Welcome to my GitHub page. I'm a PhD in Big Data Analytics. My research interest includes learning representation on graph structured data, anomaly detection, and dimensionality reduction.
(Click to expand)
- June 2022: Presented the work of applying graph-based machine learning algorithms to the study of social network at a full-day workshop of the 16th International AAAI Conference on Web and Social Media (ICWSM 2022)
- May 2022: Presented the work of simplifying inference process for link prediction task at the 6th International Conference on Information System and Data Mining conference (ICISDM 2022)
- May 2022: First PhD in Big Data Analytics from UCF .
- September 2021: ICUFN 2021 proceedings published.
- August 2021: Presented the work of validating active learning practices for graph neural networks at the 12th International Conference on Ubiquitous and Future Networks (ICUFN 2021) .
- April 2021: Journal paper published exploring the connection between network topology and active learning in graph neural networks.
- March 2021: Won 1st place in 2021 OUC Data Science Competition focused on Identifying electricity theft.
- October 2020: Journal paper published examining flexible penalization scheme to simplify graph neural networks and improve interpretability of the result.
- March 2019: Awarded Microsoft Scholarship for genotyping data-intensive research project.
- August 2018: Awarded Graduate Dean’s Fellowship.
- August 2018: Started PhD program.
- May 2018: Graduated master program.
- March 2017: Won 1st place in Touring Plans’ Big Data Challenge on Predicting wait times for multiple Walt Disney World attractions.
For more updates, please visit my personal page.
- Regularized Simple Graph Convolution (SGC) for improved interpretability of large datasets: We propose a flexible penalization scheme on the SGC to simplify the inference process and facilitate investigation of important features for node classification task
- Exploring the Value of Nodes with Multicommunity Membership for Classification with Graph Convolutional Neural Networks: We investigate optimal sampling strategies to improve the efficiency of GNNs.
- Exploring a link between network topology and active learning : We conduct simulation study on sampling process and propose indicative guildlines to select optimal sampling strategy for the SGC
- Link prediction with Simple Graph Convolution and regularized Simple Graph Convolution: We adopt the regularized SGC for link prediction task
- Exploring the Regularized SGC in application to social network data: We conduct simulation study to validate the predictive capability of the proposed regularized SGC under various network topologies.
- Doctor of Philosophy in Big Data Analytics, University of Central Florida (2018 - 2022)
- Dissertation: "Graph Neural Networks for Improved Interpretability and Efficiency"
- Master of Science in Statistical Computing, University of Central Florida (2016 - 2018)
- Research project: "Comparision of anomaly detection algorithms SVDD, OCSTM, LS-OCSTM, STDD, LS-STDD"
