He collaborates with the Centre for Vision and Visual Cognition, VIViD, and AIHS group at Durham.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
