Univ.-Prof. Torsten Möller, PhD is a Professor at the University of Vienna and serves as Head of the Research Group Visualization and Data Analysis and Head of the Research Network Data Science. His work spans data visualization, visual analytics, and human-computer interaction, with a focus on biomedical, environmental, and societal data applications. Academic rank: Professor Research group: Visualization and Data Analysis Network: Data Science Email: torsten.moeller@univie.ac.at Research interests include: Visual data analysis for complex systems Interdisciplinary applications in climate science and medicine Human-computer interaction in data exploration Image processing and computer graphics Recent publication trends show expertise in: Visualizing climate change and pandemic data Multi-volumetric and network analysis Algorithmic transparency and user-centered design Interdisciplinary collaborations (e.g., astrophysics, medical imaging) Statistical and uncertainty visualization Design frameworks for visualization recommendation Teaching includes courses in: Computer graphics and visualization Image processing and analysis Human-computer interaction Data analysis projects Doctoral research seminars
Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.
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 John Eu-Li Wong serves as Senior Vice President (Health Innovation & Translation) at the National University of Singapore (NUS) and Senior Advisor to the National University Health System (NUHS). Previously, he held pivotal roles including Dean of NUS's Yong Loo Lin School of Medicine (2003-2011), Senior Vice President (Health Affairs), and Director of the National University Cancer Institute. His leadership has been instrumental in developing Singapore's Health and Biomedical Sciences initiative through strategic international partnerships. His educational background includes an MBBS from NUS, residency and fellowship at New York Hospital-Cornell Medical Center (where he served as Chief Resident in Medicine), and specialized training at Memorial Sloan-Kettering Cancer Center. He maintains active clinical expertise as a medical oncologist-haematologist. Professor Wong's research spans comprehensive cancer centers , healthy longevity initiatives , and value-based healthcare systems . His work focuses on translating biomedical research into national health strategies, with particular emphasis on global health security , academic health system integration , and cancer clinical trials infrastructure . He champions multinational collaborations to address transnational health challenges through frameworks like the Cancer Therapeutics Research Group. His scholarly output demonstrates consistent leadership in global health policy and translational medicine , with recent publications addressing pandemic response systems (2020), genomic medicine implementation (2015), and sustainable healthcare models (2021-2022). The work reveals a strategic shift toward healthy longevity roadmaps and value-based healthcare ecosystems while maintaining foundational focus on cancer therapeutics. International Member, US National Academy of Medicine (2019) Doctor Philosophiae Honoris Causa, Hebrew University of Jerusalem (2019) President’s Science & Technology Medal, Singapore (2014) Public Administration Medal (Gold), Singapore National Day Awards (2016) Fellow of Royal Colleges of Physicians (London, Edinburgh) and American College of Physicians Professor Wong co-founded the multinational Cancer Therapeutics Research Group (nine institutions) and serves on editorial boards for JAMA and American Journal of Medicine. His grant leadership includes directing Singapore's National University Cancer Institute and spearheading the Global Genomic Medicine Collaborative. Current initiatives focus on the National Academy of Medicine's Healthy Longevity Roadmap and Qatar's Academic Medicine Advisory Board. He established the Cancer Therapeutics Research Group and maintains strategic partnerships with Karolinska Institute, University of Cambridge, Harvard, and Stanford. His leadership extends to the M8 Alliance of Academic Health Centers (past World Health Summit President) and Global Genomic Medicine Collaborative Steering Committee, driving transnational research infrastructure.
Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.
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.
Prof. Pierre Jaïs serves as University Professor in Cardiology and Cardiac Electrophysiology at the University of Bordeaux and Head of the Electrophysiology Unit at Bordeaux University Hospital. He concurrently leads the Electrophysiology and Heart Modeling Institute (LIRYC) as CEO since 2021, driving innovation in cardiac rhythm disorder treatments through multidisciplinary research. His research revolutionized cardiac electrophysiology by identifying pulmonary veins as primary sources of atrial fibrillation, establishing pulmonary vein isolation as the global treatment standard. Current work focuses on pulsed field ablation—a non-thermal technique with potential to replace conventional ablation—and developing advanced imaging for precise arrhythmia targeting, reflecting his commitment to translating scientific discovery into clinical solutions. Analysis of recent publications (2017-2021) reveals dominant trends in pulsed field ablation optimization, comparative ablation techniques, and AI integration in cardiovascular imaging. These works consistently address atrial fibrillation treatment efficacy, safety profiles, and technological innovation, positioning him at the forefront of electrophysiology advancement. His distinguished contributions are recognized through prestigious awards including: 2019: Eli S. Gang Most Innovative Abstract Award (Heart Rhythm Society) 2018: Eric N. Prystowsky Lectureship Award 2012: Academy of Medicine Membership (Paris) 2009: Circulation Best Paper Award Multiple National Academy of Medicine honors Prof. Jaïs actively mentors electrophysiology trainees and secures substantial research funding, notably leading an EU-funded randomized trial comparing pulsed field versus thermal ablation. His LIRYC institute integrates cardiology, engineering, and computational expertise to accelerate therapeutic innovation. The LIRYC institute operates as a collaborative hub where clinicians, biomedical engineers, and data scientists develop next-generation electrophysiology tools. Current projects include real-time arrhythmia mapping systems, tissue-selective ablation protocols, and AI-driven predictive models for treatment personalization, fostering seamless translation from bench to bedside.
