My name is Jiasen Yang and I am a fifth-year Ph.D. student in the Department of Statistics at Purdue University, advised by Jennifer Neville.
I also work closely with Vinayak Rao.
I obtained a B.S. degree in Statistics from the Special Class for the Gifted Young at the University of Science and Technology of China in July 2013, and an M.S. degree in Statistics and Computer Science from Purdue University in May 2015.
My primary research interests lie in the areas of machine learning and statistical network analysis.
In particular, my research focuses on developing mathematical and statistical models of large-scale network data, and in the past I have worked on point-process models for modeling dynamic networks, graph sampling algorithms, and ensemble methods for collective classification.
I am also interested in approximate Bayesian inference (MCMC and variational inference), graphons, and randomized algorithms for numerical linear algebra.
Jiasen Yang, Vinayak Rao, and Jennifer Neville. Decoupling Homophily and Reciprocity with Latent Space Network Models. In Proceedings of The 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017.
Jiasen Yang, Bruno Ribeiro, and Jennifer Neville. Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls. In Proceedings of The 7th International International Workshop on Statistical Relational AI (StarAI), 2017.
Jiasen Yang, Bruno Ribeiro, and Jennifer Neville. Should We Be Confident in Peer Effects Estimated From Partial Crawls of Social Networks? In Proceedings of The 11th International AAAI Conference on Web and Social Media (ICWSM), 2017.