Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).
Olle Sköld is a Senior Lecturer at the Department of ABM (Archives, Libraries, and Museums) at Uppsala University. He is also the Head of the Department of ABM and holds an Associate Professor position in Library and Information Science. His research spans digital humanities, documentation practices, knowledge production, and digital preservation, with a particular focus on archaeological data processes and virtual world communities. Archaeological data documentation and reuse Digital game preservation Paradata theory and applications Information practices in social media His recent publications explore paradata in data papers, literacy challenges in research data management, and trust technologies like blockchain in digital archives. Olle is involved in the CAPTURE project, investigating documentation workflows for archaeological data. He has contributed to the Master's Programme in Digital Humanities at Uppsala University, blending digital humanities with library and information science education.
Max Planck Institute of Colloids and InterfacesGermany
Dr. Yolanda Gil is a Research Professor in Computer Science and Spatial Sciences at the University of Southern California, where she serves as Principal Scientist and Senior Director for Strategic Initiatives in Artificial Intelligence and Data Science at the Information Sciences Institute (ISI). She is also the Director of AI and Data Science Initiatives in the Viterbi School of Engineering and leads the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS). Dr. Gil received her Licenciatura in Computer Science from the Polytechnic University of Madrid and her M.S. and Ph.D. in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Her research focuses on developing AI approaches that use knowledge to accelerate scientific discovery processes. Her key research interests include knowledge capture and representation, semantic workflows, ontology tools, scientific discovery methods, task-based collaboration, provenance tracking, knowledge networks, reproducibility in science, and machine learning for data analysis. She collaborates with scientists across multiple domains to improve how scientific knowledge is created, shared, and used. Dr. Gil's work has significant impact across multiple scientific domains including climate science, neuroscience, and omics research. Her projects demonstrate her commitment to building knowledge-guided systems that transform how scientists conduct research. She has pioneered approaches to capture the provenance of scientific experiments and to automate the analysis of complex scientific data. Fellow of the Association for Computing Machinery (ACM) Fellow of the Association for the Advancement of Science (AAAS) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) 24th President of the Association for the Advancement of Artificial Intelligence Co-chair of the CRA/AAAI 20-Year Artificial Intelligence Research Roadmap for the US Initiator and leader of the W3C Provenance Group that resulted in a widely-used standard for web trust As an educator and leader, Dr. Gil directs the Data Science Program in Computer Science and serves as Co-Director of multiple joint MSc programs including Communication Data Science, Spatial Data Science, Environmental Data Science, Public Policy Data Science, and Healthcare Data Science. She also leads the new dual degree USC-Tsinghua University on Communication Data Science. Her leadership extends to mentoring numerous students and researchers in AI and data science. Through the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS), Dr. Gil organizes DataFest events each semester, fostering collaboration and innovation in data science across disciplines.
Danqi Chen is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. Their research focuses on advancing large language models (LLMs), with emphasis on model alignment, safety, and long-context reasoning capabilities. Key research areas: LLMs, AI safety, retrieval systems, and model optimization Recent work explores theorem proving, context encoding, and ethical content generation Their 2025 publications highlight innovations in formal verification scaffolding, attention mechanism efficiency, and copyright-aware generation. 2024 studies investigate continual memorization, rule-based chatbot representations, and scientific literature retrieval benchmarks. Current projects demonstrate commitment to improving model robustness, interpretability, and security compliance in multimodal systems.
Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.
