Junfeng Yang

In his PhD dissertation research, Junfeng Yang created eXplode, a general, lightweight system for effectively finding storage system errors. This work led to numerous bug fixes to real systems, such as the Linux kernel, including fixing data-loss bugs that cause users to lose entire file system partitions. In 2008, Junfeng Yang worked at Microsoft Research Silicon Valley, extending eXplode to check production distributed systems. The resulting system, MoDist, was transferred to Microsoft product groups. At Columbia University, Junfeng Yang’s work on reliable multithreading was featured by sites such as ACM Tech News and The Register.  Most recently, Junfeng Yang has been working on improving the reliability, security, and privacy of machine learning systems, in particular deep learning systems and self-driving platforms. This work was featured in The Atlantic.  

Yang received a BS in computer science from Tsinghua University in 2000 and a PhD in computer science from Stanford University in 2008.   He won the Sloan Research Fellowship and the Air Force Office of Scientific Research Young Investigator Program Award, both in 2012; the National Science Foundation CAREER award in 2011; and a Best Paper Award at the USENIX Symposium on Operating System Design and Implementation in 2004.