I am a Ph.D. candidate in Computer Science at Yale University, advised by Prof. Wenjun Hu. My research interests include data analytic systems, mobile edge computing, and machine learning systems. Specifically, I’m interested in exploring how approximate computing and learning theories can be applied to design emerging distributed systems (data analytics, deep learning, etc.), to improve the performance and scalability. Prior to Yale, I received my B.Eng. degree from Tsinghua University.
Ph.D. candidate in Computer Science
M.Sc. in Computer Science, 2017
B.Eng. in Electrical Engineering, 2015
A novel caching/memoization paradigm for machine learning based applications. Approximate computation reuse aims to relax the computation reuse requirements from exact matching to approximate matching, so as to exploit the function approximation and input error tolerance properties of these emerging applications to improve system performance.
Despite extensive investigation of job scheduling in data-intensive computation frameworks, less consideration has been given to optimizing job partitioning for resource utilization and efficient processing. In light of this, we design a module as a framework extension to automate real-time job partitioning on individual task granularity.