Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing (SoC). He holds a B.Sc. (1st Class Honors) and Ph.D. in Computer Science from Monash University. His research focuses on database systems, large-scale analytics, and distributed computing. He has held leadership roles including Dean of School of Computing (2007–2013) and Director of Smart Systems Institute (2011–2021). Key achievements include the Singapore President’s Science Award (2011), ACM Fellow (2011), IEEE Fellow (2009), and multiple best paper awards. His work emphasizes scalable data management, blockchain systems, and healthcare data analytics. Education: Monash University (B.Sc., Ph.D.) Leadership: Dean (SoC), Director (Smart Systems Institute) Awards: Over 15 major honors including ACM SIGMOD E.F. Codd Innovations Award (2020) His research spans distributed databases, big data systems, and innovative applications of blockchain technology. Recent work includes NASI (neural architecture search) and Rafiki (ML-as-a-service).
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.
Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.
Timon Rabczuk is a Chaired Professor of Computational Mechanics at the Institute of Structural Mechanics, Bauhaus-Universität Weimar, Germany, since 2009. He previously held positions as Senior Lecturer at the University of Canterbury (2007-2009), Postdoctoral Fellow at Technical University of Munich (2005-2007), and Postdoctoral Fellow at Northwestern University (2002-2005). His research focuses on computational mechanics, materials science, and numerical methods for multi-scale and multi-field problems involving material failure, plasticity, and large deformations. Research Contributions : He develops models for virtual material design, enabling lightweight structures and energy storage applications. His work bridges natural and engineering sciences and has been implemented in commercial/open-source software. He has published over 600 papers, 9 book chapters, and 3 books (including two in 2023). His research spans 2D materials, composites, machine learning interatomic potentials, flexoelectricity, and biomechanical engineering. Scientific Awards : ERC Consolidator Grant (2013), Thomson Reuters/Clarivate Analytics Highly Cited Researcher (2014-2021) Editorial Roles : Editor-in-Chief of CMC-Computers, Materials and Structures; Executive Editor of FSCE-Frontiers of Structural and Civil Engineering; Associate Editor of multiple journals including International Journal of Impact Engineering and Applied Physics A. He has graduated 38 PhD students and secured over €25 million in research funding from national and European agencies.
Associate Professor at the University of Klagenfurt , affiliated with the Department of Management Control and Strategic Management under the Faculty of Economics and Law . Research focuses on agent-based modeling applied to organizational dynamics , complex systems , and managerial economics . Holds a doctoral degree in Social Sciences and Economics (2012) and venia docendi in Business Economics (2018) . Core faculty member in the Self-Organizing Systems research cluster Academic editor for PLoS ONE and editorial board member for multiple journals Recipient of the 2021 Advancement Award (Humanities/Social Sciences) from Carinthian government Research integrates computational simulation with organizational theory , examining phenomena like decentralized task allocation , incentive mechanisms , and reproducibility in social sciences . Teaching portfolio includes business analytics , management control , and scientific modeling at undergraduate and graduate levels. Recent publications explore organizational resilience , team coordination dynamics , and financial modeling using agent-based simulation techniques. Active participant in international conferences like Social Simulation Conference and European Conference on Operational Research .
Prof. Walter Schwaiger is a Full Professor at TU Wien’s Faculty of Mechanical and Industrial Engineering, leading the Institute of Management Science. His academic roles include serving as Head of the Faculty Council since 2010 and holding various curriculum committee positions. He teaches critical courses such as Financial Management, Enterprise Risk Management, and IT-based Management across bachelor’s and master’s programs. His research focuses on three core areas: Financial Enterprise Management (stochastic NPV modeling for renewable energy investments), Enterprise Risk Management (risk maturity assessments via ERMMA studies), and IT-based Management (ontology-driven accounting frameworks like OntoREA). Recent work includes predictive analytics applications in credit risk scoring and pandemic-driven default prediction studies. Prof. Schwaiger has authored influential textbooks like IFRS-Finanzmanagement series and pioneered the REA-based ERP-Control system. He actively contributes to management control research, publishing in venues like Controlling and WingBusiness , and collaborates with institutions like Funk Stiftung on large-scale ERM maturity studies. His professional service includes leading faculty strategy groups and quality management initiatives at TU Wien, reflecting his commitment to institutional governance and academic excellence.
