Machine Learning Researcher

I am a dedicated and motivated computer science graduate student with a strong academic background and a commitment to continuous growth and development.

I approach every task with responsibility, energy, and enthusiasm, and I always strive to deliver high-quality results. I learn quickly, adapt well to new challenges, and enjoy expanding my skills and knowledge.

My background includes teaching assistance and office assistance in a university setting, where I have supported database-related coursework, including MongoDB lectures, exam evaluation, and assignment review. These experiences strengthened my communication, organization, and collaboration skills.

I have a strong technical foundation in programming, machine learning, deep learning, SQL, MongoDB, and data analysis. My research interests include sequence modeling, time-series anomaly detection, federated learning, and large-scale streaming data mining.

I have also contributed to multiple research projects and publications in areas such as ECG classification, object detection, graph-to-text generation, and anomaly detection. These experiences reflect my interest in applying AI and machine learning to practical and scientific problems.

I am currently based in Chengdu, China, and I am open to opportunities where I can contribute meaningfully, continue learning, and work in dynamic, research-driven environments.