2021/09 — 2025/06
National Cheng Kung University
Department of Statistics
Graduate Student in Computer Science
Shou-Wei Tsai (蔡劭威)
Graduated with a background in Statistics and have spent the past years strengthening my skills in data analysis, machine learning, sensing peripheral devices (FPGA/Raspberry Pi) and applied computational research. My interests lie in combining statistical reasoning with software and hardware understanding to solve real-world problems.
Education
Academic Background
2025/09 — 2026/07
National Taiwan University of Science and Technology
Department of Computer Science and Information Engineering
2026/08 — Present
National Yang Ming Chiao Tung University
Department of Computer Science
Research experience
Areas of Study
My academic path spans statistics, statistical signal processing, machine learning, and hardware-oriented computing. I have worked with point clouds, probabilistic methods, denoising strategies, and large-scale data analysis, while also exploring embedded systems and digital design.
Research Focus
Projects
Selected Work
Point Clouds
3D Point Cloud Registration on Forest Datasets
Studied registration pipelines for TLS and ULS datasets, focusing on robustness under noisy and partially overlapping point sets.
Probabilistic Methods
Markov Chain Based Outlier Denoising
Investigated probabilistic self-update strategies to improve inlier recovery and reduce the impact of outliers.
Implementation
Algorithms and Data Analysis Projects
Implemented and analyzed KNN, K-means, DBSCAN, decision trees, and related methods for data exploration and evaluation.
Systems
Raspberry Pi 5 Embedded Sensing System
Built a real-time vibration and FFT analysis setup using sensors, Linux-based control, and signal processing pipelines.
FPGA
FPGA / Verilog / VHDL Coursework
Worked on datapath design, RTL simulation, and hardware verification for processor and logic-focused projects.
NLP & vision
Hate Speech Detection and Disaster Prediction with NLP
Explored text classification and prediction tasks using data-driven methods and applied modeling workflows.
Contact
Contact Information
Welcome for research collaboration, applied data projects, and opportunities related to AI, statistics, and intelligent systems.