Speakers: Harry Caufield, PhD
Harry Caufield, PhD
Biological and Biomedical Data Scientist
Lawrence Berkeley National Laboratory
Dr. Caufield is a biological and biomedical data scientist specializing in natural language processing, bio-ontologies, standards development, machine learning, and knowledge graphs, all aimed at facilitating the understanding of human disease from unstructured data. He is currently based at Lawrence Berkeley National Laboratory, where he is a member of the Berkeley Bioinformatics Open-source Projects group (BBOP), led by Chris Mungall. Before joining Berkeley Lab, Dr. Caufield conducted research in data mining and standardization in the context of cardiovascular disease as part of the HeartBD2K and iDISCOVER programs at UCLA. He earned his PhD from Virginia Commonwealth University. Additionally, he actively participates in the Bridge2AI consortium, the Monarch Initiative, and the BioPortal ontology knowledge base, all National Institutes of Health-supported programs dedicated to enhancing the standardization and integration of heterogeneous biomolecular datasets.
Finding Harmony Between Artificial Intelligence and Biomedical Informatics
An explosion of emerging technologies in computational science has recently become not only impactful but unavoidable. Machine learning and its sundry applications, generally referred to as artificial intelligence (AI), have shown remarkable potential for improving everyday life. AI’s benefits to human health remain unclear.
Medicine and biomedical research continue to generate enormous data, often in forms intractable to examination or integration with other observations. Curated biomedical knowledge bases have traditionally been used to organize and explain this data. With recent AI advancements, is this still a job for humans? What do we risk losing if biocuration and interpretation become solely computational tasks?
This talk will present my journey through bioinformatics and biomedical data science. Along the way, I will discuss strategies for orchestrating effective AI applications in biomedical informatics. Specifically, I will explore how AI can be used alongside structured data to reveal novel disease phenomena. I will also examine the relationships between data standards and the use of AI in biomedical research.
I believe that the key to constructive application of AI lies in finding harmony between human expertise and computational creativity. Our collaborative efforts can create a future where AI is a tool for improving human health.