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A solution architect with a strong background in mathematics and statistics, specializing in AI-assisted network management and AIOps. Experienced in developing RAG-based knowledge systems, real-time anomaly detection, and explainable AI models for telecommunications. Proficient in deep learning, self-supervised representation learning, and building scalable AI prototypes.
Skills
Experience
Solution Architect, AIOps & Network AI — Nokia
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Serves as the technical bridge between customers and R&D for agentic AIOps, defining operational use cases and validating AI behaviors.
Network Engineer, AI/ML Systems — Nokia
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Developed a RAG-based product knowledge system and an AI prototype for capacity intelligence using large-scale network telemetry data.
Master Thesis Student, Trustworthy / Explainable AI — Ericsson
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Conducted research on generating interpretable rule-based explanations for black-box machine learning models applied to telecom HTTP-delay prediction.
Featured projects
Siamese Masked Autoencoder (SiamMAE) Reproduction
PyTorch
Reproduced SiamMAE to study self-supervised representation learning from video using UCF-101 and DAVIS-2017 datasets.
PyTorch
Implemented various deep learning techniques including SimCLR, FixMatch, Grad-CAM, and integrated gradients.
Camera-Based Self-Driving Robot
PyTorch · OpenCV · ROS
Developed a lane-following system using visual input and ROS-based robot control.
Education
National Yang Ming Chiao Tung University — M.S., Statistics
KTH Royal Institute of Technology — Exchange Program, Computer Science
National Tsing Hua University — B.S., Mathematics
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