Welcome to visit Xiaojie Guo's Homepage!

About me

Xiaojie is now a passionate AI scientist, developing novel deep learning/machine learning techniques and applying these techniques into the real-world challenging problems. She currently serves as a Research Staff Member at IBM Thomas J. Watson Research Center.

She got her Ph.D. degree from the department of Information Science and Technology at GMU. Her advisor is Dr. Liang Zhao.  Her research interests include data mining, artificial intelligence, and machine learning, with special interests in deep learning on graphs, graph transformation and generation, and interpretable representation learning. She has published over 20 papers in top-tier conferences and journals such as KDD, ICDM, ICLR,  AAAI, CIKM, and KAIS. She won the Best Paper Award in ICDM 2019 and has one paper awarded as an ESI Hot and Highly Cited Paper as the first author. Xiaojie has also served as an independent peer reviewer for multiple top academic journals, such as the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Knowledge Discovery from Data (TKDD), International Journal of Intelligent System (IJIS), as well as workshops of conferences including KDD and AAAI. 

News

• 01/2025 One paper has been accepted in ICLR 2025!

• 10/2023 One paper has been accepted in IEEE BigData!

• 01/2024: One survey paper have been accepted in ACM Computing Surveys!

• 10/2022: One survey paper on Deep Graph Generation have been accepted in TPAMI!

• 09/2022: Two papers have been accepted in NeruIPS 2022!

• 05/2022: One paper has been accepted in KDD 2022!

• 01/2022: One paper has been accepted in TheWebConf 2022!

• 01/2022: One paper has been accepted in TNNLS.

• 12/2021: One paper has been accepted in SDM 2022.

• 12/2021: One paper has been accepted in AAAI 2022.

• 11/2021: One award paper on "Intelligent Online Selling Point Extraction for E-Commerce Recommendation" are accepted by AAAI/IAAI 2022! 

• 10/2021:Paper titled “Deep Latent-Variable Models for Controllable Molecule Generation” has been accepted in BIBM 2021.

• 10/2021: GraphGT, the first Machine Learning Datasets for Graph Generation and Transformation, has been accepted in NeurIPS Dataset and Benchmark Track. GraphGT has an easy-to-use Python API and a graph generation literature and resource collection. Please Star if you find helpful!

• 08/2021: KDD workshop on DLG have successfully delivered! You are welcome to check out various talks videos from our keynotes and authors. 

• 06/2021: We are delighted to release our Graph4NLP library, which is the first library for the easy use of GNNs for NLP! Try it out and let us know what you feel! 

• One paper is accepted in KDD 2021! Papers and code are coming soon.

• Congratulation! I have sucessfully passed my final defense and become offcially Dr. Guo!

• Our paper "Deep Graph Transformation for Attributed, Directed, and Signed Networks" has been accepted by Knowledge and Information Systems as Bests of ICDM! 

• One paper is accepted in ICLR 2021! 

• One paper is accepted in KDD 2020! Papers and code are coming soon. 

• The summary of datasets  for Deep Graph Translation Problem is  available. [link]

• Congratulations! Our paper received Best Paper Award at ICDM 2019. The paper focuses on exploring an promising domain on deep graph translation. 

• Congratulations on my offer of 2020 Research Summer Intern-PhD in IBM AI Foundations Labs !

• One paper is accepted in CIKM 2019, papers and code are available. 

Research Interests

• Deep learning on graphs

• Deep learning based Protein Structure Generation

• Open world classification

• Deep learning based Mechanical Fault Diagnosis