Professor Celso Grebogi, Sixth Century Chair in Nonlinear & Complex Systems at the University of Aberdeen, is a globally recognized leader in nonlinear dynamics , chaos theory , and systems biology . He founded the Institute for Complex Systems and Mathematical Biology and co-founded the Aberdeen-Lanzhou-Tempe Research Centre. His career spans institutions including University of Maryland, University of São Paulo, and Max-Planck-Society (External Scientific Member since 1998).
Sabine Glasl-Tazreiter is a Lecturer at the University of Vienna's Faculty of Life Sciences , specifically within the Department of Pharmaceutical Sciences and its Division of Pharmacognosy . Her office is located in room 2E 412 on the 4th floor at Josef-Holaubek-Platz 2, Vienna, Austria (1090). Contact details include telephone number +43-1-4277-55207 and email sabine.glasl@univie.ac.at . Principal research focus: Phytochemistry & Biodiscovery Specialization: Secondary metabolites from ethnomedicinally used plants across Europe, Mongolia, and Latin America Key techniques: Isolation of bioactive compounds, structural elucidation, pharmacological evaluation Quality control expertise: Macroscopic/microscopic identification, chemical analytics Recent publications highlight her work in: 2024 - Development of the VOLKSMED Database for Austrian folk medicine wound healing plants 2025 - Advanced mucociliary clearance research in respiratory systems 2023 - Innovations in optoacoustic imaging technology 2019 - Structure-function analysis of phycobiliproteins for medical imaging 2017 - Phytochemical characterization of Latin American antidiabetic plants
Ljubisa Stankovic is a Full Professor at the University of Montenegro with extensive academic and political experience. He has served as Rector of the University of Montenegro (2003-2008), Member of the National Academy of Sciences and Arts (CANU) since 1996, and Ambassador of Montenegro to the United Kingdom since 2010. As an IEEE Fellow (2012), he has made significant contributions to signal processing research. His research focuses on Signal Processing , particularly Time-Frequency Analysis , Data Processing in Joint Time and Frequency Domain , Analysis of Non-Stationary Signals , and Radar Signal Processing . With about 300 technical papers published (83 in leading international journals, mainly IEEE editions) and several textbooks in Signal Processing, his work has substantially influenced the field. The analysis of his recent publications reveals a consistent focus on advanced time-frequency methods applied to radar systems, non-stationary signal analysis, and emerging applications in machine learning and quantum processing. His research shows evolution from theoretical foundations toward practical implementations in communications, radar, and biomedical applications. His notable scientific achievements include: Member of the National Academy of Sciences and Arts (1996) Highest State award of Montenegro '13. jul' (1997) Fellow of the IEEE (2012) Fulbright fellowship (1984-1985) Alexander von Humboldt fellowship (1997) Volkswagen award grant (2001) Scientific Achievement Award by Montenegrin Academy of Science and Art (1991) Stankovic has held significant editorial positions including Associate Editor for IEEE Transactions on Image Processing, IEEE Signal Processing Letters, and IEEE Transactions on Signal Processing since 2003. He was also a member of the IEEE Signal Processing Society's Technical Committee on Theory and Methods (2002-2008). His research group received a Volkswagen Foundation research grant (2001-2003), demonstrating his ability to secure competitive funding. Beyond academia, he has held prominent political positions including Vice-president of Montenegro (1989-1991) and Member of Yugoslav Parliament (1992-1996).
