About pin-ying
I am a Machine Learning Engineer at TSMC, specializing in LLM and VLM solutions to enhance enterprise decision-making. My expertise spans Generative AI, Computer Vision, and Deep Learning. I independently developed a high-performing internal pricing decision chatbot leveraging RAG and Chain-of-Thought reasoning. Currently, I contribute to a large-scale multimodal agentic system, owning a module that consolidates diverse data sources to enhance decision reliability. I hold an M.S. in Electrical and Computer Engineering from the University of California, San Diego (UCSD), and a B.S. in Electrical Engineering from National Taiwan University (NTU). Beyond industry experience, I have conducted research in 3D Visual Question Answering with Prof. Nuno Vasconcelos at the Statistical Visual Computing Lab (UCSD), and in Audio-Visual Learning with Prof. Yu-Chiang Frank Wang at the Vision and Learning Lab (NTU).
Key Skills
Experience
Machine Learning Engineer
CurrentTsmc
Developed a LLM-powered chatbot leveraging RAG and CoT techniques for internal pricing decisions, and independently delivered a prototype with 92.1% recall, significantly improving decision-making efficiency and accuracy. - Contributing to a large-scale organization-wide VLM agentic system, owning the multimodal refinement module that consolidates information from diverse data sources, resolves conflicts, and boosts decision reliability. - Collaborating with cross-functional stakeholders to shape project requirements, manage technical risks, and align VLM-based solutions with strategic business goals, leveraging strong technical expertise and clear communication.
Graduate Research Intern, Statistical Visual Computing Lab
Uc San Diego Jacobs School Of Engineering
Designed and deployed a data pipeline to collect 100,000 question-answer pairs on safety-related reasoning in 3D scenes, combining GPT-based synthesis for simple cases with MTurk for complex, human-generated examples.
Undergraduate Researcher, Vision And Learning Lab
National Taiwan University
Developed an Audio-Visual Transformers model to learn cross-modal contextual features for locating sounding sources in an image, and conducted thorough experimental studies with the MIT-MUSIC dataset. - Addressed the fully unsupervised challenge by designing a self-supervised training framework with separate CNNs for visual and audio modalities, incorporating STFT to extract sequential audio features.
Education
Uc San Diego
Master Of Science
National Taiwan University
Bachelor Of Science
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Common Questions
What is pin-ying's expertise?
pin-ying specializes in Machine Learning Engineer, with expertise in amazon ec2, amazon web services, c++, computer vision, deep learning.
Where is pin-ying located?
pin-ying is based in san jose, california, united states.
How much experience does pin-ying have?
pin-ying has 6+ years of professional experience.
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