I'm a Ph.D. student at the University of Washington, advised by Professor Dongfang Zhao. My research focuses on the intersection of database systems and data retrieval. I develop efficient indexing techniques to optimize data retrieval strategies in large-scale vector databases.
Research Areas: Database Systems, Large-Scale Data Management, Information Retrieval, Indexing Techniques
Current Research
I am currently researching advanced indexing and search techniques.
This research introduces VecLSTM, a hybrid framework that enhances trajectory prediction by integrating vectorization techniques and a CNN-LSTM architecture. It efficiently processes trajectory data, significantly improving both accuracy and training time over traditional LSTM models.
Efficient Feature Extraction for Image Analysis through Adaptive Caching in Vector Databases
This research introduces a caching subsystem leveraging in-memory vector databases to enhance the efficiency of image feature extraction. Using advanced models like MobileNetV3 and ResNet50, the framework integrates batch insertion and parallel processing to optimize computational performance.
This research focused on using deep neural networks and hierarchical clustering to predict chaotic transitions in natural convection systems, specifically in the Lorenz system.
Education
I received my second Master’s degree in Information Technology Infrastructure from Illinois Institute of Technology, Chicago, with a 4.0 GPA. I also received my first Master’s degree in Data Science, where my thesis focused on providing a model for optimizing customer value at the contact center. Additionally, I received a Bachelor's degree in Computer Software Engineering.