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Email: x@y with x=fanghui.liu, y=sjtu.edu.cn or warwick.ac.uk

We're organising a workshop on Mathematical Foundations of Modern Machine Learning at Peking University!

We're organising IJCV Special Issue on Post-Training in Large Language Models for Computer Vision!

Research Interests

I'm generally interested in foundations of modern machine learning from the lens of learning efficiency, both theoretically and empirically. My research is always contributing to how to handle nonlinearity at a theoretical level and how to precisely and efficiently approximate nonlinearity at a practical level under theoretical guidelines, which is a longstanding question over science, technology and engineering.

My research (the past, ongoing and future) focuses on the following directions:

• machine learning theory: what is the largest function space that can be learned by neural networks, both statistically and computationally efficiently (computational-statistical gaps)?

• post-training in LLMs: expand the frontiers of empirical and theoretical knowledge on when and where to fine-tuneinference, and how much we can fine-tuneinference, precisely, efficiently, and robustly

• AI4MLT: AI for machine learning theory, lean formalisation

At SJTU, see more details in my homepage (Chinese version).

I will not take any new students at Warwick. Warwick has a strong research direction on TCS, mathematics, and statistics. Feel free to reach out to my previous colleagues at Warwick.

Due to a large number of requests, I, unfortunately, may not be able to reply to all the emails regarding PhD applications. However, I'll look at applicants that have sent me emails and contact you soon if your enquires are indeed inline with my research.

Visitors

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