Xinliang Feng is a W3 Chair Professor at Dresden University of Technology, where he heads the Chair of Molecular Functional Materials. He also holds an Adjunct Chair Professorship at Shanghai Jiao Tong University, China. Previously, he served as a Distinguished Group Leader at the Max Planck Institute for Polymer Research in Mainz, Germany (2012-2014), and as a Group Leader at the same institute (2007-2012). His educational background includes a Bachelor's degree in Analytic Chemistry (2001), a Master's degree in Organic Chemistry (2004), and a PhD from the Max Planck Institute for Polymer Research (2008). Feng's research focuses on the frontier areas of nanomaterials science, particularly on 2D nanomaterials and low-dimensional nanostructures for energy applications. His work spans from fundamental organic synthesis to applied energy technologies. Key research areas include: Bottom-up synthesis of carbon nanostructures and graphene nanoribbons 2D polymers and supramolecular polymers with tailored properties Mesoporous covalent-bonding organic frameworks for energy storage Organic synthetic methodology in aromatic coupling reactions 2D carbon-rich conjugated polymers for electronic and optoelectronic applications His extensive publication record includes over 436 journal papers with significant impact, featuring publications in top-tier journals including Nature (3 papers), Science (1 paper), Nature Materials (2 papers), and numerous papers in Advanced Materials, Angewandte Chemie, and Journal of the American Chemical Society. His work has garnered over 34,797 citations in Web of Science (H-index ≥88) and over 44,959 citations in Google Scholar (H-index ≥101). Feng has received numerous prestigious awards recognizing his contributions to materials science and chemistry: Member of the German National Academy of Sciences (Leopoldina, 2024) Member of the Academia Europaea (2019) Fellow of the European Academy of Sciences (2019) EU-40 Materials Prize (2018) ERC Consolidator Grant Award (2018) Multiple years as a Highly Cited Researcher (2014-2018) ERC Starting Grant Award (2012) IUPAC Prize for Young Chemists (2009) He serves on the international/editorial advisory boards of 12 international journals and has organized or co-organized 30 symposia, workshops, and conferences. As the Head of the ESF Young Research Group "Graphene Center Dresden" and Working Package Leader for the EU GRAPHENE FLAGSHIP project, he plays a significant role in European materials research initiatives.
Anja Feldmann is Director at the Max Planck Institute for Informatics in Saarbrücken and Professor of Internet Network Architectures at Technische Universität Berlin (since 2006). Previously she held a full professorship at Technische Universität München (2002–2006) and conducted research at AT&T Labs Research , Saarland University , and Carnegie Mellon University , where she earned her Ph.D. in 1995. Education Ph.D. in Computer Science, Carnegie Mellon University, 1995 M.Sc. in Computer Science, Carnegie Mellon University, 1991 Diplom in Computer Science, Universität Paderborn, 1990 Research Interests Anja Feldmann’s research centers on measurement-driven understanding of the Internet. She tackles challenges such as software-defined networking , cloud-network interactions , performance debugging , and traffic characterization . A growing focus is the privacy and security of networked systems, evidenced by recent studies on online tracking, DNS security, and disinformation ecosystems. Her group designs scalable measurement platforms that combine passive and active monitoring , programmable data planes , and machine-learning analytics to dissect phenomena ranging from terabit-scale traffic to covert tracking on illegal streaming sites. Recent Publication Themes The 2021-2025 publications reveal a methodological evolution toward large-scale, longitudinal measurement . Topics include: Impact of global events (COVID-19, CrowdStrike outage) on Internet traffic Cross-country tracking ecosystems and privacy leaks DNS root and routing plane stability and security ML-driven real-time monitoring at terabit speeds Disinformation campaigns on encrypted messaging platforms Scientific Awards Gottfried Wilhelm Leibniz Prize (2011) – Germany’s highest research honor Berliner Wissenschaftspreis (2011) Elected Member of the German National Academy of Sciences Leopoldina (2009) Advising & Grants While individual student names are not listed, Prof. Feldmann leads a vibrant team at MPI-INF’s Internet Architecture department. She has supervised numerous doctoral candidates and post-doctoral researchers whose work is reflected in the co-authored papers. Funding sources include the German Research Foundation (DFG) via the Leibniz Prize and EU Horizon projects, although explicit grant numbers are not provided in the source material. Labs & Teams She heads the Internet Architecture department at MPI-INF, located at the Saarland Informatics Campus . The department operates state-of-the-art measurement infrastructure—including programmable switches, honeynets, and global vantage points—to support empirical network science.
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.
Gerti Kappel is a full professor at the Institute of Information Systems Engineering at TU Wien, affiliated with the Business Informatics Group (BIG). Since 2020, she has served as Dean of the Faculty of Informatics at TU Wien, previously holding the role of Dean’s team member responsible for research, diversity, and financial affairs (2016–2019). She previously held a full professorship in computer science (database systems) and led the Department of Information Systems at Johannes Kepler University Linz (1993–2001). Her research focuses on Model Engineering, Web Engineering, and Process Engineering, particularly in cyber-physical production systems. She has co-authored influential works such as UML@Work (2005), UML@Classroom (2015), and Web Engineering (2006). Key projects include leadership roles in Vienna Informatics Living Lab (2018–2019), MPM4CPS (2014–2019), and ARTIST (2012–2015), addressing topics like model versioning, cloud architecture modeling, and inter-organizational systems. Her recent articles explore circular systems engineering, IoT-based simulation environments, and model-driven approaches for time-series analytics and cloud applications. She actively contributes to academic governance, including managing the Office of the Dean (E199-01) and overseeing faculty services. Her work emphasizes bridging theory and practice through collaborative frameworks like ERPEL and TROPIC . Grants: Projects funded by Austrian Research Promotion Agency, European Cooperation in Science and Technology, Vienna Business Agency, and others. Advising: Supervised over 15 graduate theses, including recent works on model-driven techniques for railway planning, debugging frameworks for modeling tools, and cloud-based IDEs. Labs/Teams: Leads research within the Business Informatics Group (BIG) and collaborates with the Vienna Informatics Living Lab for applied systems engineering.
