I am currently a second-year Master’s student in Applied Mathematics at Fudan University. My research focuses on artificial intelligence and machine learning, with particular emphasis on probability distribution modeling, generative models, multimodal representation learning, as well as the attack and defense of foundation models. I will apply for a PhD in Fall 2026 and seek advisors and collaborators with a mathematics background who share an interest in AI. I believe mathematics is the key to solving fundamental AI challenges.
Feel free to reach out if you’d like to learn more about my work, chat, or explore potential collaborations.
🔥 News
- 2025.01: 🎉 MVP is accepted by ICLR 2025. See you in Singapore!
- 2024.05: 🎉 evi-CEM is accepted by MICCAI 2024. Congrats to Yibo.
📝 Publications
MVP: Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs
Xin Gao, Jian Pu
- MVP introduces a novel approach to incomplete multi-view representation learning by leveraging latent space correspondences in Variational Auto-Encoders, enabling the inference of missing views and enhancing the consistency of multi-view data even with irregularly missing information.
Yibo Gao, Zheyao Gao, Xin Gao, Yuanye Liu, Bomin Wang, Xiahai Zhuang
- evi-CEM introduces an evidential learning approach to enhance the reliability of concept predictions in Concept Bottleneck Models, addressing concept misalignments in medical image analysis and improving performance in both supervised and label-efficient settings.
🎖 Honors and Awards
- 2023.09 Fudan University Zhicheng Freshman Second Prize Scholarship (Top 5%)
- 2023.06 Outstanding Graduate of Shanghai
- 2022.11 Second Prize in the Chinese Mathematics Competitions (Category A)
- 2021.12 National Scholarship, China
- 2021.09 National Second Prize in the China Undergraduate Mathematical Contest in Modeling
- 2020.12 Shanghai Scholarship
📖 Educations
- 2023.09 - 2026.06 (now), Master of Applied Mathematics, Fudan University, Shanghai, China.
- 2019.09 - 2023.06, Bachelor of Mathematics, Donghua Univeristy, Shanghai, China.
💻 Internships
- 2023.03 - 2023.07, Winning AI Lab, Shanghai, China.
📚 Learning Materials
😁 If you want the following material without watermarks, please contact me using the email address and specify your intended use.
Material 1: Frontiers in Diffusion Model Technologies (1)
This document provides an overview of key concepts related to diffusion models, particularly focusing on the theoretical foundations, development timeline, and recent advancements in the field. The content includes detailed discussions on VAE, DDPM, DDIM, SDE, and ODE, as well as conditional guidance. It also covers the evolution of stable diffusion, including topics like Latent Diffusion, VQ-VAE, and DiT. Lastly, the document highlights the latest methodology, IC-Light, set to be presented at ICLR 2025.
Material 2: Tutorial of Information Geometry and t3-VAE
This document introduces the t3-Variational Autoencoder (ICLR 2024), which uses Student’s t-distributions to model heavy-tailed data distributions and improve latent variable representations. It also explores the framework of Information Geometry, focusing on how generative models can be understood through statistical manifolds, divergences, and Riemannian metrics, providing a deeper understanding of probability distributions and their applications in machine learning, signal processing, and neuroscience.
Material 3: EM Algorithm and X-metric
This document introduces the X-metric framework (PAMI 2023), an N-dimensional information-theoretic approach designed for groupwise registration and deep combined computing, with applications in advanced machine learning tasks. It also covers the theoretical foundations, including entropy, mutual information, and the MLE algorithm, alongside the framework’s modifications for deep computing and network training.