Dr. Xiaorui Liu
Dr. Xiaorui Liu
Department of Computer Science
Department of Electrical and Computer Engineering (by courtesy)
North Carolina State University
Office: Room 2296, Engineering Building II
Email: xliu96 at ncsu dot edu
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About Me
About Me
Xiaorui Liu has been an Assistant Professor of Computer Science at North Carolina State University since August 2022. He received his Ph.D. degree in Computer Science from Michigan State University in 2022 under the supervision of Dr. Jiliang Tang. Before that, he received his Master's and Bachelor's degrees from South China University of Technology. He was awarded the AAAI-2025 New Faculty Highlights, National AI Research Resource Pilot Award in 2024, ACM SIGKDD Outstanding Dissertation Award (Runner-up, 2023), Amazon Research Award (2023), NCSU Data Science Academy Award (2023), NCSU Faculty Research and Professional Development Award (2023), Chinese Government Award for Outstanding Students Abroad (2022), Best Paper Honorable Mention Award at ICHI (2019), MSU Cloud Computing Fellowship (2021), and MSU Engineering Distinguished Fellowship (2017).
His research interests include trustworthy artificial intelligence, large-scale machine learning, deep learning on graphs/language/vision, and generative AI. He has published innovative works in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, AISTATS, WWW, and SIGIR. He also organized and co-presented multiple tutorials on these research topics in SDM 2024, AAAI 2024, KDD 2023, WWW 2022, KDD 2021, IJCAI 2021, and ICAPS 2021. He regularly serves as the organizer, (senior) PC member, and reviewer for top-tier international conferences and journals in machine learning and data science such as ICML, NeurIPS, ICLR, KDD, AAAI, IJCAI, WWW, WSDM, LoG, MLSys, CIKM, JMLR, TPAMI, TMLR, and TNNLS.
Research Interests:
Large-scale Optimization, Distributed Optimization
Deep Learning on Graphs, Graph Neural Networks, LLMs
Trustworthy AI, Robust and Certifiable Machine Learning
Generative AI for Vision, Language, and Graphs
AI for Networking, Cybersecurity, Manufacturing, Biology, Healthcare
I am recruiting multiple Ph.D./M.S./UG/K-12 students to work in Machine Learning and Data Science. Please make sure to inform me that you have applied for our Computer Science Program. Otherwise, I may not be able to review the application.
I am recruiting multiple Ph.D./M.S./UG/K-12 students to work in Machine Learning and Data Science. Please make sure to inform me that you have applied for our Computer Science Program. Otherwise, I may not be able to review the application.
If you are interested in related topics, please send your resume, transcript, self-introduction, and research interests to the email xliu96@ncsu.edu
If you are interested in related topics, please send your resume, transcript, self-introduction, and research interests to the email xliu96@ncsu.edu
News
News
Call for Paper: We are organizing a new research topic Graph Machine Learning at Large Scale in Frontiers in Big Data. If you are working in relevant directions, don't hesitate to submit your paper to Frontiers!
05/2025 One paper on supporting efficient large language model (LLM) training on large-scale graphs have been accepted in KDD 2025 Research Track.
05/2025 Two papers on Accelerating Diffusion Models and Large-scale GNN Training have been accepted in ICML 2025.
04/2025 New preprint: "Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks".
02/2025 Invited to serve on an NSF panel.
02/2025 My students, Weizhi Gao and Zhichao Hou, will begin their summer internships in May at Oak Ridge National Laboratory (ORNL) and Amazon, focusing on exciting frontiers of Generative AI research.
02/2025 New preprint: "Boosting Adversarial Robustness and Generalization with Structural Prior".
01/2025 Our research on LLM safety has been supported by the OpenAI Researcher Access Program.
01/2025 Our paper "Robustness Reprogramming for Representation Learning" has been accepted as a Spotlight paper (1.4% ≈ 162/11670) at ICLR 2025. Congratulations to Zhichao. It explores an intriguing and fundamental open challenge in Trustworthy AI: Given any well-trained deep learning model, can it be reprogrammed to enhance its robustness?
12/2024 Received a research grant from Snap Research to support our research on large-scale machine learning.
12/2024 Our group presents three exciting works (authored by Zhichao and Weizhi) at NeurIPS 2024.
12/2024 Excited to be selected for AAAI-2025 New Faculty Highlights.
12/2024 Invited to serve on an NSF panel.
11/2024 New preprint on Exploring LLMs for Transcriptional Regulation Analysis of Long Non-coding RNAs: https://arxiv.org/abs/2411.03522
10/2024 New preprint: "Robustness Reprogramming for Representation Learning".
10/2024 A paper authored by former undergraduate student Wendi Yu was accepted in IEEE BigData.
10/2024 An interdisciplinary journal paper on fiber length measurement was accepted in Fibers.
10/2024 My student Weizhi Gao received the NeurIPS Travel Award.
10/2024 Our group organized and presented a tutorial "Adversarial Robustness in Graph Neural Networks" at DSAA.
10/2024 Attending the Oak Ridge National Laboratory’s Core Universities AI Workshop.
