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:
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 sciencesed and pharma-focu 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.
Zen Jelenje (Moderator)
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.
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.
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.
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