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Jiao LichengView profile

Professor

• Artificial Intelligence
• Machine Learning
• Image Understanding
• +6 more

Jiao Licheng is a Distinguished Professor and Doctoral Supervisor at Xidian University, leading the School of Artificial Intelligence and the Department of Computer Science and Technology. He holds prominent roles such as Director of the Key Laboratory of Intelligent Perception and Image Understanding (Ministry of Education) and the International Joint Research Center for Intelligent Perception and Computing. His research focuses on Artificial Intelligence, Deep Learning, Evolutionary Computation, and Remote Sensing, with significant contributions to image understanding and brain-inspired computing. Education: B.E. (1982) from Shanghai Jiao Tong University, M.E. (1984) and Ph.D. (1990) from Xi'an Jiaotong University. Postdoctoral research at Xidian University (1990–1992). Research Interests include AI, Machine Learning, Image Processing, and Big Data Analysis. His work bridges theoretical advancements and practical applications, such as medical imaging, SAR image analysis, and autonomous systems. Recent articles emphasize innovations in remote sensing, deep learning architectures, and evolutionary algorithms. Awards include IEEE Fellow, IET Fellow, and the Wu Wenjun Artificial Intelligence Outstanding Contribution Award. Labs/Teams: Key Lab of Intelligent Perception, International Joint Research Center, and leadership in national innovation bases. Active in academic societies, including editorial roles in IEEE Transactions on Cybernetics and Geoscience and Remote Sensing.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.

Zhao GuoyingView profile

Academic

• Human Behavior Understanding
• Machine Learning
• Emotion AI
• +5 more

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.

Alan BovikView profile

Professor

• Image quality
• Video compression
• Streaming video
• +7 more

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.

• Temporal aspects in NLP/IR
• Digital Humanities
• Large Language Models
• +5 more

Adam Jatowt is a Professor and Head of the Data Science group at the Department of Computer Science, University of Innsbruck. He also serves as Deputy Head of the Digital Science Center and Research Center for Digital Humanities. His academic career spans roles at Kyoto University (2010-2020), National Institute of Advanced Industrial Science and Technology (AIST), and visiting positions at Karlsruhe Institute of Technology, University of La Rochelle, and University of California Berkeley. Research interests focus on temporal aspects of NLP/IR, computational history, large language models, and future forecasting. He leads projects combining digital humanities with advanced AI techniques, including temporal validity assessment and hint generation systems. Recent publications (2025) emphasize LLM applications in temporal analysis, QA systems, and energy sector digitalization. His work has been recognized through awards like the Friedrich Wilhelm Bessel Research Award (2024) and top-cited paper distinction in Information Sciences. He actively organizes conferences like ECIR 2026 and Text2Story workshops. Key contributions include developing WikiHint dataset, PlausibleQA framework, and tools like Rankify. His research also addresses societal challenges through ESG rating prediction and medical LLM applications.

Klaus SchöffmannView profile

Associate Professor

• Video Understanding
• Deep Learning
• Computer Vision
• +4 more

Assoc. Prof. Dr. Klaus Schöffmann is an Associate Professor at the Institute of Information Technology (ITEC) at Klagenfurt University, Austria. He holds a PhD and MSc in Computer Science, and received his habilitation (venia docendi) in 2015. His research focuses on video content understanding (including medical/surgery videos), deep learning, multimedia retrieval, and interactive multimedia. He has secured over €2M in research funding and mentored 6 PhD candidates. He chairs the Video Browser Showdown (VBS) and Lifelog Search Challenge (LSC), and is a member of IEEE/ACM. He has served as program co-chair for MMM 2021, CBMI 2021, ACM ICMR 2020, and others. Affiliations: Deputy Head of the Institute of Information Technology, Chairman of the Curricular Commission for Computer Science. Grants & Impact: €2M+ funding from FWF, KWF, and industry; Google H-index 36 (4,000+ citations). Teaching: Courses in computer vision, multimedia technologies, and app development. He actively organizes conferences including general co-chair roles for ACM ICMR 2024, CBMI2025, and ACMMM2025, and contributes as a reviewer for top journals/conferences in multimedia and medical imaging.

