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 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.
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.
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.
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.
Craig Gotsman is a Professor and Dean at the Ying Wu College of Computing, New Jersey Institute of Technology. He previously held roles at Cornell Tech, Technion, ETH Zurich, and MIT. His research focuses on computational geometry, computer graphics, and 3D animation. Ph.D. in Computer Science, Hebrew University of Jerusalem (1991) His work spans geometric modeling, mesh processing, and applications in animation and visualization. Recent research trends include gaze correction in video conferencing, mesh parameterization, and spectral compression techniques. Notable awards include Fellowships in the US National Academy of Inventors and the Academy of Europe, multiple best paper awards, and the Technion's Hewlett Packard Chair in Computer Engineering. Gotsman has mentored over 50 postgraduate students and holds ten US patents. He co-founded three companies: Virtue 3D Inc. (acquired by NVIDIA), Estimotion Inc. (now ITIS Israel Ltd.), and CatchEye.
Christian Timmerer is a Professor at the Institute of Information Technology, Alpen-Adria-Universität Klagenfurt. His research focuses on adaptive video streaming , energy efficiency , MPEG standardization , and quality of experience (QoE) , with significant contributions to HTTP Adaptive Streaming (HAS), multi-codec optimization, and immersive media systems. Email: christian.timmerer@aau.at Office Hours: Monday 3:00-4:00 PM (by appointment) Projects: CD-Labor ATHENA, GAIA, SPIRIT His research integrates machine learning and generative AI to enhance video encoding, super-resolution, and voice dubbing, while prioritizing sustainability through energy-aware algorithms and open-source tools like GREEM and VEED. Current work emphasizes latency reduction and dynamic bitrate adaptation in live streaming environments. Recent publications address VVC optimization , multi-resolution encoding , and perceptual quality modeling , reflecting interdisciplinary efforts in networking , computer vision , and human-computer interaction . Awards include leading funded projects on adaptive streaming and green video systems.
Prof. Hermann Hellwagner is a Full Professor at the Department of Information Technology, University of Klagenfurt. He has held roles such as Vice President (Natural and Technical Sciences) at the Austrian Science Fund (FWF) and Vice Dean of the Faculty of Technical Sciences. His research focuses on multimedia communication, network engineering, and future internet architectures. Notable projects include work on adaptive streaming, edge computing, and drone networks. He holds a Ph.D. in Systolic Architectures from the University of Linz (1988). Research interests span distributed multimedia systems, information-centric networking (ICN), and optimizing video streaming quality-of-experience (QoE). Recent work emphasizes edge computing solutions for low-latency streaming and dynamic codec adaptation. His contributions include frameworks like ALPHAS and MEDUSA for bitrate optimization, and studies on point cloud streaming in augmented reality. Publications (2021–2025) highlight advancements in edge-assisted streaming, hybrid P2P-CDN architectures, and transcoding techniques. His work often bridges theoretical models with real-world implementations, addressing challenges in latency, cost, and device adaptability. Current projects involve 6DoF video streaming and multi-robot system optimization. Labs/Teams: Part of the Institute of Information Technology (ITEC), Klagenfurt. Collaborates on EU-funded projects and industry partnerships in 5G edge computing and drone networks. Active in standards groups for HTTP adaptive streaming protocols.
Yun Fu is a Distinguished Professor at Northeastern University, affiliated with the College of Engineering and Khoury College of Computer Science. He holds tenure in Electrical and Computer Engineering (ECE). His roles include Professor, Senior Vice President at Shiseido Americas, founder of Giaran (acquired by Shiseido), and co-founder of TVision Insights. He earned his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on artificial intelligence, computer vision, machine learning, and data mining. Key achievements include over 500 publications, 50+ patents, and prestigious awards like IEEE Fellow, OSA Fellow, and AAIA Fellow. He leads the SMILE Lab, exploring AI applications in vision, robotics, and healthcare. Notable entrepreneurship includes AI-driven ventures in cosmetics and media analytics. Research interests emphasize AI-driven solutions for computer vision challenges, including anomaly detection, trajectory prediction, and multimodal learning. His work bridges academia and industry, with impactful contributions to both fields.
Borivoje Furht is a Professor of Computer Science and Engineering at Florida Atlantic University (FAU) in Boca Raton, Florida, a position he has held since 1992. He maintains dual affiliations with FAU and the Mathematical Institute of the Serbian Academy of Sciences and Arts. At FAU, he serves as Director of the NSF-sponsored Industry/University Cooperative Research Center for Advanced Knowledge Enablement since 2009. His academic leadership includes serving as Chair of the Department of Computer and Electrical Engineering and Computer Science (2009-2013) and Chair of the Department of Computer Science and Engineering (2002-2009). He earned his Ph.D. in electrical and computer engineering from the University of Belgrade, following early career positions at the Institute Boris Kidric-Vinca in Yugoslavia and Modcomp (a computer division of Daimler Benz). Dr. Furht's research spans multiple areas in computer science with a strong focus on multimedia technologies. His current research interests include multimedia systems, video coding and compression, cloud computing, big data, mobile multimedia, and wireless multimedia applications. Over his career, he has secured approximately $15 million in research funding from government agencies including NSF, NIH, Department of Navy, DoD, and NASA, as well as private industries such as IBM, Google, Apple, LexisNexis, Motorola, and Emerson. His scholarly contributions include founding and serving as editor-in-chief of the Journal of Multimedia Tools and Applications (Springer) and co-founding the Journal of Big Data (Springer). His work has been recognized with numerous awards including multiple FAU Researcher of the Year Awards (2019, 2013), a Distinguished Engineering Education Award from The Engineer's Council (2019), and various technical achievement awards throughout his career spanning from 1982 to 2019. Dr. Furht has also served as a consultant to various companies, colleges, and universities, leveraging his expertise in multimedia systems and computer engineering across both academic and industrial settings.