Professor Isto Huvila is a leading scholar at Uppsala University's Department of ABM (Archives, Libraries, and Museums) within the Faculty of Humanities. His academic profile spans information science, digital humanities, and cultural heritage studies with a distinctive focus on documentation practices and information management across diverse contexts. His research interests encompass: Information and knowledge management Social and participatory information practices Documentation and paradata theory Digital humanities and archaeological information systems Health information and e-health services Open data and research data management Recent scholarly output reveals a strong trajectory toward understanding information practices across multiple domains. A significant portion of his work examines paradata in archaeological contexts through the CAPTURE project, investigating how documentation of data creation processes can enhance research transparency and data reuse. He also conducts extensive research on digital inclusion among older adults and immigrant populations regarding e-health services, analyzing both experiences and expectations of these user groups. Professor Huvila publishes in high-impact journals including Journal of Documentation, Information Research, Journal of the Association for Information Science and Technology, and Journal of Medical Internet Research. His interdisciplinary approach connects information science with archaeology, health informatics, and digital humanities, yielding practical insights for improving documentation standards, research data management, and e-health service design. His 2025 book 'Paradata: Documenting Data Creation, Curation and Use' represents a major contribution to the field, alongside influential review articles like 'Trends in information behavior research, 2016-2022' in the Annual Review of Information Science and Technology. His work bridges theoretical and practical concerns in information science, making substantive contributions to both academic knowledge and real-world applications in cultural heritage institutions and healthcare information systems.
Luisa Domingues is an Assistant Professor in the Department of Information Science and Technology at ISCTE - University Institute of Lisbon. She serves as an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture, specializing in Information Systems research. Her academic qualifications include a PhD in Information Science and Technology from ISCTE-IUL (2013). Her primary research interests focus on: Project Management : Methodologies, risk assessment, and knowledge sharing Database Systems : Management, interoperability, and standardization e-Government Solutions : Public sector information systems and shared services Ontological Frameworks : Particularly for architectural heritage documentation Educational Technologies : Blended learning approaches in higher education Her recent publications (2016-2024) demonstrate a strong focus on project management methodologies, knowledge transfer, and information systems in public sector contexts. Key thematic trends include PMBOK framework applications, agile methodology adoption challenges, data science project risks, and technological solutions for cultural heritage preservation. Most works employ case study research and empirical validation methods. Dr. Domingues maintains an active advising portfolio with 1 doctoral candidate and 36 master's students, primarily focusing on topics in project management, information systems, and data science applications. She has held significant academic leadership positions including: 4th Year Coordinator for Bachelor's in Computer Science and Business Management (2020-2027) Program Director for the same degree (2017-2019) At ISTAR-Iscte research center, she contributes to projects involving information systems architecture, data standardization, and knowledge management frameworks.
Renée J. Miller is Professor and Canada Excellence Research Chair in Data Intelligence at the University of Waterloo. A Fellow of the Royal Society of Canada and ACM, her research transforms how organizations manage and derive value from heterogeneous data sources. Professor Miller pioneered foundational work in schema mapping and data exchange recognized by the ICDT Test-of-Time Award. Her current research develops frameworks for semantic data discovery in data lakes, including the SANTOS system for relationship-based table search and Gen-T for table reclamation. She leads international collaborations advancing data management practices through tools like iBench for metadata generation and DIALITE for open data integration. Her CERC position establishes Canada's leadership in next-generation data intelligence systems.
Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.
Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.
Tamara Munzner is a Professor of Computer Science at the University of British Columbia, with a confirmed email at tamara@cs.ubc.ca . Her research focuses on information visualization , visual analytics , and graph drawing , emphasizing practical design frameworks and theoretical foundations. Recent work explores visitor engagement with science museum exhibits large-scale data visualization challenges health informatics applications for chronic pain management advanced graph neural network visualization His publications in IEEE Transactions on Visualization and Computer Graphics and Eurographics conferences demonstrate her expertise in visual analytics. Scientific awards include the Best Panel Award at Euro Vis 2009 for her work on visualization education. Her research spans visualization design principles, dimensionality reduction techniques, and applications in genomic epidemiology and environmental sustainability.
