Yueh-Po Peng

Experience

AI Engineer Oct 2024 – Present

Gate.io Taipei, Taiwan (Remote)

• Developed a fund flows anomaly detection system with LLMs and tree-based models, enhancing financial security.
• Built a Text-to-SQL system to streamline internal data queries, improving query efficiency by 20%.

Research Assistant Mar 2022 – Oct 2024

Institute of Information Science, Academia Sinica | MCTLAB | Supervisor: Dr. Li Su Taipei, Taiwan

Research Topics: Self‑Supervised Learning, Medical Imaging

• Proposed a Transformer-based self-supervised learning method for decoding brain signals (fMRI), achieving an 77% reduction in memory footprint.
• Conducted distributed training experiments on high-resolution 4D medical images (fMRI) using TWCC HPC.
• Proposed a whole-brain feature selection method for decoding musical pitch from fMRI [2].

AI Engineer Intern Mar 2023 – Jul 2024

Tomofun - World's leading pet technology company Taipei, Taiwan

Research Topics: Computer Vision, Large Language Models, Multimodal Learning

• Developed an automatic short music video generation system for daily pet clips.
• Fine-tuned visual language models (e.g., BLIP), achieving a 20.6% improvement in visual question answering.
• Enhanced LLaVA image inference speed by 250% with only a 3% accuracy reduction.
• Developed APIs for visual language models using llama.cpp/ollama for image-caption pair datasets.

Education

National Taiwan University Feb 2023 – Jun 2024

Taipei, Taiwan

• M.S. in Data Science
• Thesis topic: Whole‑Brain Feature Selection Methods for Decoding from fMRI Data

National Taiwan University Sep 2019 – Jan 2022

Taipei, Taiwan

• B.S. in Computer Science and Information Engineering (CSIE)

Research & Projects

Guitar Effect Removal Collaboration with Positive Grid ML Team

• Proposed a two-stage method to remove distortion effects from guitar recordings using Positive Grid VST plugins.
• Achieved 20% higher audio quality than the best baseline, rated by 26 professional guitarists.
• Published in DAFx 2024 [1].

Whole Brain fMRI Feature Selection

• Proposed a two-stage method to extract fMRI features and predict musical pitch.
• Demonstrated 2-fold improvement over ROI-based feature selection in fMRI-music analysis.
• Published in ICASSP 2023 [2].

Publications

[1] Lee, Y. S.*, Peng, Y. P.*, Wu, J. T., Cheng, M., Su, L., & Yang, Y. H. "Distortion Recovery: A Two-Stage Method for Guitar Effect Removal," Proc. Int. Conf. Digital Audio Effects 2024 (DAFx’24). (* equally contributed) Paper | Demo

[2] Cheung, V. K.*, Peng, Y. P.*, Lin, J. H., & Su, L. "Decoding Musical Pitch from Human Brain Activity with Automatic Voxel-Wise Whole-Brain FMRI Feature Selection," Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing 2023 (ICASSP’23). (* equally contributed) Paper

Skills

• Languages/Frameworks: Python, PyTorch, TensorFlow, Pandas, Scikit-learn, Slurm, Go, HTML, JavaScript, C++, C, Linux.
• Skillset: Self-Supervised Learning, Medical Imaging, Computer Vision, Music Information Research, Distributed Training.