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Daniel (Danny) Wells, PhD

Principal Data Scientist



Daniel Wells, PhD, is the principal data scientist scientist at PICI. The first member of the informatics team, he’s worn a lot of hats since the institute opened, including: collaborating with PICI investigators to accelerate scientific discovery; contributing to the design of in-house tools for next generation sequencing analysis; and contributing to PICI-lead research projects such as TESLA. Currently his focus is building out capabilities to analyze multi-omic data sets to identify new strategies for combination immunotherapy (“Reverse Translational Medicine”).

Throughout all of his work, his core passion is leveraging large data sets to uncover basic principles of human tumor immunobiology, and collaborating with doctors and clinical investigators to translate these discoveries into new therapies for cancer patients.

Before joining PICI, Wells was a postdoctoral fellow at UC-Berkeley where he studied the the evolution of multicellular animal life. He completed his PhD in applied math at Northwestern University as an NSF Graduate Research Fellow, where he worked on a diverse set of projects in machine learning, computational biophysics, and tumor immunology. He did his undergraduate degree in math at Carleton College. An internationally recognized speaker, he has published dozens of research articles in journals such as Nature and Nature Medicine.