I am currently a Postdoctoral Research Fellow at Boston Children’s Hospital (the world’s #1 ranked pediatric hospital), a primary teaching affiliate of Harvard Medical School working under the mentorship of Prof. Kaifu Chen. My research focuses on decoding gene regulatory relationships and reconstructing cellular lineage trajectories using graph-based machine learning and single-cell multi-omics data.

I received my Ph.D. from the School of Software at Tsinghua University, advised by Prof. Chaokun Wang. Before that, I was a postgraduate student in the College of Intelligence and Computing at Tianjin University, where I was advised by Prof. Di Jin and Prof. Dongxiao He.

Education & Experience

Sep. 2025 – Present          💼 Postdoctoral Research Fellow      Harvard Medical School

Sep. 2021 – Jun. 2025      🎓 Ph.D. in Software Engineering      Tsinghua University

Feb. 2019 – Aug. 2021      💼 Algorithm Engineer      JD.com (#44, Fortune Global 500 2024)

Sep. 2012 – Jan. 2019      🎓 B.S./M.S. in Computer Science     Tianjin University

Research

Graph data is ubiquitous, e.g., social networks, recommendation systems, molecular graphs. My research focuses on Graph Neural Network (GNN) and its applications. I have developed different methods for enhancing the capability of GNNs or graph contrastive learning from different aspects, including high-quality embedding, efficient storage & computation, and structure-preserving.

I am currently interested in leveraging GNNs and large language models to solve the challenges in biology 🧬.

Contact

The easiest way to reach me is email. My address is ziyang.liu@childrens.harvard.edu.

What’s New

• [Sep. 2025] 🤝 One paper “Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation” has been accepted to NeurIPS 2025!
• [Sep. 2025] 🤝 One paper “PLForge: Enhancing Language Models for Natural Language to Procedural Extensions of SQL” has been accepted to SIGMOD 2025!
• [Aug. 2025] 🌟 One paper “Molecular Motif Learning as a pretraining objective for molecular property prediction” has been accepted in principle to Nature Communications!
• [Aug. 2025] 🤝 One paper “LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation” has been accepted to EMNLP 2025 (findings)!
• [Jun. 2025] 🌟 One paper “TeRDy: Temporal Relation Dynamics through Frequency Decomposition for Temporal Knowledge Graph Completion” has been accepted to ACL 2025 (main conference)!
• [Apr. 2025] 🤝 One paper “Balancing Self-Presentation and Self-Hiding for Exposure-Aware Recommendation Based on Graph Contrastive Learning” has been accepted to SIGIR 2025!
• [Dec. 2024] 🤝 One paper “Learning Multiple User Distributions for Recommendation via Guided Conditional Diffusion” has been accepted to AAAI 2025!
• [Dec. 2024] 🌟 One paper “Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender Systems” has been accepted to ACM Transactions on Recommender Systems (ToRS)!
• [Jul. 2024] 🌟 One paper “Efficient Unsupervised Graph Embedding with Attributed Graph Reduction and Dual-level Loss” has been accepted to IEEE Transactions on Knowledge and Data Engineering (TKDE)!
• [Apr. 2024] 🌟 One paper “Graph Contrastive Learning with Reinforcement Augmentation” has been accepted to IJCAI 2024!
• [Mar. 2024] 🌟🤝 Two papers “Incorporating Dynamic Temperature Estimation into Contrastive Learning on Graphs” and “GraphHI: Boosting Graph Neural Networks for Large-Scale Graphs” have been accepted to ICDE 2024!
• [Dec. 2022] 🌟 One paper “Fast Unsupervised Graph Embedding via Graph Zoom Learning” has been accepted to ICDE 2023!
• [Sep. 2022] 🌟 One paper “Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search” has been accepted to EMNLP 2022!