Yun S. Song

Yun S. Song is a full professor at the University of California, Berkeley, holding dual appointments in the Department of Electrical Engineering and Computer Sciences (EECS) and the Department of Statistics. His primary affiliations include the College of Engineering and the Simons Institute for the Theory of Computing. He leads research in computational biology, artificial intelligence, and mathematical genetics, with a focus on developing statistical methods for analyzing genomic data.

Education: Dr. Song earned dual bachelor's degrees in Mathematics and Physics from MIT (1996-1997), followed by a PhD in Physics from Stanford University in 2001.

Research Interests: Song's work bridges computational methods and biological problems, including population genetics, evolutionary genomics, and statistical inference. He develops machine learning tools for predicting genetic variants' effects, modeling evolutionary processes, and analyzing large-scale genomic datasets. Key areas include:

• Algorithm design for phylogenetic reconstruction
• Deep learning applications in genetic data analysis
• Statistical models for population demography
• Computational methods in molecular biology

Publications Trends: His recent work emphasizes scalable computational methods (e.g., CherryML for phylogenetic inference), transfer learning for disease variant prediction, and genomic analysis of human population diversity. Articles frequently appear in top journals like Nature Methods, Genome Biology, and PLOS Genetics.

• Scientific Awards: NIH Pathway to Independence Award (2006), Alfred P. Sloan Fellowship (2008), Packard Fellowship (2008), NSF CAREER Award (2009), Miller Professorship (2014), and Chan Zuckerberg Biohub Investigator (2017).

Advising & Grants: Song has advised numerous graduate students in computational biology and AI. He has secured grants from NIH, NSF, and the Simons Foundation. Current research focuses include developing statistical frameworks for analyzing high-throughput genomic data and applying deep learning to biological systems.

Labs/Teams: He directs research groups at the Center for Computational Biology (CCB) and collaborates with the Simons Institute on theoretical computing projects.