Juan Zhai

Interests

Software Engineering and Program Languages, Natural Language Processing and Software Text Analytics, Software Reliability and Security, Deep Learning

Research

Professor Zhai's research delves into the intricate interplay of formal reasoning, program analysis, and natural language artifact analysis. She takes a unique approach to comprehending software behaviors by examining two distinct types of artifacts. Firstly, she utilizes program analysis and formal methods to scrutinize precise yet intricate artifacts like program code and specifications. Secondly, she employs machine learning techniques to analyze easily understandable yet potentially imprecise software text artifacts, such as documents. By combining these approaches, her research aims to enable automated software reasoning at both the high level, such as addressing fairness in decision-making systems, and the low level, including detecting memory bugs. The techniques developed through her research find practical applications in diverse domains, including software testing, debugging, verification, and security enforcement.

Biography

Juan Zhai is currently an Assistant Professor in the Manning College of Information & Computer Sciences (CICS) at the University of Massachusetts, Amherst. Prior to joining UMass, she spent four years at Rutgers University. Before that, she held a postdoctoral position at Purdue University for one year, following her tenure-track faculty position at Nanjing University. In 2016, she earned her Ph.D., and in 2010, she completed her B.E., both from Nanjing University.

Activities & Awards

Prof. Zhai's achievements include receiving the Rutgers Research Council Award in 2020 and the Distinguished Paper Award from the USENIX Security Symposium in 2017. In 2016, she was recognized as an Outstanding Ph.D. and a NASAC Excellent Ph.D. candidate, while also earning several other national scholarships during her studies. She actively contributes to the academic community by serving on the technical program committees and editorial boards of esteemed conferences and journals in the fields of software engineering and machine learning.