10/2024 Received the CMI Ideation Award from NCSU Comparative Medicine Institute to support our research on Bioinformatics.
09/2024 Three papers on ML robustness and security from our group have been accepted in NeurIPS 2024.
09/2024 Congratulations to our lab members, Sumaita Rahman and Monica Jin, for receiving the Research Experiences for Undergraduates (REU) Award ($3,000) to support their undergraduate research in LLM Robustness and Safety.
08/2024 Invited to serve as the Guest Editor for the special issue on "Applications of Deep Learning in Advanced Materials Processing" for "International Journal of AI for Materials and Design".
08/2024 Thrilled to join the Organizing Committee for KDD 2025 to serve as the Workshop Chair.
08/2024 Congratulations to my student Xingyue for receiving the Goodnight Doctoral Fellowship for 4 years of support!
08/2024 Welcome Ms. Xingyue Shi from Peking University to join our lab as a PhD student.
08/2024 Welcome Mr. Daniel Buchanan to join our lab as a Master's student.
07/2024 Thrilled to receive the National AI Research Resource Pilot Award for our research on exploring and enhancing the robustness of LLMs and foundation models.
07/2024 Invited to give a talk about exascale graph deep learning at ORNL's AI Seminar Series in DOE Oak Ridge National Laboratory.
06/2024 Invited to serve as a Senior Program Committee for AAAI 2025.
05/2024 Our tutorial on "Adversarial Robustness in Graph Neural Networks" is accepted by DSAA 2024.
05/2024 My students Zhichao Hou and Weizhi Gao received the Summer Graduate Merit Awards. Zhichao also started his internship at Amazon.
04/2024 Thrilled to receive the Amazon Research Award to support our research on AI for Information Security: Amazon News
04/2024 Attended and presented two tutorials on large-scale graph deep learning (website & slide) and data quality-aware graph machine learning (website & slide) at SDM 2024.
04/2024 Our survey paper "Graph Machine Learning in the Era of Large Language Models (LLMs)" is online
04/2024 Congratulations to Zhichao for receiving a research intern offer from Amazon!
03/2024 Our paper "Manufacturing Service Capability Prediction with Graph Neural Networks" has been accepted by Journal of Manufacturing Systems.
03/2024 Our paper "ProTransformer: Robustify Transformers via Plug-and-Play Paradigm" has been accepted by ICLR 2024 Workshop on Reliable and Responsible Foundation Models. We propose a novel attention mechanism to robustify any transformer-based architectures such as LLMs with only 4 lines of code without additional training or fine-tuning.
03/2024 Our paper "Efficient Large Language Models Fine-Tuning on Graphs" has been accepted by The Web Conference 2024 Workshop on Large Language Models (LLMs) for Graph Learning.
03/2024 Invited to give a keynote talk about Large-scale Graph Learning at the 5th International Workshop on Machine Learning on Graphs (MLoG) at WSDM 2024.
02/2024 Our slides for the Tutorial on Large-Scale Graph Neural Networks at AAAI 2024 are available online: website & slide
02/2024 New preprint: "Rethinking Large Language Model Architectures for Sequential Recommendations"
01/2024 Our paper "Linear-Time Graph Neural Networks for Scalable Recommendations" has been accepted in WWW (TheWebConf) 2024. The code is available.
01/2024 Our paper "Structural Fairness-aware Active Learning for Graph Neural Networks" has been accepted in ICLR 2024.
01/2024 My students Weizhi and Zhichao gave Research Lightning Talks on Large Language Models and AI Security to our undergraduates.
01/2024 Invited to serve as a Reviewer for ICML 2024 and KDD 2024.
12/2023 New preprint: "Efficient Large Language Models Fine-Tuning on Graphs"
11/2023 New preprint: "Robust Graph Neural Networks via Unbiased Aggregation"
07/2023 New preprint: "Automated Polynomial Filter Learning for Graph Neural Networks"
06/2023 New preprint: "Can Directed Graph Neural Networks be Adversarially Robust?"
11/2023 Our tutorial "Data Quality-Aware Graph Machine Learning" is accepted by SDM 2024. See you in Houston!
11/2023 Our tutorial "Large-Scale Graph Neural Networks" is accepted by SDM 2024. See you in Houston!
10/2023 Our tutorial "Large-Scale Graph Neural Networks" is accepted by AAAI 2024. See you in Vancouver!
10/2023 I will participate in the NSF PI Meeting of the Computer Systems Research (CSR) Program at Duke to explore future research on large-scale machine learning systems and algorithms.
09/2023 One paper is accepted in NeurIPS 2023. See you in New Orleans!
09/2023 Congratulations to two female undergraduate students, Sumaita Rahman and Kshithija Golla, for receiving the Research Experiences for Undergraduates (REU) Award ($3,000) to support their undergraduate research in our group.
08/2023 Welcome Zhichao Hou and Weizhi Gao to join us as Ph.D. students.
08/2023 We organized and presented the Large-scale Graph Neural Networks Tutorial at KDD2023 (website and slides).
