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 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.