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.
Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.
Ahmet M. Tekalp is a Professor of Electrical and Computer Engineering at Koc University , Istanbul, Turkey, since 2001. Previously, he held a full-time professorship at the University of Rochester (1986-2005) and part-time roles such as Chair of the Electronics and Informatics Group at TUBITAK, Turkey. His work bridges academic and industrial research, with affiliations to institutions like Eastman Kodak Company and Rensselaer Polytechnic Institute. Education: BS (1980), Electrical Engineering & Mathematics (High Honors), Bogaziçi University MS (1982) & PhD (1984), Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute (RPI) Research Interests: Digital image and video processing Video compression and streaming Motion-compensated video filtering for high-resolution Video segmentation and object tracking Content-based video analysis and summarization Multi-camera surveillance video processing Digital content protection Scientific Awards: Member, Turkish Academy of Sciences (TUBA) Fellow, IEEE Fulbright Senior Scholarship (1999) TUBITAK Science Award (2004), Turkey's highest science award Grants and Contracts: FP6 Network of Excellence, SIMILAR (2003-2007), Euro 6 Million FP6 Network of Excellence, 3DTV (2004-2008), Euro 6 Million FP7 STREP projects (DIOMEDES, SARACEN) with Euro 3 Million budgets NSF grants spanning wireless sensor networks, visual databases, and MRI motion artifact suppression Industry partnerships with Eastman Kodak, Xerox, Siemens Corporate Research Professional Activities: Editor-in-Chief, Signal Processing: Image Communication (Elsevier) Member, ERC-Advanced Panel on Informatics Editorial roles in IEEE journals and other publications Active in ISO/IEC and ANSI standards committees
Wenwu Zhu is a Professor and Vice Chair of the Department of Computer Science and Technology at Tsinghua University. He has held prominent positions at Microsoft Research Asia, Intel Research China, and Bell Labs, establishing himself as a leading figure in multimedia computing and networking with international recognition as a FOREIGN member of the Academy of Europe (elected 2018). His educational background includes: Ph.D. in Electrical and Computer Engineering from New York University (1996) Professor Zhu's research focuses on the intersection of multimedia systems, networking, and big data. His work has pioneered advancements in internet video streaming, multimedia cloud computing, and social-aware content distribution. He has made significant contributions to understanding how multimedia content can be efficiently delivered across diverse network environments, from traditional wired networks to modern mobile and social platforms. His research bridges theoretical computer science with practical applications, with his work on social-aware video content distribution being transferred to Tencent company. His publication record shows a clear evolution from foundational work on internet video streaming in the early 2000s, through multimedia cloud computing in the early 2010s, to more recent work on social-aware multimedia and network embedding using deep learning approaches. This progression reflects the changing landscape of multimedia computing from infrastructure-focused to socially-aware and AI-driven systems. Professor Zhu has received numerous prestigious honors: AAAS Fellow (2016) SPIE Fellow (2013) IEEE Fellow (2010) Minister of Education's Natural Science Award, 1st prize (2017) Chinese Institution of Electronics's Natural Science Award, 1st prize (2015, 2012) National Natural Science Award, 2nd prize (2012) Chief Scientist for NSFC Major Project (2016) Chief Scientist for Ministry of Science and Technology's 973 Project (2014) Multiple Best Paper Awards including ACM Multimedia 2012 As Editor-in-Chief of IEEE Transactions on Multimedia since 2017 and through leadership roles as General Co-Chair for ACM CIKM 2019 and ACM Multimedia 2018, Professor Zhu has significantly shaped the multimedia research community. His research has been supported by major grants including NSFC Major Projects and Ministry of Science and Technology's 973 Projects, demonstrating both academic and national strategic importance. He has published over 300 referred papers with an H-Index of 55, including 6 Best Paper Awards and 7 books or book chapters. Professor Zhu leads a research group at Tsinghua University focused on multimedia big data computing, with strong industry connections. His team has made pioneering contributions to structural network embedding using deep learning and social contextual recommendation systems, bridging theoretical advances with practical applications in social media platforms.
Karlheinz Brandenburg is a Professor at Technische Universität Ilmenau, holding the Chair of Electronic Media Technology since 2000. He serves as Director of the Fraunhofer Institute for Digital Media Technology IDMT (2004–present) and the Institute for Media and Mobile Communications (2012–present). His work focuses on digital audio coding, perceptual measurement techniques, and psychoacoustics, revolutionizing audio compression through the development of MP3 and AAC standards. Born in Erlangen (1954) Dipl.-Ing. in Electrical Engineering (1980) and Dipl.-Math. (1982), both from Friedrich-Alexander University Erlangen-Nuremberg Ph.D. in Electrical Engineering (1989) from the same university His research spans digital media formats, wave field synthesis, and signal analysis, with applications in audio/video compression. He has received 18+ major awards, including: Honorary Ph.D. degrees from Valencia, Lüneburg, and Koblenz-Landau Induction into Internet Hall of Fame (2014) Election to acatech (2014) Fellowships from IEEE (2006) and AES (1994)
