Welcome to the website of the Learning and INference Systems (LINs) Lab at Westlake University!
Our research interests lie in the intersection of optimization and generalization for deep learning:
- leveraging theoretical/empirical understanding (e.g., loss landscape, and training dynamics)
- to design efficient & robust methods (both learning and inference)
- for deep learning (centralized) and collaborative deep learning (distributed and/or decentralized),
- under imperfect environments (e.g., noisy, heterogeneous, and hardware-constrained).
Lab activities:
- We are running a research seminar on Deep Learning and Optimization.
News
| Feb 21, 2026 |
Several papers from our group were accepted at this year’s CVPR 2026 conference.
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| Jan 26, 2026 | Several papers from our group were accepted at this year’s ICLR 2026 conference. Congratulations to our PhD student Peng SUN, Zhenglin CHENG, and Siyuan LU, and our internship student Fulin LIN. |
| Dec 9, 2025 | We are excited to release TwinFlow (arxiv and code), a simple, flexible, and memory-efficient framework for one-step generation on large-scale models. The project has already garnered 200+ GitHub stars in a few days! |
| Sep 18, 2025 | Our CPathAgent was accepted at this year’s NeurIPS 2025 conference. Congratulations to Yuxuan. |
| Feb 27, 2025 | Our paper on Unified Multimodal Foundation Model for Computational Pathology was accepted at this year’s CVPR 2025 conference. Congratulations to Yuxuan. |