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Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.

• Data Visualization
• Human-Computer Interaction
• Big Data Analytics
• +13 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
• Image Understanding
• +6 more

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.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +6 more

Uwe Zdun is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice-Director of Studies for Computer Science and Head of the Research Group Software Architecture. His teaching portfolio includes core courses such as Software Engineering 2, Advanced Software Engineering, and Practical Software Courses for Bachelor's and Master's theses across multiple semesters (2024W-2025S). His research spans software architecture with emphasis on microservices, cloud computing, and DevOps. Key focus areas include architectural design decisions, infrastructure-as-code conformance, security in distributed systems, and the integration of machine learning operations (MLOps/RLOps). He investigates cognitive aspects of architecture practices through controlled experiments and develops model-driven approaches for quality assessment in complex systems. Recent publications (2024-2026) reveal three dominant trends: (1) Security and coupling analysis in infrastructure-as-code deployments, (2) MLOps/RLOps integration for Industry 4.0 cyber-physical systems, and (3) Performance optimization patterns for CI/CD pipelines and autoscaling. His work bridges theoretical architecture models with industrial practice, particularly in microservice ecosystems and reinforcement learning applications. Professor Zdun leads the Research Group Software Architecture at the University of Vienna's Faculty of Computer Science. The group focuses on empirical validation of architectural patterns, tool development for conformance checking, and advancing design decision methodologies in cloud-native and AI-driven systems.

• Software Architecture
• Microservices
• Cloud Computing
• +7 more

Rainer Schuhmacher is a Full Professor for Plant and Microbe Metabolomics at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the Department of Agricultural Sciences and the Institute of Bioanalytics and Agro-Metabolomics in Tulln an der Donau. His research focuses on metabolomics, analytical chemistry, and plant-microbe interactions, with emphasis on fungal secondary metabolites, mycotoxin resistance, and environmental stress responses in crops. He leads projects funded by the Austrian Science Fund (FWF), European Commission, and national agencies. Research interests span: Metabolomic profiling of plant-pathogen interactions (e.g., Fusarium -wheat systems) Stable isotope-assisted techniques for metabolic pathway analysis Cold stress mitigation in plants using Antarctic bacteria Development of computational tools for untargeted metabolomics data processing His work integrates analytical chemistry, bioinformatics, and molecular biology to study metabolic crosstalk in agricultural systems. Awards include the Fritz-Feigl-Preis (2012) and Dr.-Wolfgang-Houska-Preis (2005). He has supervised multiple PhD projects and leads the Plant-Microbe Metabolomics group, managing core metabolomics platforms and international collaborations. Key projects: Chemical crosstalk in mycoparasitic interactions (FWF) Metabolomics of cold stress tolerance mediated by psychrotolerant bacteria Extension of metabolomics platforms for plant-pathogen studies

• Metabolomics
• Analytical Chemistry
• Plant-Microbe Interaction
• +2 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.

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

Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing. He has been with NUS since 1991, progressing through the ranks from Lecturer to his current distinguished position. He previously served as Dean of the School of Computing from 2007 to 2013 and as Director of the Smart Systems Institute from 2011 to 2021. His educational background includes: 1985: B.Sc. (1st Class Honors) from Monash University, Melbourne, Australia 1989: Ph.D. in Computer Science from Monash University, Melbourne, Australia Beng Chin Ooi's research focuses on database systems, large scale analytics, and distributed systems. His work has been instrumental in advancing the field of data management technology, particularly in the context of "big data" in large-scale parallel and distributed systems. He has made significant contributions to spatio-temporal and distributed data management, as well as pioneering research in distributed database management and peer-to-peer based enterprise quality management. His recent publications demonstrate a strong focus on blockchain technology, machine learning systems, and healthcare informatics. There's a clear progression from foundational database research to applications in emerging technologies like blockchain and AI. His work bridges theoretical advances with practical system implementations, as evidenced by multiple open-source projects associated with his publications. His notable awards include: 2021: NUS Research Recognition Award 2020: ACM SIGMOD E.F. Codd Innovations Award 2020: ACM SIGMOD Research Highlight Award 2019: VLDB Best Paper Award 2016: Fellow of Singapore National Academy of Science 2016: China Computer Federation Overseas Outstanding Contributions Award 2014: VLDB Best Paper Award 2014: IEEE TCDE CSEE Impact Award 2013: Singapore National Day's Public Administration Medal (Silver) 2013: NUS Outstanding Researcher Award 2012: IEEE Computer Society Kanai Award 2011: ACM Fellow 2011: Singapore President's Science Award 2009: IEEE Fellow 2009: ACM SIGMOD Contributions Award Throughout his career, Professor Ooi has demonstrated exceptional leadership in the database community, promoting high standards of database research at both international and regional levels. His BLOCKBENCH framework became the world's first benchmarking tool for private blockchains, and his work on data provenance on blockchain systems earned both the VLDB Best Paper Award and the ACM Research Highlight Award. He has led several major research initiatives, including the Smart Systems Institute at NUS. Professor Ooi has established multiple open-source projects including FabricSharp for blockchain data provenance and Cool for cohort online analytical processing. His research group has consistently produced high-impact work that bridges theoretical advances with practical system implementations.