Dr. Albert Ruhi is Associate Professor in Environmental Science, Policy and Management at UC Berkeley. His research examines freshwater ecosystem responses to global change, with focus on hydrologic alteration and drought impacts across river networks. The Ruhi Lab integrates field experiments, time-series analysis, and hydrologic modeling to understand metacommunity dynamics and ecosystem resilience. Educational background: PhD in Ecology, University of Girona BS in Biology, University of Girona Research explores: Drought impacts on aquatic communities Climate change effects on stream metabolism Wetland restoration outcomes Metacommunity recovery pathways Ecohydrological feedbacks Recent publications address: Phenological shifts in estuarine food webs Flow alteration cascades in river networks Beaver-mediated drought resilience Groundwater-riparian interactions Awards and honors: NSF CAREER Award (2021) California Sea Grant Award (2021) Hellman Fellowship (2020) Winkler Scholar (2023) Research group includes 7 graduate students and postdocs studying: Urban river restoration Tidal marsh food webs Alpine stream thermal regimes Vernal pool seedbanks Field sites span Sierra Nevada streams, San Francisco Bay wetlands, and Mediterranean rivers.
California Institute of Technology (Caltech)United States
Lior S. Pachter is the Bren Professor of Computational Biology and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). He holds a B.S. from Caltech (1994) and a Ph.D. from MIT (1999). His affiliations include the Division of Biology and Biological Engineering at Caltech. Roles: Faculty member, Principal Investigator Departments: Computational Biology and Computing and Mathematical Sciences Research interests span computational and experimental genomics, with a focus on single-cell sequencing technologies and RNA biology. His lab develops tools like kallisto, sleuth, and gget for genomic analysis. Key contributions include methods for quantifying RNA-Seq data and analyzing high-dimensional genomic datasets. Publications highlight advancements in spatial genomics, bioinformatics tools, and genomic data retrieval. His work emphasizes open-source software, with repositories hosted on GitHub.
Ian Ewart is an Associate Professor at the University of Reading's School of the Built Environment, serving as Head of Construction and Engineering Management and Research Group Lead for Organisation, People and Technology. He chairs the Research Ethics Committee since 2016 and supervises undergraduate/postgraduate dissertations. His academic journey spans engineering and anthropology: DPhil Social and Cultural Anthropology, University of Oxford, St Hugh's College (2007-2012) MSc Material Anthropology and Museum Ethnography, University of Oxford, St Hugh's College (2006-2007) BA (Hons) Archaeology and Anthropology, University of Oxford, Harris Manchester College (2003-2006) Diploma in Management Studies, University of the West of England (1990-1994) BEng (Hons) Mechanical Engineering, Staffordshire University (1983-1987) Ewart's research integrates ethnographic methods with digital technology studies, examining human-technology interactions in construction and domestic settings. His work bridges engineering practice and social anthropology, focusing on skill transmission, sustainable design, and multisensory experiences in virtual environments. Publications from 2025-2013 reveal a dominant trajectory in digital twins for socio-ecological sustainability, VR-based occupant behavior prediction, and HBIM for heritage conservation. The corpus demonstrates consistent cross-disciplinary innovation, merging archaeological reconstructions with healthcare applications while maintaining anthropological rigor. Key recognition: ESRC Future Research Leader fellowship (2013) for Designing Healthy Homes project He supervises PhD candidates like Afolabi Dania (Nigerian sustainable construction) and Joanna Hull (Heritage BIM), leveraging ESRC funding for ethnography-VR health studies. His grants emphasize participatory design and real-world impact assessment in built environments. Leaders the Organisation, People and Technology research group, developing multisensory Roman town reconstructions with sound/smell integration to advance archaeological and architectural experience modeling.
Dr. H.V. Jagadish is a Professor in the Computer Science and Engineering department, focusing on database systems, data science, and ethical AI. He emphasizes mentorship and independence in research, guiding students to become colleagues through collaborative growth. Research Focus His work spans Data integration and fairness in machine learning Query processing and knowledge graph analysis Reproducibility and ethical data management Advising and Research Dr. Jagadish encourages open communication, frequent feedback, and proactive student-driven research. He supports internships and conference attendance, prioritizing SIGMOD and VLDB for presentations.
