I am an Assistant Professor of Computer Science at Emory University since 2020. Before that, I received my Ph.D. in Computer Science at the University of Illinois, Urbana Champaign, where I was working in the Data Mining Group, advised by Prof. Jiawei Han. Further before, I received my B.Eng. in Computer Science in 2014, from the Chu Kochen Honors College of Zhejiang University, advised by Prof. Xiaofei He.
My research interests lie in graph data mining, applied machine learning, knowledge graphs and federated learning, as well as their applications in recommender systems, social networks, neurocience and healthcare.
[2025.04] We will be organizing the International Joint Workshop on Federated Learning for Data Mining and Graph Analytics (FedKDD2025), co-located with ACM SIGKDD 2025 in Toronto this August. Submissions are welcomed!
[2025.03] I will be visiting Nanyang Technological University in Spring/Summer 2025. Many thanks for the generous fellowship from NTU's Tan Chin Tuan Exchange Program.
[2025.01] I am happy to serve as one of the PC Chairs for CIKM 2025.
[2024.10] I am honored to be selected as the Outstanding Associate Editor of 2023-2024 for IEEE TNNLS .
[2024.07] Please check out our preprint of A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises (arXiv 2023).
[2024.04] We will be organizing the International Joint Workshop on Federated Learning for Data Mining and Graph Analytics (FedKDD2024), co-located with ACM SIGKDD 2024 in Barcelona this August.
[2024.04] Congratulations to my PhD student Xuan Kan for smoothly passing his doctoral dissertation defense! Check out his thesis Empower Deep Learning for Brain Network Analysis. Xuan will join Meta as a research scientist after his graduation.
[2024.04] Congratulations to my PhD student Han Xie for smoothly passing her doctoral dissertation defense! Check out her thesis Federated Learning on Graphs: New Scenarios, Challenges, and Methods. Han will join Amazon as an applied scientist after her graduation.
[2024.04] Congratulations to my PhD student Jiaying Lu for smoothly passing his doctoral dissertation defense! Check out his thesis Learning Structured Knowledge from Real-World Data without Excessive Annotations. Jiaying will join the Center for Data Science at Emory University as a research-track assistant professor after his graduation.
[2024.04] Congratulations to my PhD student Hejie Cui for smoothly passing her doctoral dissertation defense! Check out her thesis AI-Assisted Healthcare with Multimodal Structured Knowledge Extraction and Augmented Inference. Hejie will join the Shah Lab at Stanford University as a postdoc fellow after her graduation.
[2024.04] Congratulations to my PhD student Han Xie for winning the Best Doctoral Forum Poster Runner-Up and the Doctoral Forum Travel Award in SDM 2024, for her doctoral thesis on Data Heterogeneity in Federated Graph Learning: Problems, Applications, and Methods.
[2024.03] Congratulations to my PhD student Jiaying Lu for winning the 2024 MCBIOS Young Scientist Excellence Award, for his recent work on Uncertainty-Aware Pre-Trained Foundation Models for Patient Risk Prediction via Gaussian Process.
[2024.03] I am honored to receive a courtesy appointment as Assistant Professor in the Center for Data Science in the Nell Hodgson Woodruff School of Nursing at Emory University.
[2024.03] Our survey paper (led by Dr. Liang Zhao) on LLM domain specialization has been cited by the 2024 Economic Report of the President! This annual report is generated by the Council of Economic Advisers in the White House to present an overview of the nation's economic progress and makes the case for the Biden-Harris Administration's economic policy priorities.
[2024.03] Many thanks to Emory Global Diabetes Research Center (EGDRC) for providing the Doctoral Support Fund to my PhD student Ran Xu, to support our research on graph learning empowered diabetes subtyping.
[2024.02] I am happy to join Google DeepMind as a part-time visiting faculty, after 9 years since I interned at Google in 2015 (and it is such a nostalgia to find my previous employee profile still there).
[2024.01] My high school student Alexis Li has been named a top 40 finalist in the 83rd Regeneron Science Talent Search-- the nation’s oldest and most prestigious science and mathematics competition for high school seniors, also known as the youth Nobel prize competition. Alexis recently has also been REA accepted by Stanford.
[2024.01] We will be conducting a new tutorial for BrainGB at the ISBI 2024 conference in Athens, Greece at the end of this May.
[2023.10] I am honored to receive a courtesy appointment as Assistant Professor in the Department of Biostatistics and Bioinformatics in the Rollins School of Public Health (RSPH) at Emory University.
[2023.10] During my incoming trip to UK for CIKM 2023, I will deliver two seminar talks in the School of Informatics at University of Edinburgh and the Department of Computer Science at University of Oxford.
[2023.09] Our paper on artificial node features for GNNs has been consistently ranked as 1st in the 2023 lists of Most Influential CIKM Papers produced by Best Paper Digest (2023-01, 2023-04, 2023-09).
[2023.09] We thank Microsoft Azure for supporting our research on HealthGPT under the Accelerating Foundation Models Research initiative.
[2023.09] We thank OpenAI for providing us with API credits under the Researcher Access program.
[2023.09] Many thanks to NSF CISE/SBE/EDU for funding our Foundations project Dynamic Brain Graph Mining under the Integrative Strategies for Understanding Neural and Cognitive Systems (NCS) Program.
[2023.08] Many thanks to NSF CISE for funding our Medium project VirtualLab: Integrating Deep Graph Learning and Causal Inference for Multi-Agent Dynamical Systems under the competitive IIS/III Core Program.
[2023.06] It was my pleasure to lead the nomination for Jure Leskovec towards his well-deserved and well-belated KDD Innovation Award.
[2023.04] We will be organizing the 2nd International Workshop on Federated Learning with Graph Data (FedGraph2023) at the ICDM 2023 conference in Shanghai. Please see our call for various types of submissions and co-hosted new data challenge (with cash awards)!