Martin HollerView profile

Professor

• Mathematics of data science
• Machine learning
• Generative models
• +7 more

Martin Holler is a Professor at the Institute of Mathematics and Scientific Computing at the University of Graz, Austria, where he leads the research group Applied Mathematics and Machine Learning . His work bridges theoretical mathematics with practical applications in imaging and machine learning. Research Focus: His primary research areas include the mathematics of data science, variational methods in imaging, dynamic and multi-modality inverse problems, and biomedical imaging. He has made significant contributions to model-based regularization techniques, particularly with Total Generalized Variation (TGV) approaches for image and video reconstruction. Publication Trends: Over the past decade, Holler's research has evolved from traditional variational methods for image reconstruction toward increasingly sophisticated machine learning approaches. His recent work (2021-2023) focuses on integrating deep learning with variational methods, particularly for motion separation in medical imaging and learning-informed parameter identification in partial differential equations. His publications demonstrate a consistent thread of applying rigorous mathematical frameworks to solve practical problems in medical imaging and computer vision. Mathematics of data science and machine learning Generative models in machine learning Variational methods in imaging Dynamic and multi-modality inverse problems Model-based regularization Biomedical imaging Image and video decompression Technical Leadership: Holler has developed several open-source software packages implementing advanced reconstruction algorithms, particularly for multi-modal imaging problems. His GitHub repositories show active maintenance and development of these tools, which have been cited in the medical imaging community.

• Augmented Reality
• Computer Vision
• Human-Computer Interaction
• +4 more

Dr. Alexander Plopski is an Assistant Professor at the Institute of Visual Computing, Technische Universität Graz. His research focuses on advancing augmented reality (AR) technologies, human-computer interaction (HCI), and optical display systems. He holds a PhD, M.Sc., and BSc in relevant fields. His work emphasizes perceptual optimization in AR displays, eye tracking integration, and accessibility solutions for color vision deficiencies. Key research areas include gaze-contingent AR interfaces, light field manipulation for extended reality, and multimodal interaction techniques. Notable contributions include the development of the 'guitARhero' interactive AR guitar tutorial system and studies on focal distance effects in optical see-through displays. His publications span topics from AR display calibration to gesture recognition using radar sensing. He has explored applications in industrial training, medical AR, and robotic telemanipulation. His work often bridges theoretical perceptual studies with practical system implementations, aiming to enhance user experience and accessibility in AR/VR environments.

Xu DongView profile

Professor

• Artificial Intelligence
• Computer Vision
• Multimedia
• +3 more

Professor Dong Xu is a Tenured Professor in the Department of Computer Science at the University of Hong Kong (HKU), part of the School of Computing and Data Science. He holds a B.Eng. and Ph.D. from the University of Science and Technology of China (USTC). His career includes tenured roles at Nanyang Technological University and the University of Sydney, alongside postdoctoral research at Columbia University. His research focuses on Artificial Intelligence, Computer Vision, Multimedia, and Machine Learning , with applications in autonomous driving, AR/VR, medical image analysis, and video surveillance. Xu has authored over 150 papers in top journals and conferences, including CVPR, ICCV, and IEEE Transactions. He actively contributes to the academic community as an editorial board member for journals like ACM Computing Surveys and IEEE Transactions, and through leadership roles in conferences such as ACM Multimedia and ICME. Notable awards include Fellowships from IEEE and IAPR, and the IEEE Signal Processing Society Distinguished Lecturer title (2021–2022). Education: B.Eng. (USTC, 2001), Ph.D. (USTC, 2005) Professional Service: Program Coordinator of ACM Multimedia 2024, Guest Editor of over ten special issues.

