I am a Ph.D student at School of Computing and Information (SCI), University of Pittsburgh. My major is Information Science and Technology. Currently, I work in PITT Computational Social Dynamics Lab (PICSO LAB) which is directed by Dr. Yu-Ru Lin. I am interested in machine learning, deep learning, data science and network science. With the escalating amount of heterogeneous, multi-sourced and time-varying datasets, I explore intelligent and computational methods to characterize underlying patterns, identify anomalous phenomena, and to forecast future’s evolutions. If you are interested in my research, please visit Research and Publication.
- 03/2019 I pass my PhD comprehensive exam -- a milestone in my doctoral program.
- 02/2019 I will do 2019 Summer Internship at Facebook (Menlo Park, CA).
- 07/2018 Our paper Deep into Hypersphere: Robust and Unsupervised Anomaly Detection in Dynamic Networks is accepted for inclusion in the IJCAI 2018. [Codes] [Slides]
- 06/2018 Received a GHC Student Scholarship to attend the 2018 Grace Hopper Celebration in Houston, Texas.
- 06/2018 Our CI-TM algorithm code is published now at github for the convenience of whom might need it. It is proposed in our paper Efficient collective influence maximization in cascading processes with first-order transitions.
- 02/2018 I will do 2018 Summer Internship at Samsung Research (Mountain View, CA).
- 08/2017 I received student travel grants from NSF & SIGWEB as well as SIGIR to attend CIKM 2017 in Singapore.
- 08/2017 Our paper Anomaly detection in dynamic networks using multi-view time-series hypersphere learning is accepted for inclusion in the CIKM 2017 conference. [Codes] [Slides].
- 03/2017 Our paper Efficient collective influence maximization in cascading processes with first-order transitions is published in Scientific Reports. We develop a new algorithm called "CI-TM" (codes available at github), which can quickly spot influential spreaders to promote information diffusion in networks.
- 10/2016 Our paper Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks is published in Scientific Reports.