Andi Han
Incoming Lecturer (Assistant Professor) at USYD
Postdoctoral Researcher at RIKEN AIP
PhD, USYD | 2023
Email: andi[dot]han[at]riken[dot]jp
jm3andy[at]gmail[dot]com
Github | Google Scholar | LinkedIn
About Me
Hi! I'm currently a Postdoctoral Researcher at RIKEN AIP, Continuous Optimization Team, under the supervision of Prof. Akiko Takeda. I completed my PhD
in Business Analytics at USYD, where I was advised by Prof. Junbin Gao.
My research broadly covers optimization (on manifolds), theory for large foundation models,
efficiency in machine learning and
graph neural networks.
I will be joining University of Sydney as a Lecturer (equiv. Assistant Professor) in the School of Mathematics and Statistics in May 2025!
If you would like to conduct research with me, feel free to reach out via email!
News
- [2025.01] Four papers on Diffusion model feature learning, Transformer optimization, DMD for GNN, GNN hyperparameter tuning with diffusion model accpeted to ICLR 2025.
- [2024.12] We are organizing a workshop Deep Generative Model in Machine Learning: Theory, Principle and Efficacy at ICLR 2025! See more details on Call for papers!
- [2024.11] Created a GitHub repo on Riemannian optimization, compiling key papers, books, and resources.
- [2024.10] One paper on Protein sequence generation accpeted to IEEE BIBM 2024.
- [2024.09] Four papers on Riemannian bilevel optimization, Parameter and memory efficient pretraining, Theoretical comparisons between single and multi-modal contrastive learning, In context learning with multi-concept word semantics accpeted to NeurIPS 2024.