• Database systems
• Large scale analytics
• Distributed systems

Prof. Elmar Rueckert is the Chair of the Cyber-Physical-Systems Institute at Montanuniversität Leoben in Austria since March 2021. He holds a PhD in Computer Science from TU Graz (2014) and previously served as a Senior Researcher at TU Darmstadt (2014–2018) and Assistant Professor at the University of Lübeck (2018–2021). His research focuses on Cyber-Physical Systems, Robotics, Machine Learning, and Human Motor Control, with applications in industrial automation, healthcare robotics, and environmental sustainability. Key research interests include stochastic machine/deep learning, reinforcement learning, brain-computer interfaces, and tactile learning. His work bridges robotics, neuroscience, and AI, emphasizing practical applications such as autonomous navigation, exoskeleton control, and sensor-based recycling technologies. Rueckert has led projects like the AI Robot Lab funded by Robert Bosch Stiftung and the KIRAMET recycling initiative. Recent publications highlight advancements in neural networks for industrial condition monitoring, multimodal robotic learning, and environmental infrastructure modeling. Awards include the German Young Researcher Award (2019) and the Advanced Robotics Best Paper Award (2018). Rueckert advises a team of researchers, including notable students like Daniel Tanneberg (PhD graduate, 2019) and Linus Nwankwo (best student paper winner, 2023). His lab develops open-source tools like ROMR and collaborates on datasets like EnvoDat for robotic spatial reasoning.

• Cyber-Physical Systems
• Robotics
• Machine Learning
• +9 more

Zahra Dabiri is a Postdoctoral Researcher at the Department of Geoinformatics (Z_GIS), part of the Faculty of Digital and Analytical Sciences at the University of Salzburg , Austria. With a PhD in geospatial technologies, she specializes in Earth Observation (EO) applications for natural hazard analysis. Current research focuses on glacier retreat impacts on alpine infrastructure, rock glacier dynamics using InSAR, and landslide-dammed lake monitoring through satellite time series. Her expertise spans SAR polarimetry , hyperspectral imaging , and machine learning for geospatial analysis. Recent publications highlight her contributions to: Flood susceptibility mapping using AHP and EO data Automated rock glacier delineation with deep learning Landslide volume estimation via multi-temporal DEMs Heatwave impact analysis on water temperatures Scientific recognition includes the 1st runner-up of Copernicus Price Austria (2020) . She actively contributes to the Copernicus Master in Digital Earth program and collaborates with institutions across Europe, Iceland, and New Zealand.

• Remote Sensing
• Digital Image Processing
• Spatio-temporal Data Analysis
• +7 more

Dr. Martin Loidl is a researcher in Geoinformatics at the University of Salzburg's Department of Geoinformatics - Z_GIS, part of the Faculty of Digital and Analytical Sciences. His work focuses on GIS applications in mobility, urban planning, and cartography. Key projects include developing tools like NetAScore for assessing bikeability and walkability, and frameworks for analyzing transportation networks. He has authored over 75 publications, with recent work emphasizing spatial analysis for sustainable urban mobility. His research integrates agent-based modeling, spatial data science, and real-world transportation challenges. Education: Dr., MSc in Geoinformatics. Key Projects: GISMO Study on active commuting, Bike Sharing System Planning, Digital Twins for Urban Sustainability. Research Interests: GIS and mobility optimization, cartography, agent-based simulations, and spatial data science for urban planning. His contributions include software tools like NetAScore and frameworks for bicycle infrastructure analysis. Grants & Awards: Secured funding from various research initiatives; part of interdisciplinary teams addressing urban mobility challenges. Labs/Teams: Mobility Lab | UNIGIS, collaborating on projects like the Bicycle Observatory and spatial network analysis.

