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I am working on physics-informed generative models and physics-informed representation learning, exploring both their theoretical foundations and practical applications, especially in the field of AI for Science and Science for AI. I am a visiting student in the Anima AI+Science lab at the California Institute of Technology, advised by Professor Anima Anandkumar. I received my graduate degree in Applied Mathematics from Fudan University, under the esteemed supervision of the distinguished scholar Prof. Zengfeng Huang. At Fudan University, my work focused on the theory and real-world applications of graph learning and generative models. I was also a research intern at Tencent AI Lab and Shanghai AI Lab.
Feel free to reach out to me if you're interested in discussing research or potential collaborations! Email: yanliangfdu[at]gmail.com. Google Scholar / Github / ORCID / Twitter / LinkedIn
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I am working on generative models, exploring both their theoretical foundations and practical applications. I firmly believe that the boundaries of generative models extend far beyond this. My long-term goal is to develop the next generation of generative models that are more controllable, scalable, efficient, cost-effective, and, most importantly, grounded in first principles. Recently, I have been working on AI for Science and Science for AI, with a emphasis on the development of generative models. I want to have a more principled understanding of generative models from the perspective of science, especially physics and mathematics. My research interests lie in the theoretical understanding of generative models and the development of effective algorithms to address key challenges in science. I love to find a sweet balance between mathmetical theory and practical tricks.
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MGB: The Material Generation Benchmark
Geometric Imbalance in Semi-Supervised Node Classification
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
An Adaptive Autoregressive Diffusion Approach to Design Active Humanized Antibody and Nanobody
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
NeuralCrystal: A Geometric Foundation Model for Crystalline Material Discovery
CrystalFlow: An Equivariant Flow Matching Framework for Learning Molecular Crystallization
Hierarchical Graph Latent Diffusion Model for Conditional Molecule Generation
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
Training Class-Imbalanced Diffusion Model Via Overlap Optimization
Physics Informed Spectral Element Network with Positional Parameter |
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Reviewer: KDD (2023, 2024), ICLR (2024, 2025, 2026), ICML (2024, 2025), NeurIPS (2023, 2024, 2025), ACM MM (2025), ACM MM Datasets Track (2025). ICML LXAI Workshop (2025), ICML AI4Math Workshop (2025), ICML AIW Workshop (2025), ICML DataWorld Workshop (2025), NeurIPS 2025 LXAI Workshop (2025), NeurIPS 2025 VLM4RWD Workshop (2025), |
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Xu Jiang (B.E. from Tsinghua University, 2025.05-Present) |
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I originally had no connection to the field of artificial intelligence. If everything had gone as expected, I might have become a bank manager or an accountant. However, during my undergraduate years, I happened to stumble upon a book on artificial intelligence while wandering through the library. That book left a profound impact on me, and from that moment, I made up my mind to devote myself to this exciting field. This is my origin, the path I started on, and I hope I will never forget the inspiration and determination I felt at the very beginning. I am a fan of the late NBA star Kobe Bryant. He has been a great source of inspiration for me. He once said: "If you love a thing, you will overcome all difficulties." So, the most important thing is to find something you truly love. I hope I already find mine too. RIP, Kobe. |
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Updated at Jan. 2026
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