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I am a software engineer at Google, currently working on on-device ML and LLM applications. My experience in Machine Learning spans from academic research during my undergraduate and master's studies to real-world applications in my current role. I've published two papers in CVPR 2022 and CVPR 2024, contributing to the field of computer vision. During my Master's program, I was honored with the Presidential Award for Graduate Students, granted to the top 1% of students. |
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2024/07 - Current Google - Software Engineer Team: On-device ML/LLM Application |
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2022/09 - 2024/06 NTU - M.S. Department of CSIE CMLab (Advisor: Yung-Yu Chuang) VLLab (Co-advisor: Yen-Yu Lin) Thesis: Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation (CVPR 2024) |
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2023/07 - 2023/11 Google - Software Engineering Intern Team: Pixel Camera System Software |
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2022/07 - 2023/06 Asus - Software Engineering Intern AISVision Department |
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2018/09 - 2022/06 NYCU - B.S. Department of Computer Science Paper: An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation (CVPR 2022) |
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Ji-Jia Wu, Andy Chia-Hao Chang, Chieh-Yu Chuang, Chun-Pei Chen, Yu-Lun Liu, Min-Hung Chen, Hou-Ning Hu, Yung-Yu Chuang, Yen-Yu Lin CVPR, 2024 arXiv / code This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only imagetext pairs without dense annotations. |
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Cheng-Kun Yang, Ji-Jia Wu, Kai-Syun Chen, Yung-Yu Chuang, and Yen-Yu Lin CVPR, 2022 paper / code This paper addresses weakly supervised point cloud segmentation, aiming to learn a segmentation model capable with only 0.02% of label required. |
| (Spring 2024 - NYCU) | Pattern Recognition |
| (Fall 2023 - NYCU) | Introduction to Machine Learning |
| (Spring 2023 - NYCU) | Pattern Recognition |
| (Fall 2022 - NYCU) | Introduction to Machine Learning |
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The template is stolen from
Yu-Lun Liu's
website.
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