My research interest lies primarily in the fields of extracting representative features to help understand the mechanism of learning and get inference better with more emphasis on time series data. I believe there is passing through information, and I could find it from partial data information with data distribution changing. Here are some of my interests:

• Extract key features under data distribution changing environment
• Understand the causation and relation in variables of time series data
• Adapt Federated learning to overcome data imbalance in time series data

Research

International Conference

Feature Expansion of Single Dimensional Time Series Data for Machine Learning Classification	1st author
Traffic Data Classification using Machine Learning Algorithms in SDN Networks	2nd author
An Iterative Algorithm of Key Feature Selection for Multi-class Classification	1st author

Domestic Conference

Impact of Input Data Randomness on Training Performance of Autoencoder (Best Awards)	2nd author
On Improving Network Data Anomaly Detection Performance based on Meta Characteristics	2nd author
Machine Learning based Algorithm for Small Amount Multi-featured Data in Three Classes	1st author
Study on Impact of Class Combinations on Performance of Multiple Class Classification	1st author

Domestic Journal Articles

Survey on Machine Learning Algorithms for SDN/NFV Automation

2nd author

Experience

University of Maryland, College Park | Auc 2022-May 2023

• Teaching Assistanct
• Advanced Data Structures(CMSC 420), Discrete Structures(CMSC 250)
  • Conducted discussion session to promote the understanding to implement the algorithm.

Carnegie Mellon University | Jan 2020-Jul 2020

• Visiting Scholar
• Full Scholarship for Intensive AI Program in CMU under the Ministry of Science and ICT
  • Developed a general model for chatbot with profoundly experienced in Natural language Processing (NLP)
  • Managed team and established architecture for a chatbot that satisfied user requirements
  • Processed with large-scale multimedia data to generate Generative Adversarial Network using AWS
  • Experienced in recommendation algorithms by applying text mining-related theories such as collaborate filtering and content-based filtering