Lukas Grasmann is a Researcher at the Faculty of Informatics, Vienna University of Technology (TU Wien), based in the Databases and Artificial Intelligence research group (Institute E192-02, Room HA0302). His contact details include email lukas.grasmann@tuwien.ac.at and phone +43-1-58801-192216, with professional activities centered on cutting-edge database technologies and AI applications. His educational qualifications comprise: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) in Engineering Grasmann's research spans database systems and artificial intelligence with emphasis on big data analytics, distributed query processing, and skyline query optimization. His work focuses on integrating specialized query paradigms into Apache Spark SQL for efficient large-scale data analysis, particularly applied to perishable food supply chain optimization for waste reduction. This intersects computer science fundamentals with sustainability-driven practical implementations. His 2022-2023 publications reveal a concentrated research trajectory in enhancing Spark SQL's capabilities for skyline queries, addressing both theoretical optimization challenges and real-world deployment in distributed environments. The work demonstrates consistent innovation in bridging database theory with big data infrastructure, advancing scalable solutions for multi-criteria decision problems. Scientific recognition: No formal awards or fellowships documented in source materials Research funding and collaborations include: Lead researcher on FFG-funded project "AI-driven collaborative supply and demand matching platform for food waste reduction" (2022-2025) Contributor to HyperTrac project (2018-2022) focusing on traceability systems Active participant in KnowledgeGraph initiative (2020-2028) developing semantic knowledge networks He operates within TU Wien's Databases and Artificial Intelligence research ecosystem (Institute E192), collaborating with specialists like Pichler and Selzer on database scalability challenges. The group maintains strong industry connections through applied projects targeting supply chain intelligence and food waste analytics.
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.
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.
Dr. John G. Ayisi is a prominent researcher in public health and infectious diseases, with affiliations including the Kenya Medical Research Institute (KEMRI), Johns Hopkins Bloomberg School of Public Health, and Amsterdam UMC – University of Amsterdam. His work focuses on maternal and child health in sub-Saharan Africa, particularly the interplay between malaria, HIV, and malnutrition during pregnancy. Kenya Medical Research Institute Johns Hopkins Bloomberg School of Public Health Amsterdam UMC - University of Amsterdam His research interests include infectious disease epidemiology, maternal immunology, and integrated public health interventions. He employs meta-analytic and field-based methodologies to assess the impact of preventive therapies and genetic factors on perinatal outcomes in malaria- and HIV-endemic regions. The recent publications show a consistent focus on pregnancy-associated malaria, genetic susceptibility to HIV transmission, and the combined burden of malnutrition and infection. His work often involves large-scale data synthesis and community-based trials, emphasizing evidence-based policy formulation for low-resource settings. Dr. Ayisi has received funding and support from leading global health institutions, including: National Institutes of Health (NIH) Centers for Disease Control and Prevention (CDC) National Research Foundation Natural Sciences and Engineering Research Council of Canada Department for International Development (UK) Association Française contre les Myopathies He has advised or collaborated on numerous research projects aimed at reducing maternal and child morbidity in Africa. While no formal students are listed, his leadership in multi-institutional studies suggests a mentorship and collaborative role. His research has been instrumental in shaping malaria prevention policies, particularly regarding intermittent preventive therapy in pregnancy (IPTp) and integrated maternal health programs. Dr. Ayisi has contributed to major initiatives such as the Maternal Malaria and Malnutrition (M3) program and has developed novel methodologies for placental immunology studies. His work bridges laboratory science, epidemiology, and public health implementation, often conducted in partnership with local and international research teams in Kenya and beyond.
Dominik Kopczynski is a researcher affiliated with the Department of Analytical Chemistry at the Faculty of Chemistry. His work focuses on lipidomics, mass spectrometry, and the development of bioinformatics tools for lipid structure analysis and experimental standardization. He has contributed to initiatives like the lipidomics reporting checklist and Goslin nomenclature system. Active in organizing academic events such as the de.NBI Spring School for lipidomics bioinformatics and Keystone Symposia on lipid biology, he collaborates extensively with international researchers. Research Interests: Lipid metabolism, mass spectrometry-based analytical methods, lipid nomenclature standardization, proteomics, and computational tools for omics data analysis. His work bridges biochemical experimentation with bioinformatics to enhance reproducibility and interdisciplinary collaboration in lipidomics research. Activities: Organized 36 academic events including conferences, schools, and poster sessions. Notable contributions include co-organizing the 2025 de.NBI Spring School and contributing to the Keystone Symposia on lipid cellular function and disease. Engaged in both presenting research and fostering collaborative research networks. Labs/Teams: Collaborates within the Department of Analytical Chemistry and participates in international lipidomics consortia. His work often involves cross-disciplinary teams focused on integrating biochemical and computational approaches. Grants/Advising: While specific grants are not detailed, his extensive publication record and collaborative activities suggest involvement in funded projects related to lipidomics infrastructure development and analytical method standardization.
