Hi, I am Zelin Zang 臧泽林

Fundamental AI, Foundation Models, AI for Science & AI Scientists

19 CCF-A 5 first · 3 co-first · 4 corr. · 8 other
26 CAAI-A 7 first · 4 co-first · 5 corr. · 11 other
21 SCI Q1 5 first · 5 co-first · 3 corr. · 8 other
1406+ Google Scholar Citations Updated automatically each month

Author roles may overlap when one paper is both co-first and corr. author.

I am currently a Research Assistant Professor (RAP) at Hong Kong Institute of Innovation and Westlake University, working with Prof. Stan Z. Li (IEEE Fellow), Prof. Zhen Lei (IEEE Fellow), and Prof. Jiebo Luo (IEEE Fellow).

My core research focuses on Fundamental AI and Foundation Models. I am particularly interested in representation learning, deep manifold learning, geometric deep learning, multimodal foundation models, and trustworthy AI agents, aiming to build interpretable, scalable, and robust AI systems. My latest interest is AI Scientists and autonomous knowledge discovery — building agentic systems that can autonomously form hypotheses, design and run experiments, and discover new scientific knowledge.

I also work on interdisciplinary applications of AI, especially in AI for Science, life sciences, and medicine. My recent work explores how foundation models and agentic AI can support single-cell analysis, molecular modeling, biomedical reasoning, and clinical decision support.

Previously, I obtained my Ph.D. from Zhejiang University (jointly trained with Westlake University), supervised by Prof. Stan Z. Li (IEEE Fellow). I was also a CSC Visiting Scholar at the National University of Singapore (NUS), advised by Prof. Yang You.

Openings

I am actively recruiting Interns and PhD Students (Jointly trained with Institute of Automation-CAS, Zhejiang University, or Westlake University).

We are looking for self-motivated students interested in:

  • Fundamental AI — Foundation Models, Representation Learning
  • AI Scientists — Autonomous Knowledge Discovery
  • AI for Life Science — Single-cell, Molecular Biology
  • Medical AI & AI Agents
  • AI + X — Interdisciplinary Research

Details on positions and how to apply: Openings page. If you are interested, please send your CV to zangzelin@gmail.com.

Code / Software

GitHub

Diffusion-based data augmentation that enhances unsupervised contrastive learning without domain knowledge.

ICML 2024 DiffAug

MuST

2025

Unified spatial transcriptomics multitask analysis with multimodal structure transformation.

Briefings in Bioinformatics Spatial Transcriptomics

DMT-ME

2026

MoE-enhanced explainable deep manifold transformation for complex data embedding and visualization.

IEEE TPAMI Deep Manifold Learning

DLME

2022

Deep local-flatness manifold embedding for high-dimensional data visualization.

ECCV 2022 Deep Manifold Learning

Research Interests

Fundamental AI Representation learning, deep manifold learning, geometric deep learning, trustworthy AI
AI Scientists & Autonomous Discovery Autonomous hypothesis generation and testing, self-driving experiments, automated scientific knowledge discovery
Foundation Models & Agents Multimodal foundation models, LLM agents, scalable and interpretable AI systems
AI for Science & Medicine Single-cell foundation models, molecular biology, medical reasoning, phylogenetic inference
Selected visual results from HDTree, scHelix, and DMT-ME

* denotes equal contribution; † denotes corresponding author.

Selected Publications

Full list

AI / Manifold Learning

AI for Life Science & Medicine

News

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For a full list of publications, please refer to my Publications page.