Markus HaltmeierView profile

Professor

• Image Reconstruction
• Inverse Problems
• Deep Learning
• +4 more

Markus Haltmeier is a Professor in the Department of Mathematics at the University of Innsbruck. His research focuses on inverse problems, image reconstruction, and deep learning with applications in medical imaging, photoacoustics, and computational mathematics. He leads a group dedicated to advancing theoretical and practical solutions for challenges in non-destructive testing and medical diagnostics. His work integrates mathematical analysis with machine learning, addressing issues such as high-resolution imaging in scattering media and automated segmentation of cardiac structures. Key research areas include regularization techniques for inverse problems, self-supervised learning approaches for limited data scenarios, and computational methods for photoacoustic tomography. His contributions span both theoretical developments (e.g., inversion formulas for Radon transforms) and applied solutions (e.g., algorithms for cylinder liner wear assessment and myocardial infarct segmentation). Publications highlight advancements in neural network-based regularization, 3D medical image synthesis, and unsupervised learning frameworks for segmentation and registration. His research emphasizes bridging the gap between mathematical theory and real-world applications in healthcare and engineering.

Shah NawazView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Multimodal Systems
• +3 more

Shah Nawaz is an Assistant Professor at the Institute of Computational Perception , Johannes Kepler University Linz. His research focuses on multimodal systems, deep learning applications in healthcare, and cross-modal learning frameworks. He leads projects addressing challenges like missing modalities in machine learning, face-voice association, and medical image analysis. Key research interests include machine learning for medical diagnostics (e.g., breast cancer detection, skin lesion segmentation), speech recognition, and adaptive neural network architectures. He has contributed to frameworks like Chameleon for robust multimodal learning and the FAME challenge for face-voice association in multilingual environments. Publications emphasize practical applications, such as bilingual healthcare chatbots for pregnant women and light-weight speech recognition models for resource-constrained systems. His work bridges theoretical advancements and real-world deployment in healthcare and security domains. Shaw Nawaz actively participates in academic communities through workshops like DaQuaMRec@RecSys2025 and has developed open-source frameworks for image restoration and multimodal fusion. His lab focuses on scalable solutions for multimodal data challenges in both technical and clinical contexts.

Robert PeharzView profile

Assistant Professor

• Probabilistic Machine Learning
• Tractable Probabilistic Models
• Artificial Intelligence
• +6 more

Robert Peharz is an Assistant Professor at Graz University of Technology, where he leads research at the Institute of Machine Learning and Neural Computation. His work focuses on probabilistic machine learning, with particular emphasis on tractable probabilistic models, causality, and neurosymbolic AI. Education and Career PhD from TU Graz (Austria) in 2015 Postdoc at Medical University of Graz Postdoc and Marie-Curie Individual Fellow at University of Cambridge (2017-2019) Assistant Professor at Eindhoven University of Technology (2019-2021) Current: Assistant Professor at Graz University of Technology Research Interests Peharz's research spans multiple areas of artificial intelligence with a focus on making probabilistic reasoning both theoretically sound and practically efficient. His work addresses fundamental challenges in tractable probabilistic inference and learning, probabilistic circuits as a unified framework for deep generative models, Bayesian causal inference, and neurosymbolic AI combining sub-symbolic and symbolic approaches. His research has applications in cybersecurity, healthcare, and energy systems. Research Projects VENTUS (2024-present): Physics-informed, probabilistic and causal machine learning for wind energy systems NEO DNA (2023-present): DNA-based data storage systems using computer vision and probabilistic ML VanillaFlow (2023-present): AI-guided development of novel vanillin-based molecules for redox flow batteries Bilateral AI : Cluster of Excellence focused on Broad AI combining sub-symbolic and symbolic AI approaches Awards and Recognition Finalist for TUG's Excellent Teaching Award (2023) for all 3 of his courses Marie-Curie Individual Fellow at University of Cambridge Academic Service Peharz is actively involved in the academic community through conference organization and reviewing: Area Chair: UAI (2022), ECML/PKDD (2022) Senior Committee Member: UAI (2021), IJCAI (2019, 2020) Reviewer for major conferences including ICML, NeurIPS, AAAI, IJCAI-ECAI Teaching and Mentorship Peharz supervises multiple PhD students working on diverse projects at the intersection of machine learning, causality, and neurosymbolic AI. His current advisees include Sepideh Adamiat, Irina Dobrianski, Johannes Exenberger, Giacomo Di Gobbi, Tim d'Hondt, Christian Toth, and Thomas Wedenig. Previous students include Alvaro Correia, Martin Trapp, and David Montalvan.

