Ye (Henry) Mao

Biography

I am currently a second-year PhD candidate specializing in Computer Vision and Machine Learning at MatchLab, Imperial College London. My research is supervised by supported by Prof. Krystian Mikolajczyk and funded by the Imperial President's Scholarship. Research Interest: 3D Scene Understanding, Vision-Language Model, and Open-Vocabularly Learning.

Prior to my PhD journey, I completed an MPhil in Medical Sciences at the University of Cambridge and an MSc in Applied Machine Learning at Imperial College London. My undergraduate studies were in Computer Science at King's College London.

Education

• PhD (Probationary) in Computer Vision and Machine Learning, Imperial College London, 2023-now
• MPhil in Medical Sciences, University of Cambridge, 2022-2023
  • Thesis: Brain MRI Super-Resolution using Conditional Diffusion Model (Pass with Minor Revision)
• MSc in Applied Machine Learning - First Class Honours (Top 1 Grade: 81%), Imperial College London, 2021-2022
  • Thesis: Data-Driven Multiplexing using Real-Time Digital PCR (Top 1 Grade: 87%)
• BSc in Computer Science - First Class Honours (Top 1 Grade: 82%), King's College London, 2018-2021
  • Thesis: String Sanitisation Algorithm Development (Top 1 Grade: 81%)

Publications

	Hypo3D: Exploring Hypothetical Reasoning in 3D Ye Mao, Weixun Luo, Junpeng Jing, Anlan Qiu, and Krystian Mikolajczyk Arxiv [Project Page] [Arxiv] [Code]
	OpenDlign: Enhancing Open-World 3D Learning with Depth-Aligned Images Ye Mao, Junpeng Jing, and Krystian Mikolajczyk NeurIPS 2024. [Project Page] [NeurIPS] [Code]
	Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching Junpeng Jing, Ye Mao, and Krystian Mikolajczyk ECCV 2024. [Project Page] [ECCV] [Code]
	DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution Ye Mao, Lan Jiang, Xi Chen, and Chao Li MICCAI 2023. [Project Page] [MICCAI] [Code]
	CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis Lan Jiang, Ye Mao, Xi Chen, and Chao Li MICCAI 2023. [Project Page] [MICCAI] [Code]
	Deep Domain Adaptation Enhances Amplification Curve Analysis for Single-Channel Multiplexing in Real-Time PCR Ye Mao, Ke Xu, Luca Miglietta, Louis Kreitmann, Nicolas Moser, Pantelis Georgiou, Alison Holmes, and Jesus Rodriguez-Manzano IEEE Journal of Biomedical and Health Informatics. [IEEE JBHI]

Academic Reviewer Experience

• ECCV 2024, MICCAI 2024, NeurIPS 2024, ICLR 2025, CVPR 2025, ICML 2025, IEEE Transactions on Medical Imaging

Selected Honors & Awards

• Imperial President’s PhD Scholarship – Among top 50 selected college-wide, 2023.
• Applied Machine Learning Prize – Prize for the Highest Overall Grade in MSc. Applied Machine Learning, 2022.
• Hertha Ayrton Centenary Prize – Prize for the Highest Final Year Project Grade in the Imperial EEE department, 2022.
• Robotics Prize – Prize for the Highest Overall Grade in BSc. Computer Science and Robotics, 2021.
• Peplow Prize – Prize for the Highest Final Year Project Grade in the King’s Informatics department, 2021.

Teaching

• Deep Learning (ELEC60009), Computer Vision and Pattern Recognition (ELEC70073) GTA, Imperial, 2024-Present
• Foundation of Computing, Robotics Group Project, UTA, KCL, 2021

Research

• Research Assistant at Centre for Antimicrobial Optimisation Lab, Imperial, 2024-Present
• Research Assistant at Cambridge Brain Tumour Imaging Lab, Cambridge, 2023.01-2023.09

Professional Skills

• Tools: Python, PyTorch, TensorFlow, C++, Java, OpenCV, Git, LaTex.
• Language: Mandarin (Native), English (Fluent).