主要研究方向
張昕的主要研究方向為程序設計語言與軟件工程，其研究重點在于程序分析與機器學習的交叉領域。一方面，他利用機器學習技術提高程序分析的可用性，提出了概率與邏輯相結合的程序分析、自适應性程序分析、基于用戶反饋的程序分析等；另一方面，他開發了針對機器學習系統的程序分析和語言，在機器學習系統的公平性、可解釋性問題上都有所創新。
教育與工作經曆
2017-2020，博士後，美國麻省理工學院
2011-2017，博士，美國佐治亞理工學院
2007-2011，學士，上海交通大學
所獲獎項
FSE 2015傑出論文獎
PLDI 2014傑出論文獎
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.
