Hi, I am Chen Liu, a PhD student in Computer Science at Yale University.
Since 2022, I have been working broadly on manifold learning, studying the geometry of learned spaces. This area has gained renewed attention in recent advances such as DeepSeek's manifold-constrained hyper-connections and Kaiming's manifold-inspired generative models (Just Image Transformer, Drifting Model).
Specifically, I design methods to understand and control how neural networks organize information in their internal representations, covering both general AI and AI for bioscience. The former includes manifold-constrained LLM pre-training (Dispersion) and generative modeling (GAGA, RNAGenScape). The latter includes disease progression modeling (ImageFlowNet), image segmentation (CUTS, DiffKillR), and protein function prediction (ImmunoStruct).
✔️ How I make high-quality figures for publications in top AI conferences and journals
✔️ The first free tool to generate your Google Scholar Citation World Map
✔️ The first tool to quantify your Github repo star percentile
🎉 [Nature Machine Intelligence] ImmunoStruct enables multimodal deep learning for immunogenicity prediction
🎉 [ICASSP 2025 Oral] ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images 
🎉 [ICASSP 2025 Oral] DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images 
🎉 [MICCAI 2024] CUTS: A deep learning and topological framework for multigranular unsupervised medical image segmentation 
🎉 [ICMLW 2023] A novel method to compute entropy and mutual information in neural net representations 
✔️ An on-the-fly evaluator for GANs, with a simple working example of DCGAN on SVHN
✔️ A guide to simulate bigger batch sizes beyond GPU capability
✔️ A Technical Deep Dive into Drag Your GAN (DragGAN)