Elisa Riedo is a tenured Professor of Chemical and Biomolecular Engineering at New York University (NYU) Tandon School of Engineering, with joint appointments as Professor of Physics in NYU’s College of Arts and Science and as affiliated Professor of Mechanical Engineering at Tandon. She serves as Director of Faculty Development at NYU Tandon and has held prior tenured positions at Georgia Tech (2003–2015) and CUNY ASRC (2015–2018). Her academic career spans over two decades, with a Ph.D. in Physics from the University of Milano (2000) and postdoctoral work at EPFL. Her research focuses on nanotechnology , graphene and 2D materials , and thermal scanning probe lithography (tSPL) , with applications in biomedical diagnostics quantum electronics electromagnetic interference shielding mechanical reinforcement of materials She pioneered tSPL for sustainable nanofabrication and discovered diamene—a single-layer diamond structure from graphene under pressure. Her recent work involves transparent infrared electrodes using silver nanowires (2025) and self-organized graphene stacking domains for quantum technologies (2024). She has secured major grants from National Science Foundation , Department of Defense , and Army Research Office . Scientific honors include: 2023 NYU Tandon Excellence in Research Award 2013 American Physical Society Fellow 2005 CREA Innovation Award Membership in the Academy of Europe (2023) She contributes to editorial boards for journals like 2D Materials and Applications and advises companies such as Mirimus Inc. and SwissLitho AG .
Professor Robert Eason is a leading academic at the University of Southampton, specializing in photonics and laser technology. His research spans interdisciplinary areas combining Machine Learning , Medical Diagnostics , and Microfluidics . Research Interests : Eason focuses on AI-driven laser applications, including deep learning for phototherapy , autonomous laser machining , and low-cost paper-based diagnostic devices . His work bridges photonics with biomedicine and advanced manufacturing. Recent Publications : His 2025 article in Scientific Reports explores AI simulations for psoriasis treatment, while 2024-2022 works address laser-controlled microfluidics, deep learning in microscopy, and reinforcement learning for laser machining. Supervision : He supervises PhD student Georgia Mourkioti in laser-based research projects. External Roles : Eason has served as a speaker at international conferences including the International Symposium on Laser Precision Microfabrication (2018), LAISER (2019), and Deep Learning for Control of Light-Matter Interactions (2022).
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.
Ivan Viola is an Associate Professor at the Institute of Computer Graphics and Algorithms, part of the Faculty of Informatics at TU Wien, Austria. He holds a leave of absence until December 2024 while also being affiliated with King Abdullah University of Science and Technology (KAUST) as an Associate Professor funded by the Vienna Research Groups program. His research focuses on visualization techniques in medicine, biological sciences, and earth sciences, with a specialty in illustrative visualization and DNA-nanotechnology applications. Viola has contributed over 100 scientific works and serves as a reviewer and panelist for major conferences in computer graphics and visualization. Education: M.Sc. (2002) and Ph.D. (2005) in Computer Graphics from TU Wien. Postdoctoral research at the University of Bergen (2006-2011), where he became Full Professor before returning to TU Wien. Research Interests: Whole-cell visualization Molecular modeling Interactive 3D environments Biomedical visualization Data-driven colormap techniques Awards: IEEE VIS 2017 Best Paper Honorable Mention, 'Best Overall Concept' for CellView, and multiple visualization awards. Active in EuroVis and IEEE VIS organizing roles. Grants & Supervision: Leads the Visualization Group at TU Wien, supervising student projects and master’s theses. Involved in grants like the Vienna Research Groups program. Labs/Teams: Visualization Group at TU Wien, collaborating on projects like CellView and Molecumentary.
Georg Langs is a Full Professor of Machine Learning in Medical Imaging at the Medical University of Vienna and Founding Director of the Computational Imaging Research Lab (CIR). He leads a 25-member interdisciplinary team focusing on machine learning methodologies for medical image analysis. Key roles include Director of the Joint Initiative on AI in Medical Imaging (European Institute of Biomedical Imaging Research) and Scientific Lead of the Respiratory Disease Phenotype Observatory (ZODIAC, UNO/IAEA). He is affiliated with MIT’s CSAIL and serves on advisory boards for global AI initiatives. Education: PhD in Computer Science, Graz University of Technology (2007) M.Sc. in Mathematics, Vienna University of Technology (2003) Research Interests: Machine learning-driven precision imaging, neuroimaging, clinical data phenotyping, and cross-species brain connectivity analysis. His work bridges imaging biomarkers with biological mechanisms and large-scale clinical data integration. Grants & Funding: Over €6M in competitive grants as Principal Investigator in the last two years. Projects include ARTEMIS (fatty liver disease digital twins) and AI-POD (personalized risk scores via imaging). Awards: 2022 IS3R Emerging Leaders Club 2022 National Academy of Medicine Emerging Leader Programme 2018 Advisor, AI Mission Austria 2030 Lab & Teams: CIR Lab focuses on AI-driven medical imaging solutions. Co-founded contextflow GmbH , a MedUni spin-off developing AI software for imaging analysis.
