Keynote Speakers
Bio: Juan Sequeda is the Principal Scientist and Head of the AI Lab at data.world. He holds a PhD in Computer Science from The University of Texas at Austin. Juan's research and industry work has been on the intersection of data and AI, with the goal to reliably create knowledge from inscrutable data, specifically designing and building Knowledge Graph for enterprise data and metadata management. Juan is the co-author of the book "Designing and Building Enterprise Knowledge Graph" and the co-host of Catalog and Cocktails, an honest, no-bs, non-salesy data podcast.
Juan has researched and developed technology on semantic data virtualization, graph data modeling, schema mapping and data integration methodologies. He pioneered technology to construct knowledge graphs from relational databases, resulting in W3C standards, research awards, patents, software and his startup Capsenta acquired by data.world in 2019. Juan is the recipient of the NSF Graduate Research Fellowship, received 2nd Place in the 2013 Semantic Web Challenge for his work on ConstituteProject.org, Best Student Research Paper at the 2014 International Semantic Web Conference (ISWC), the 2015 Best Transfer and Innovation Project awarded by the Institute for Applied Informatics, 2023 Best Industry Paper at SIGMOD and nominated two additional times for best paper at ISWC.
Juan strives to build bridges between academia and industry as former co-chair of the LDBC Property Graph Schema Working Group, member of the LDBC Graph Query Languages task force, standards editor at the World Wide Web Consortium (W3C). Juan continues to be an active member of the scientific community by being on the editorial board and program committees of scientific journals and conferences in Semantic Web, Knowledge Graphs, Databases and AI, as well as organizer of various academic and industry conferences, including being the General Chair of The ACM Web Conference 2023.
Toward the Era of Scientific AI: Adapting Large Language Models to Scientific Domains
This talk provides an overview of how large language models (LLMs) are being adapted for scientific domains. It begins by discussing the recent trend of LLMs and highlights the development of the LLM-JP model series, which are fully open, fully scratch-built Japanese-centric LLMs. Then, the talk introduces projects aimed at developing LLMs for the Japanese medical domain, as well as the design of model construction pipelines in the AI for Science area. Finally, it addresses current and future challenges related to these adaptations.
Bio: Akiko Aizawa is a professor and the director of the Digital Content and Media Sciences Research Division at the National Institute of Informatics (NII) in Japan. She is also an adjunct professor at the University of Tokyo and the Graduate University for Advanced Studies.
Aizawa’s research interests include natural language understanding, information retrieval, and scientific research infrastructure. She is currently working on developing a Japanese-centric medical Large Language Model as part of the Cross-Ministerial Strategic Innovation Promotion Program (SIP) focused on creating an “Integrated Health Care System.” Additionally, she serves as the Deputy Director of the Large Language Model Research and Development Center at NII.
