I am generally interested in data science, text mining, information retrieval, software engineering, data security and privacy.
Most of my work leverages machine learning/data mining/ information retrieval to computationally improve user experience in decision making tasks, including assisting e-Commerce users with exploratory search, assisting Android users with security decision making, and assisting scholars exploring text corpora. My ongoing work is on natural language to program synthesis to help programmers more easily write code.
Text Mining for Mobile Security Interaction
CLAP: A Recommender System for Assisting User Security Interaction
Learning Search Log to Optimize Numerical Facet Interface
Interactive Hierarchical Moment-based Inference
Constructing a topic hierarchy for large text collection, such as business documents, news articles, social media messages, and research publications, is helpful for information workers, data analysts and researchers to summarize and navigate them in multiple granularity efficiently. However, complete automatic approaches are often error prone, often failing to meet user requirements. We proposes to give users freedom to construct topical hierarchies via interactive operations such as expanding a branch and merging several branches. We build our approach based on a spectral learning framework named moment-based inference method, and our technical contributions are of two folds. First, we derive robust inference solutions for each operation, so that user editing does not lose information for the inference. Second, we optimize the algorithms of moment-based framework, so our proposed method is orders of magnitude faster than existing hierarchical topic construction methods.