Antonio PlazaView profile

Professor

• Hyperspectral Image Analysis
• Remote Sensing
• High-Performance Computing
• +4 more

Antonio Plaza is a Full Professor at the University of Extremadura, Spain, and Head of the Hyperspectral Computing Laboratory. With over 600 publications, he is a leading expert in hyperspectral data processing and parallel computing of remote sensing data. He serves as IEEE Fellow and has received numerous accolades, including the 2019 Excellent Teaching Award and multiple Highly Cited Researcher recognitions. Research Interests : His work bridges Hyperspectral Image Analysis , Medical Imaging , and High-Performance Computing . Recent projects focus on 3D anatomical modeling, AI-driven surgical tools, and deep learning applications for aortic dissection segmentation. Scientific Awards : 2019 Highly Cited Researcher (Geosciences) 2015 IEEE Fellow 2019 Excellent Teaching Award 2018 Highly Cited Researcher (Cross-Field) 2002 Best PhD Dissertation, University of Extremadura Editorial Leadership : Served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing (2013–2017) and held multiple committee roles in IEEE GRSS. His articles reflect a shift from remote sensing to medical imaging, with a focus on Aortic Dissection Segmentation , Skull Reconstruction , and AI-driven Medical Tools .

Ahmet TekalpView profile

Professor

• Digital Image Processing
• Video Compression
• Motion-Compensated Video Filtering
• +5 more

Ahmet Tekalp is a Professor in the Department of Electrical and Computer Engineering at Koc University's College of Engineering since 2001. He holds dual citizenship in Turkey and the USA, with prior academic roles at the University of Rochester (1986-2005) and research positions at Eastman Kodak (1984-1987) and Rensselaer Polytechnic Institute (1981-1984). He chairs the Electronics and Informatics Group at TUBITAK since 2004 as a part-time position. B.S. (1980) in Electrical Engineering & Mathematics, Bogaziçi University M.S. (1982) and Ph.D. (1984) in Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute His research focuses on digital image and video processing, including video compression, motion-compensated filtering for high-resolution applications, video segmentation, object tracking, content-based video analysis, multi-camera surveillance processing, and digital content protection. He has led numerous European and U.S. grants, including FP7 STREP projects and NSF awards, emphasizing applications in sensor networks, visual databases, and medical imaging. His scholarly work spans diverse areas such as superresolution reconstruction, head gesture animation, 3DTV streaming, and reversible data hiding. He has played pivotal roles in editorial boards, including serving as Editor-in-Chief of Signal Processing: Image Communication, and has contributed to major standards bodies like ISO MPEG and ANSI NCITS. Member, Turkish Academy of Sciences (TUBA) Fellow, IEEE Fulbright Senior Scholarship (1999) TUBITAK Science Award (2004) IEEE Signal Processing Society Distinguished Lecturer (1998) He has led multiple international research collaborations and projects, including European FP6/FP7 networks and NATO programs, with substantial grant funding from NSF, NYSTAR, and industry partners like Eastman Kodak, Xerox, and Siemens.

Michal PiovarciView profile

Researcher

• Computer Graphics
• Computational Fabrication
• Haptic Reproduction
• +3 more

Michal Piovarci is a Researcher (Postdoc) at ETH Zurich's Computational Design Lab led by Bernd Bickel. He holds a Ph.D. from USI Lugano (2020) under Piotr Didyk, receiving the Eurographics PhD Award for his thesis on Perception-Aware Computational Fabrication. His research focuses on computer graphics, computational fabrication, and haptic/appearance reproduction, emphasizing perception-driven solutions. Education: Ph.D., USI Lugano (2020) Previous postdoc at ISTA's Visual Computing Group Research interests include 3D Printing Innovations Haptic Feedback Systems Material Perception Modeling Directional Surface Design Professional Activities: Area chair at ACM Symposium on Computational Fabrication (2024), program committee roles at SIGGRAPH, SAP, and Eurographics. Organized Computational Fabrication Seminars (2021-2022). Grants: SNSF Project Funding (CHF 1M, 2025-2029) and FWF Lise Meitner Grant (2022-2023). Teaching: Taught Scientific Machine Learning for Design (ETH Zurich, 2024) Computational Fabrication (TU Wien, 2022)