• GIS and Mobility
• GIS in Planning
• Cartography and Geovisualization
• +3 more

Elena Nafieva is a Researcher at the Department of Geoinformatics (Z_GIS) within the Faculty of Digital and Analytical Sciences at the University of Salzburg. Her work focuses on remote sensing , spatio-temporal analysis , and synthetic aperture radar applications for monitoring natural hazards and glacial dynamics using Big Earth Observation data . Current projects include: LEONA : Landslide Information from Earth Observation for Humanitarian Aid (2024-2027) ROGER : EO-based Rock Glacier Mapping (2024-2025) ReHIKE : Glacier Retreat Effects on Alpine Hiking Infrastructure (2024-2026) Her recent publications demonstrate expertise in landslide mapping , rock glacier characterization , and glacial foreland evolution using earth observation technologies. Project partners include Austrian Federal Ministry for Climate Action (FFG-ASAP) and European Space Agency (ESA). Research interests span: Remote Sensing of geohazards Spatio-temporal modeling of alpine environments Synthetic Aperture Radar processing Climate Change Impacts on mountainous regions Big Data Analytics in earth observation Digital Elevation Modeling for hazard assessment

• Remote Sensing
• Spatio-temporal analysis
• Synthetic aperture radar
• +3 more

Günter Blöschl is a Professor at the Institute of Hydraulic Engineering within the Technical University of Vienna 's Division of Mathematics and Natural Sciences. His research focuses on Hydrology , Water Resources , Environmental Engineering , and the impacts of Climate Change on water systems. He has contributed extensively to understanding flood dynamics, human-water interactions, and multi-scale hydrological processes. Selected Scientific Awards : Dr. h.c. Università di Bologna Dr. h.c. Slovak University of Technology Großes Silbernes Ehrenzeichen für Verdienste um Österreich ERC Advanced Grant Robert E. Horton Medal (AGU) Recent Research Trends : His publications from 2015-2019 emphasize climate impacts on flood regimes, sociohydrology, field-based hydrological observatories, and cross-scale Earth system dynamics. Key themes include flood risk assessment, human-water coevolution, and data-driven modeling.

• Environmental Engineering
• Applied Geosciences
• Hydrology
• +5 more

Ezio Bartocci is a Full Professor in Formal Methods for Cyber-Physical Systems Engineering at TU Wien's Faculty of Computer Science. He leads the Trustworthy Cyber-Physical Systems (TrustCPS) Group within the Cyber-Physical System Research Unit. His research focuses on formal verification, probabilistic systems, and runtime monitoring, with applications in autonomous systems, safety-critical software, and embedded systems. Roles & Affiliations: Full Professor, TU Wien (100% research focus) Principal Investigator in projects funded by EU, WWTF, FFG, and industry partners Chair of the Curriculum Commission for Computer Engineering Editor-in-Chief of the Formal Methods in Outer Space series Research Interests: Formal methods for CPS: verification, synthesis, and runtime monitoring Probabilistic programming and loop analysis Temporal logic specifications and mining Automated tools for safety-critical systems (e.g., Polar, MoonLight) Applications in healthcare, robotics, and autonomous vehicles Key Projects: ProbInG (2020–2025): Analyzing probabilistic loops ARTIST (2021–2026): AI and robotics safety EdgeAI (2022–2025): Optimizing embedded processing TAIGER (2023–2027): Trustworthy AI and CPS Grants & Funding: €10M+ secured from EU Horizon 2020, WWTF, FFG, and industry partners like TTTech Auto AG. Academic Leadership: Teaches courses on logical methods, CPS engineering, and scientific research at TU Wien. Supervises PhD students in formal methods and CPS domains. Tools Developed: Polar (probabilistic loop analyzer), MoonLight (spatio-temporal monitoring), and FIM (fault injection tool).

• Formal Methods
• Cyber-Physical Systems
• Probabilistic Programming
• +4 more

Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.

• Bayesian Statistics
• Time Series Analysis
• Econometrics
• +3 more

Dr. Alexander Pressl is a researcher at the Institute of Sanitary Engineering and Water Pollution Control , part of the University of Natural Resources and Life Sciences, Vienna (BOKU) . His work focuses on water quality monitoring , constructed wetlands for wastewater treatment , and climate change adaptation in hydrological systems . He has developed autonomous monitoring technologies and contributed to uncertainty assessment frameworks for environmental modeling. Research Themes Climate change impacts on river catchments Nitrogen removal in engineered wetlands Real-time pollution load quantification Cost-effective sanitation system planning Key Collaborations Worked with EGU (European Geosciences Union) and CCCA (Climate Change Centre Austria) Collaborated on international projects in India, Spain, and China Technological Contributions Developed GUESS sensitivity analysis framework Evaluated distributed temperature sensing for leakage detection Optimized two-stage vertical flow wetland designs

• Water Quality Monitoring
• Constructed Wetlands
• Hydrological Modeling
• +9 more