Ph.D.
Identifying where lab-validated ML breaks in the field.
After 10 years of working with real-world data and use scenarios, I am now looking for opportunities to take a step closer to the deployment side.
With a focus on computer vision, audio and machine learning, I have been working on identifying when, where and how ML models/frameworks would fail when applied to real-world data and deployment constraints across my work prior to and during my PhD at Ghent University-imec. Years of work have led to recognition including 13 peer-reviewed publications (7 journal papers, ICIP oral), 150+ citations, an APICTA Award, and a Top 3% Master’s graduate honor. After my PhD, I extended this work to VLM quantization for edge-cloud deployment with Qualcomm AI Hub, as well as synthetic data generation to bridge cross-site domain shift.
After closing my chapter in Belgium, I returned to Taiwan in late August and am now actively looking for new opportunities.
- Deep Learning
- Computer Vision
- Audio Privacy
PhD in Computer Science Engineering
Ghent University, Belgium
MSc in Computer and Communication Engineering
National Cheng Kung University, Taiwan
BSc in Electrical Engineering
National Cheng Kung University, Taiwan
- 2026 to now
ResearchEdge-Cloud VLM: vision encoder quantization for on-device deployment
Quantizing a VLM vision encoder for a Qualcomm QCS8550 NPU. Eleven configurations evaluated on GPU, four actually executable on target. The paper is about where evaluation breaks between development and deployment. Manuscript to be submitted.
- 2026.06
ProposalGoogle TPU Research Cloud proposal, under review
Reality-Bounded Synthetic Data via Decoupling and Recombination: extending the PhD line to cross-environment event and behaviour transfer, and person-level stress-testing for deepfake detection.
- 2026.01
BuildMulti-agent LLM orchestration PoC
Cost-efficient routing, context isolation, and model gating, validated on a CPU/API-only setup before committing to GPU-heavy compute.
- 2025.11
MilestonePh.D. conferred by Ghent University-imec
From Lab to Street: transferable and privacy-friendly deep learning for urban surveillance, built on 5000+ hours of uncurated real-world audio.
- 2025.06
PaperSource-free model transferability assessment
Published in MDPI Sensors: ranking model transferability for smart surveillance without access to source data.
- 2025.01
PaperEmbedding-based pair generation for contrastive learning
Published in Frontiers in Robotics and AI: audio-visual representation learning on real surveillance streams.
