David Siegel is a computational geneticist with a PhD in physics. He specializes in HIV host genetics and has extensive experience in experimentation, data analysis, and machine learning. As a former postdoctoral researcher at UCSF, he now serves as a computational geneticist in HIV Host Genetics, studying advanced genomic technologies to discover genes influencing HIV disease severity and susceptibility. He has also worked as an adjunct faculty member at several colleges and researching institutions, including Diablo Valley College, California State University, East Bay, and the Lawrence Berkeley National Laboratory. With a strong educational background, David has studied physics, mathematics, and other related courses extensively.

Throughout his career, David Siegel has investigated the behavior of materials at various states of matter. However, he has transitioned his knowledge to computational genetics to understand the genetic elements associated with HIV disease vulnerability. David's expertise lies in analyzing large datasets using statistical and computational methods to extract knowledge from the underlying data. As a published author with vast experience, he has leveraged his proficiency in experiment design, data analysis, and machine learning techniques to unravel complex biological systems.