My research interests focus on federated and adaptive machine learning, with an emphasis on understanding how models learn, reason, and update their knowledge under data heterogeneity and distribution shifts. I am particularly interested in settings where data are non-IID, partially observed, and dynamically evolving, and how learning systems can remain reliable in such environments.

My current research interests include:

• Learning representative and decision-oriented features under data imbalance and distribution shifts
• Understanding and improving reasoning behavior in learning systems across heterogeneous clients
• Developing unlearning-aware federated learning algorithms to handle outdated, biased, or harmful information

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 Paper Award)	2nd author
On Improving Network Data Anomaly Detection Performance based on Meta Characteristics	2nd author
Machine Learning-based Algorithm for Small-scale Multi-featured Data Classification	1st author
Study on Impact of Class Combinations on Performance of Multi-class Classification	1st author

Domestic Journal Articles

Survey on Machine Learning Algorithms for SDN/NFV Automation

2nd author

Experience

University of Maryland, College Park | Aug. 2022 – May 2023

• Teaching Assistant
• Advanced Data Structures (CMSC 420), Discrete Structures (CMSC 250)
  • Conducted discussion sessions to enhance students’ understanding of algorithm design and implementation.

Carnegie Mellon University | Jan. 2020 – Jul. 2020

• Visiting Scholar
• Full scholarship recipient for the Intensive AI Program at CMU under the Ministry of Science and ICT
  • Developed a general-purpose chatbot model with a strong focus on Natural Language Processing (NLP)
  • Led a team and designed the system architecture to meet user requirements
  • Processed large-scale multimedia data and implemented Generative Adversarial Networks (GANs) using AWS
  • Gained experience in recommendation algorithms, including collaborative filtering and content-based filtering