Dr. Chris Hart, PhD

VP, Data Science and AI/ML at Eli Lilly and Company

Speaker

Chris is a pioneering scientist and biotech executive who has spent over two decades advancing the intersection of computational biology, genomics, and drug development. Currently Chris is the Vice President, Data Science and AI/ML within the Genetic Medicines team at Eli Lilly & Company. Prior to joining Lilly, he was the founding CEO of Creyon Bio where he led groundbreaking work in oligonucleotide-based medicines, focused on understanding how sequence and chemistry collude to impact the safety and activity of nucleic acid drugs and how aptameric extensions could be used to deliver OBMs to distinct tissues and cells. Under his leadership, the team leveraged AI/ML approaches to achieve orders-of-magnitude improvements in lead compound identification, enabling rapid therapeutic advances. Among these was the development of an allele-selective LNA ASO for an ultra-rare disease that advanced from concept to clinic in just 13 months.

Prior to founding Creyon Bio, Chris spent seven years at Ionis Pharmaceuticals, where he built and led the functional genomics group, an elite multidisciplinary team that transformed the company's approach to genomic and bioinformatic analysis across their development and discovery programs. His work contributed to multiple therapeutic advances, including pioneering efforts in regenerative medicine and the optimization of drug-candidate screening processes.

Earlier in his career, Chris served as a Research Staff Member at the Science and Technology Policy Institute in Washington, D.C., where he provided strategic guidance to the President's Council of Advisors on Science and Technology, the NIH, and other federal agencies on matters ranging from personalized medicine to research portfolio analysis. At Helicos Biosciences, he contributed to the development of novel sequencing technologies and bioinformatic tools.

Chris has deep interdisciplinary training at the interface of biology and computer science. He earned his Ph.D. from Caltech, where his doctoral research focused on building interpretable AI/ML models for genetic regulatory networks, followed by postdoctoral training at Yale University in experimental and computational genomics.