[2023.03] We will be conducting a tutorial for BrainGB during the ICIBM 2023 conference in Tampa, FL, on June 18th.
[2023.02] Many thanks to NIH NIDDK for funding my K25 (Career) award on Understanding Diabetes Heterogeneity via Mining Multimodality Interconnected Data. The award could not have been possible without the help from my many colleagues and great mentor team Dr. Vicki Hertzberg, Dr. Mohammed Ali, Dr. Guillermo Umpierrez and Dr. Roy Simpson.
[2022.11] Our paper on EHR-based clinical predictions on the Best Paper Award at ML4H. Congratulations to Ran Xu and Yue Yu, and many thanks to Joyce Ho and Mohammed Ali!
[2022.09] It was a great pleasure to mentor Ms. Alexis Li from Hamilton High School at Chandler. Alexis will take her work under my mentorship on brain network mixup to the final competition of ISEF this year. Good luck Alexis!
[2022.08] Our research on FedGraph was selected to be funded by Amazon Research Awards.
[2022.08] Congratulations to FederatedScope-GNN on winning the ADS Best Paper Award of KDD 2022, and many thanks for the detailed featuring and integration of our FedSage and GCFL as the most representative FGL models in the platform.
[2022.07] I was happy to serve as a mentor for the KDD 2022 Undergraduate Consortium. Check out this interesting paper on per-node privacy of GCN written by my mentee at University of Virginia.
[2022.07] It was a great pleasure to serve as a mentor for our NSF REU/RET site on Computational Mathematics for Data Science at Emory this summer. Here goes a cute video made by my mentees and also check out their web post, poster and paper!
[2022.06] We will be organizing the 1st International Workshop on Federated Learning with Graph Data (FedGraph2022), at ACM CIKM 2022 in Atlanta this October. Various types of submissions are welcomed!
[2022.05] Our four papers got accepted by KDD 2022, all of which are based on our recent progress in healthcare and neuroscience informatics-- we have successfully applied modern graph learning techniques to electronic health records, mobile health data, brain imaging, and SEEG data. One of them (GraphDNA) was honored to receive the Best Paper Award of Health Day (3 in total). Congratulations, team!
[2022.04] We will be organizing the International Workshop on Neural Network Models for Brain Connectome Analysis (BrainNN2022), at IEEE BigData 2022 in Osaka this December. Various types of submissions are welcomed!
[2022.03] Our benchmark paper on GNNs for brain network analysis can be accessed on arXiv and the benchmark website is also fully available! ([2022.10] Update: the BrainGB paper is now accepted by IEEE TMI.)
[2021.09] Our four papers got accepted by NeurIPS 2021. Three of them were under my supervision-- FedSage (subgraph-level federated learning), GCFL (graph-level federated learning) and EGI (GNN transferability). FedSage was selected for a Spotlight Presentation (3%). Great work, team!
[2021.04] Our three papers on graph neural networks (secure graph generation, embedding dimension selection and robust neighborhood aggregation) have been accepted by IJCAI 2021.
[2020.12] Our survey and benchmark paper on heterogeneous network representation learning has been accepted by IEEE TKDE. All code and data are available at https://github.com/yangji9181/HNE.
[2020.10] I am honored to receive the Best Paper Award from ICDM 2020 (1 out from 930 submissions and 91 acceptances)! Check out the TaxoGAN paper.
[2020.05] Our work (MultiSage) in collaboration with researchers in Pinterest and Stanford on web-scale contextualized graph neural networks has been selected as the Best Paper Runner-Up in the Applied Data Science Track of KDD 2020.
[2020.05] I am honored to receive the UIUC 2020 Doctoral Dissertation Completion Fellowship ($20,000), which is awarded to 20 candidates (from 20 departments) out from 74 nominations (from 48 departments) across the whole university.
[2020.05] I will be joining Emory University Dept. of Computer Science as a Tenure-Track Assistant Professor in September this year. Cor prudentis possidebit scientiam!
[2020.04] I am visiting University of Oxford and collaborating with Prof. Thomas Lukasiewicz and his team on structured information extraction from multi-media data this summer.
[2020.02] I will deliver research talks in Northwestern University, Simon Fraser University, University of British Columbia, Emory University, University of Florida and University of Sydney.
[2020.01] I am visiting my alma mater, Zhejiang University, for the first time after my graduation five years ago.
[2019.09] I will give a talk in the Great Lakes Workshop on Data Science.
[2019.07] The Han Family will get together in San Francisco. Happy birthday Prof Han!
[2019.05] I am working in Pinterest Lab this summer with the Applied Science team led by Prof. Jure Leskovec.
Participated in and advised on the development and evaluations of several graph-enhanced LLMs such as regarding efficient distillation, aligned reward, logical planning and diversified search.
Empowered GraphSage for web-scale contextualized recommendation through context-aware aggregation and Hadoop-based stream training on heterogeneous pin-board networks.
Developed a two-step data-driven pipeline of feature generation and metric learning for place embedding to leverage ad-hoc place attributes and noisy training data towards efficient place deduplication.
Developed a relation profiling algorithm based on multiple signals including user attributes, link structures and diffusive messages in the social network with novel multi-modal graph autoencoders.
Constructed the transportation HIN (heterogeneous information network) based on DiDi's travel data and developed a pattern-aware HIN embedding algorithm for passenger experience prediction.
Developed a joint learning framework of user links and attributes for friend recommendation and interest targeting. Implemented a Spark pipeline and scaled it to networks with millions of nodes and billions of edges.
Implemented data extraction and inventory analysis pipeline using flume C++. Implemented an online simulation of ads serving and an offline optimal algorithm based on max flow to analyze the inventory.