Dr Ye Pu
Senior Lecturer
Department of Electrical and Electronic Engineering
142 Scholarly works
4 Projects
HIGHLIGHTS
2026
Conference Proceedings
High efficiency light engine for holographic tomographic volumetric additive manufacturing using phase light modulator (PLM)
DOI: 10.1117/12.30999502026
Conference Proceedings
Fuorescence lifetime micro-endoscopy (FLIMME) via single multimode fiber: a high-resolution tool for identifying head and neck cancer margins
DOI: 10.1117/12.30994792026
Conference Proceedings
Hybrid two-photon polymerization and single-photon tomographic volumetric additive manufacturing
DOI: 10.1117/12.30989952026
Journal article
High-efficiency multi-scale holographic volumetric 3D printing with a phase light modulator.
DOI: 10.1038/s41377-026-02331-42022
Research grants (other domestic)
Real-Time Control With Safety Guarantees: Theory and Applications
2021
Research grants (ARC, NHMRC, MRFF)
Digitally Networked Dynamical Systems: Performance and Robustness Analysis
2020
Research Contracts
Mobile Robots for Conveyor Carryback Removal
RECENT SCHOLARLY WORKS
2025
Journal article
Koopman-based predictive tracking control
DOI: 10.1016/j.engappai.2025.1113492025
Journal article
Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-Synthesized Images
DOI: 10.1109/LRA.2025.35898052025
Journal article
Input-mapping based data-driven model predictive control for unknown linear systems via online learning
DOI: 10.1002/rnc.62372025
Conference Proceedings
Implementation of a micro electromechanical system (MEMS) phase-only light modulator (PLM) for holographic volumetric additive manufacturing
DOI: 10.1117/12.30430692025
Conference Proceedings
Fluorescence lifetime endo-microscopy: identifying margins of head and neck cancer tissues through multimode fiber endoscopy and machine learning
DOI: 10.1117/12.30418742025
Journal article
Nonlinearity in thermal comfort-based control systems: A systematic review
DOI: 10.1016/j.enbuild.2024.115060
RECENT PROJECTS
2026
Research grants (ARC, NHMRC, MRFF)
Models Meet Data: Accelerating Safe Learning and Optimization in Robots