• 9/2021 - present: Ph.D Candidate in Computer Science, Tufts University

• 9/2019 - 5/2021: M.S. in Data Science, Tufts University

• 9/2014 - 5/2018: B.S. in Information System, Wuhan University of Technology

• 9/2024 - 12/2024: Part-time Student Researcher, Meta AI

• 6/2024 - 9/2024: AI Research Scientist Intern, Meta AI

• 5/2023 - 11/2023: Research Scientist Intern, ByteDance

• 5/2022 - 9/2022: Research Scientist Intern, ByteDance

• 10/2018 - 6/2019: Machine Learning Engineer, Cobot

• 6/2017 - 9/2017: Software Engineer Intern, SuperMap

• Conference Papers

1. Wu, M., Chen, X., Liu, L. "EDGE++: Improved Training and Sampling of EDGE," NeurIPS Workshop on Diffusion Models 2023.

2. Chen, X., Wang, Y., Du, Y., Hassoun, S., Liu, L. "On Normalization in Self-supervised Transformers," Conference on Neural Information Processing Systems (NeurIPS) 2023.

3. Chen, X., Sun, J., Wang, T., Guo, R., Liu, L., Zhang, A. "Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems," Conference on Knowledge Discovery and Data Mining (SIGKDD) 2023.

4. Chen, X., He, J., Han, X., Liu, L. "Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling," International Conference on Machine Learning (ICML) 2023.

5. Chen, X.*, Han, X.*, Hu, J., Ruiz, F., and Liu, L. "Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation," International Conference on Machine Learning (ICML) 2021.

6. Chen, X.*, Hosseini, R.*, Panetta, K., and Sinapov, J. "A Framework for Multisensory Foresight for Embodied Agents," IEEE International Conference on Robotics and Automation (ICRA) 2021.

7. Chen, X.*, Han, X.*, and Liu, L. "GAN Ensemble for Anomaly Detection," Association for the Advancement of Artificial Intelligence (AAAI) 2021.

• Journals

1. Han, X., Chen, X., Ruiz, F., and Liu, L. "Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation," Journal of Machine Learning Research (JMLR).

2. Chen, X.*, Chen, X.*, Liu, L. "Interpretable Node Representation with Attribute Decoding," Transactions on Machine Learning Research (TMLR).

• Preprints

1. Chen, X., Liu, Y., Yang, Y., Yuan, J., You, Q., Liu, L., Yang, H. "Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis," 2023.

2. Chen, X., Li, Y., Zhang, A., Liu, L. "NVDiff: Graph Generation through the Diffusion of Node Vectors," 2022.

• 2023 Spring: Statistical Pattern Recognition, Graduate Level, Tufts University

• 2020 Fall: Introduction to Machine Learning, Graduate Level, Tufts University

• Conference Reviewers

AAAI (2023-2025), ICLR (2023-2025), AISTATS (2023,2024), NeruIPS (2023,2024), CVPR (2024)

• Journal Reviewer

JMLR