About

I am a 2nd year Ph.D. student in the Computer Science Department at Purdue, advised by Prof. Jean Honorio. My research interests lie broadly in statistical learning and optimization. Recently, my research focuses on developing statistically and computationally efficient algorithms for problems in community detection and related applications.

Previously I obtained my B.S. degree in Computer Science from University of Rochester, where I was advised by Prof. Ji Liu.

Publications

• Chuyang Ke, and Jean Honorio, "Exact Recovery in the Latent Space Model" (Preprint), under submission.
• Chuyang Ke, and Jean Honorio, "Information-theoretic Limits for Community Detection in Network Models", Neural Information Processing Systems (NeurIPS), 2018.
• Chester Holtz, Chuyang Ke, and Daniel Gildea, "University of Rochester WMT 2017 NMT System Submission", Conference on Machine Translation (WMT), 2017.
• Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, and Ji Liu, "On The Projection Operator to A Three-view Cardinality Constrained Set", International Conference on Machine Learning (ICML), 2017.
• Chuyang Ke, Yan Jin, Heather Evans, Bill Lober, Xiaoning Qian, Ji Liu, and Shuai Huang, "Prognostics of Surgical Site Infections using Dynamic Health Data", Journal of Biomedical Informatics, 2017.

Teaching

Purdue:

• TA CS 182 Foundations of Computer Science, Spring 2018
• TA CS 182 Foundations of Computer Science, Fall 2017

Rochester:

• TA CSC 240 Data Mining, Spring 2017
• TA MTH 150 Discrete Mathematics, Fall 2015
• TA MTH 150 Discrete Mathematics, Spring 2015
• TA MTH 150 Discrete Mathematics, Fall 2014

Presentations

• Neural Information Processing Systems (NeurIPS), “Information-theoretic Limits for Community Detection in Network Models”, Montreal, Canada, 2018.
• National Conference on Undergraduate Research, “Prognostics of Surgical Site Infections Using Spatial-Temporal Matrix Data with Incomplete Measurements and Censored Outcome”, Asheville, NC, 2016.