This is the first webinar in the four-part series called “AI in innovation: Unlocking R&D with data-driven AI.”
From poor data to the frame problem, RAG, and vector-based IR, our panel of AI and data experts will outline the issues that can derail your AI projects as well as explore the perils, pitfalls, and promise of generative AI for R&D. They’ll also answer your questions about how Elsevier licenses, delivers and updates data for use in generative AI.
During this session, we will cover:
Head of Ontologies, SciBite
Jane leads the Ontologies technical and services team at SciBite. She holds a PhD in Genetics from Cambridge University and has 20 years’ of experience working with biomedical ontologies, including at EMBL-EBI and the Wellcome Sanger Institute. She has published over 35 scientific papers, mainly in the area of ontology development, and is a regular contributor to public endeavors, including the Pistoia Alliance, Elixir, and the International Society of Biocuration.
Director of Data Science & Professional Services, SciBite
Joe’s academic background sits in compbio and the application of ML-based analytics to semantic knowledge graphs, particularly in the context of drug repositioning. Since leaving academia, Joe has focussed on applying SoTA technology to a wide variety of life sciences and pharma-focus tasks. Joe currently leads the data science and professional services team at SciBite, who are dedicated to providing bespoke solutions to address customers’ specific scientific needs.
Vice President of Data Science, Life Sciences, Elsevier
Mark has been an active player in multiple waves of digital transformation throughout his 20+ year career in Elsevier. He was part of the team that developed and implemented Elsevier’s journal and book data standards, powering the transition from print to online through to ebooks and e-learning. He currently leads a large data science and subject matter expert team developing AI capabilities to enrich scientific content, with a particular focus on extracting biology and chemistry entities and relationships to support the drug discovery life cycle. His expertise in data science and scientific content has supported Elsevier’s significant developments of industrial-scale award-winning AI enrichment pipelines for Life Sciences.
Commercial Director, Corporate Markets, Elsevier
Zen’s academic background is in Environmental Science, with a focus on modeling and policy. He has contributed to UK, EU, and industry consultations on the commercial, statutory, IP, and privacy implications of data science and artificial intelligence. He is responsible for Elsevier’s data division; licensing datasets to organizations undertaking AI-first research projects.
Leading SciBite’s data science and professional services team, Joe is dedicated to helping customers unlock the full potential of their data using SciBite’s semantic stack. Spearheading R&D initiatives within the team and pushing the boundaries of the possible. Joe’s expertise is rooted in a PhD from Newcastle University, focussing on novel computational approaches to drug repositioning; building atop semantic data integration, knowledge graph & data mining.
Since joining SciBite in 2017, Joe has been enthused by the rapid advancements in technology, particularly within AI. Recognizing the immense potential of AI, Joe combines this cutting-edge technology with SciBite’s core technologies to craft tailored, bespoke solutions that cater to diverse customer needs.
Other articles by Joe
Jane leads the development of SciBite’s vocabularies and ontology services. With a Ph.D. in Genetics from Cambridge University and 15 years of experience working with biomedical ontologies, including at the EBI and Sanger Institute, she focussed on bioinformatics and developing biomedical ontologies. She has published over 35 scientific papers, mainly in ontology development.
Other articles by Jane: