I am currently a researcher on system security team at Visa Research, my research area lies in the intersection of Programming Language, Deep Learning and Artificial Intelligence with a increasing focus of applying deep models in the domain of static analysis and formal methods. Previously I obtained my PhD in Computer Science from UC Davis under the supervision of Prof. Zhendong Su.
Visa Research is an emerging industrial research lab. We focus on fundamental research, encourage university collaboration and emphasis publication. If you have a great publication record and are interested in pursuing a research career in our lab. Feel free to reach out at [first name](no space, comma, hyphen, etc.)[last name]@visa.com
Learning semantic program embeddings is an important problem. Precise and scalable program representation enables the application of deep neural networks to a wide range of program analysis tasks. My reseach tackles this problem from three different angles: dynamic (i.e. learning from execution trace), static (i.e. learning from source code) and hybrid (i.e. learning from both).
06/2018-02/2017 Microsoft Research Redmond
Papers: PLDI 2018, ICLR 2018
Patent: Data-driven feedback generator for programming assignments
Products: Microsoft C# course, Microsoft Python Course
Post: AI for Education: Individualized Code Feedback for Students
12/2016-09/2016 Facebook Inc.
09/2016-03/2016 Microsoft Research Redmond.
09/2010-07/2012 Siemens Corporate Technology.
06/2010-01/2010 Siemens Corporate Research.
I will be serving on the program committe for MAPL 2020.
I will be serving on the program committe for PLDI 2020.
Joined Visa Research.
Search, Align and Repair for Feedback Generation to appear at PLDI 2018.
Dynamic Neural Program Embedding to appear at ICLR 2018.
I am leading a second team to adapt the feedback technology to a new Microsoft Python course to be launched after the new year.
I am leading a team to productionalize the research on feedback generation for MOOC.