My name is Hong Ye, and I am a Hedrick Assistant Adjunct Professsor at UCLA, specializing in Computational and Applied Mathematics, hosted by Prof. Stanley Osher. I was previously a PhD student in the Cambridge Image Analysis group, supervised by Prof. Carola-Bibiane Schönlieb, Prof. Subhadip Mukherjee and Prof. Junqi Tang, funded by GSK.ai.

What do I work on?

My current focus is on machine learning theory, such as the manifold hypothesis and links with learning theory, and generally why machine learning works as a function of problem, data, optimizer and network. I aim to answer: what do we need to assume to make machine learning work?

My previous work addresses a similar question: how can we exploit problem structures to make machine learning work? We combine theoretical guarantees with practical applications, developing provable ML-based algorithms using classical mathematics as a skeleton. This includes optimization (convex learning-to-optimize, Plug-and-Play, inverse problems), MCMC sampling (noise-free methods, unsupervised learning/ imaging), and differential geometry (manifold hypothesis, intrinsic dataset complexity).

Other tidbits

• I was the youngest member admitted to the University of Hong Kong in 2015 (aged 11). I am also probably the youngest member (aged 13) admitted to Cambridge in recent history, but I have not verified this. I am now 21, if you are interested.

• I do all the coding and 98% of the writing for my first-author papers. Feel free to use the code from any of my work, and do send me an email if you have any questions or want access to updated code.

• I am an alumnus of the Masason Foundation.

• My thesis can be found here, where I passed with no corrections.