主要研究方向
张昕的主要研究方向为程序设计语言与软件工程，其研究重点在于程序分析与机器学习的交叉领域。一方面，他利用机器学习技术提高程序分析的可用性，提出了概率与逻辑相结合的程序分析、自适应性程序分析、基于用户反馈的程序分析等；另一方面，他开发了针对机器学习系统的程序分析和语言，在机器学习系统的公平性、可解释性问题上都有所创新。
教育与工作经历
2017-2020，博士后，美国麻省理工学院
2011-2017，博士，美国佐治亚理工学院
2007-2011，学士，上海交通大学
所获奖项
FSE 2015杰出论文奖
PLDI 2014杰出论文奖
Selected Publications
1. Osbert Bastani, Xin Zhang, Armando Solar-Lezama. Verifying Fairness Properties via Concentration. ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 2019.
2. Xin Zhang, Armando Solar-Lezama, Rishabh Singh. Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. Conference on Neural Information Processing Systems (NeurIPS), 2018.
3. Xin Zhang, Ravi Mangal, Mayur Naik, and Aditya Nori. Query-Guided Maximum Satisfiability. ACM Symposium on Principles of Programming Languages (POPL), 2016.
4. Ravi Mangal, Xin Zhang, Mayur Naik, and Aditya Nori. A User-Guided Approach to Program Analysis. ACM Symposium on Foundations of Software Engineering (FSE), 2015. Distinguished Paper Award.
5. Xin Zhang, Ravi Mangal, Radu Grigore, Mayur Naik, Hongseok Yang. On Abstraction Refinement for Program Analyses in Datalog. ACM Conference on Programming Language Design and Implementation (PLDI), 2014. Distinguished Paper Award.
6. Xin Zhang, Ravi Mangal, Mayur Naik, Hongseok Yang. Hybrid Top-down and Bottom-up Interprocedural Analysis. ACM Conference on Programming Language Design and Implementation (PLDI), 2014.
7. Xin Zhang, Mayur Naik, Hongseok Yang. Finding Optimum Abstractions in Parametric Dataflow Analysis. ACM Conference on Programming Language Design and Implementation (PLDI), 2013.
