I am on the 2023-2024 job market.

🧑‍🎓Bio. I am a fifth-year Ph.D. candidate (2019 – expected 2024) in the Department of Electrical and Computer Engineering at Texas A&M University, advised by Prof. Yang Shen and co-advised by Prof. Zhangyang Wang. I received my bachelor’s degree from Xi’an Jiaotong University. For more details please refer to my CV.

Besides, our lab is recruiting excellent Ph.D. students, postdocs, and student workers if you are TAMU students.

🧫Research. I am interested in machine learning on non-Euclidean data (e.g. graphs or hypergraphs), with fundamental understanding in theory and challenging real-world applications to biomedicine. Representative works include:

• Graph contrastive self-supervised learning (GraphCL, 1k+✍️) with its automated versions (e.g. JOAO) and extension on hypergraphs (HyperGCL);
• A model-based risk bound analysis of graph domain adaptation (GDA);
• An application of graph self-supervised learning to compound-protein affinity and contact prediction (CPAC).

Moreover, for a glance of research in the field of graph self-supervised learning, please refer to the GitHub repo (1k+⭐).

📰News.
2024/04. Participate in the community effort of CAGI6 Rare Genomes Project with the outcome accepted @ Human Genomics’24.
2024/03. “Multi-Modal Contrastive Learning for Proteins by Combining Domain-Informed Views” (multi-modal protein representation learning) is accepted @ MLGenX Workshop, ICLR’24.
2024/01. “Latent 3D Graph Diffusion” (latent diffusion models for 3D graphs) is accepted @ ICLR’24.

2023/10. Talk on Prof. James Cai’s group @ TAMU, online.
2023/08. Talk on the Spatial Omics Journal Club @ Genentech, online.
2023/05 – 2023/08. Join the Research and Early Development organization at Genentech, Inc. (gRED), South San Francisco, as an AIML intern advised by Dr. Changlin Wan & Dr. Kai Liu. ✈️
2023/04. Receive the Quality Graduate Student Award from ECEN @ Texas A&M University.
2023/01. “Graph Domain Adaptation via Theory-Grounded Spectral Regularization” (model-based risk bound analysis of GDA) is accepted @ ICLR’23. [poster] 🎉

2022/10. “Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?” (multi-modal/task protein-protein interface prediction) is accepted @ MLSB Workshop, NeurIPS’22. [poster]
2022/09. “Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative” (contrastive learning on hypergraphs) is accepted @ NeurIPS’22. [poster]
2022/06. “Cross-Modality and Self-Supervised Protein Embedding for Compound-Protein Affinity and Contact Prediction” (multi-modal self-supervision in CPAC) is accepted @ Bioinformatics’22 (MoML’22, ECCB’22). [poster]
2022/05 – 2022/08. Join the Department of Data Science and Machine Learning at insitro, Inc., South San Francisco, as an ML small molecules intern advised by Dr. Bowen Liu & Ralph Ma. 🎉✈️
2022/03. Talk on the AI&A Journal Club @ AstraZeneca, online.
2022/01. “Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How” (Bayesian learning to optimize) is accepted @ ICLR’22. [poster]

2021/12. Receive the NSF Student Travel Awards from WSDM’22.
2021/10. Talk on Prof. Mingyuan Zhou’s group @ UT Austin, online.
2021/10. “Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations” (generative augmentations in GraphCL) is accepted @ WSDM’22. [poster]
2021/09. Receive the Chevron Scholarship from ECEN @ Texas A&M University.
2021/08. Talk on LoGaG @ Technical University of Munich, online.
2021/07. Talk on 3DSIG COSI @ ISMB/ECCB’21, online. [video]
2021/07. Serve as the session chair of Semisupervised and Unsupervised Learning @ ICML’21 and talk.
2021/06 – 2021/08. Join the Product Semantics Team at Amazon.com Services, Inc. remotely, as an applied scientist intern advised by Dr. Tong Zhao.
2021/05. “Graph Contrastive Learning Automated” (long presentation, automatic augmentation selection in GraphCL) is accepted @ ICML’21. [video] 🎉
2021/03. “Probabilistic Constructive Interference Precoding for Imperfect CSIT” (undergraduate thesis work, robust CI precoding) is accepted @ TVT’21. 🎉
2021/02. Pass the Ph.D. qualifying exam.

2020/09. “Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction” (cross-modality learning in CPAC) is accepted @ MLSB Workshop, NeurIPS’20. [poster]
2020/09. “Graph Contrastive Learning with Augmentations” (contrastive learning in GNN pre-training) is accepted @ NeurIPS’20. [poster]
2020/09 – Present. Employed by the Department of Electrical and Computer Engineering at Texas A&M University, College Station, as a graduate research assistant advised by Prof. Yang Shen.
2020/06. “When Does Self-Supervision Help Graph Convolutional Networks?” (self-supervision in GCNs) is accepted @ ICML’20.
2020/02. “L²-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks” (efficient GCN training) is accepted @ CVPR’20.

2019/08 – Present. Attend Texas A&M University, College Station, for the Ph.D.’s Degree in Electrical Engineering, advised by Prof. Yang Shen.
2019/02. Receive the Electrical and Computer Engineering PhD Merit Fellowship from ECEN @ Texas A&M University.