08/2023 Excited to share that I've received the 1st Runner-Up of ACM SIGKDD Outstanding Dissertation Award. I gave a short talk about this at KDD 2023.
06/2023 Our new preprint on robust graph deep learning is online: Can Directed Graph Neural Networks be Adversarially Robust?. We provide the first study on the adversarial robustness of graph deep learning in directed graphs.
06/2023 Invited to serve as a Reviewer for LoG (Learning on Graphs Conference) 2023.
06/2023 Invited to serve as a Senior Program Committee member (SPC) for AAAI 2024.
06/2023 Received a seed grant from the Faculty Research and Professional Development Program.
05/2023 Our tutorial "Large-Scale Graph Neural Networks: The Past and New Frontiers" is accepted at KDD. See you in Long Beach!
05/2023 Two papers are accepted in KDD 2023.
04/2023 Two papers are accepted in ICML 2023. See you in Hawaii!
04/2023 Received a seed grant from NC State Data Science Academy to support our research on AI for Atmospheric Science.
04/2023 Excited to share that I’ve received an Amazon Research Award.
03/2023 Invited to serve as the Reviewer for NeurIPS 2023.
02/2023 Our new preprint on large-scale graph deep learning is available: LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation (now published at ICML 2023). We propose a novel approach that is versatilely friendly to data sampling, computation, memory, parallelism, and end-to-end training but still captures long-distance dependency in graphs for large-scale GNN training and inference. The code is available.
02/2023 Invited to serve as an external reviewer for the Research Grants Council (RGC) of Hong Kong.
01/2023 Invited to serve as the PC member for KDD 2023 and ICML 2023.
01/2023 Glad to hold a courtesy appointment in the Department of ECE at NC State.
01/2023 I will be teaching CSC 422 Automated Learning and Data Analysis for undergraduate students in 2023 Spring. Welcome to enroll!
11/2022 Invited to give a talk in the special session "Sparse signal learning and its applications in data science" at 13th AIMS Conference on Dynamical Systems, Differential Equations and Applications in May 2023.
11/2022 Two papers are accepted at ICDE 2023.
08/2022 Start my new position as a Tenure-Track Assistant Professor at NC State.
08/2022 Invited to serve on the Program Committee of the Conference on Systems and Machine Learning (MLSys 2023).
08/2022 I will be teaching CSC 791 Machine Learning with Graphs for graduate students (Ph.D. and M.S.) in 2022 Fall. Welcome to enroll!
08/2022 Grateful to receive the Chinese Government Award for Outstanding Self-finance Students Abroad.
04/2022 I am invited to serve as a Session Chair for KDD 2022.
07/2022 Invited to serve as a Senior Program Committee (SPC) Member for AAAI 2023.
05/2022 Our paper Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective is accepted in KDD 2022.
05/2022 Our paper Trustworthy AI: A Computational Perspective is accepted in ACM Transactions on Intelligent Systems and Technology (TIST).
05/2022 I am invited to give a talk at the ML Seminar at Vanderbilt University on May 16.
04/2022 I am invited by TechBeat to give an online talk about Communication-Efficient Distributed Machine Learning on April 28th [Video].
04/2022 I am invited to serve as a Session Chair for SDM 2022.
04/2022 Our paper Graph Trend Filtering Networks for Recommendations is accepted in SIGIR 2022.
03/2022 I am invited to serve as the reviewer for NeurIPS 2022.
02/2022 I am invited to serve as the PC member for KDD 2022 and CIKM 2022.
01/2022 Two papers are accepted in ICLR 2022.
Is Homophily a Necessity for Graph Neural Networks?
Automated Self-Supervised Learning for Graphs12/2021 Our tutorial on "Trustworthy AI: A Computational Perspective" is accepted in the Web Conference (WWW 2022).
12/2021 One paper "Learning from Imbalanced Crowdsourced Labeled Data" is accepted in SDM 2022.
12/2021 I am invited to serve as the reviewer for ICML 2022.
10/2021 I have been selected to be the volunteer for NeurIPS 2021.
09/2021 Our paper Graph Neural Networks with Adaptive Residual is accepted in NeurIPS 2021.
08/2021 Received the MSU Cloud Computing Fellowship.
08/2021 Present a tutorial about Communication Efficient Distributed Learning at IJCAI 2021. Please check the website and slide for details.
08/2021 Our new preprint Decentralized Composite Optimization with Compression is online.
Older News
Older News
08/2021 Three papers are accepted in CIKM 2021!
08/2021 I am invited to serve as the PC member for AAAI 2022.
08/2021 Present two tutorials about Graph Representation Learning and Adversarial Robustness at KDD 2021.
08/2021 Present one tutorial about Trustworthy AI at ICAPS 2021.
07/2021 Our new preprint Trustworthy AI: A Computational Perspective is online.
07/2021 Our tutorial on Trustworthy AI: A Computational Perspective is accepted to be held in ICAPS 2021.
07/2021 Give a long presentation about Elastic GNN at ICML 2021. Welcome to check the paper, slide, poster, and code for details.
07/2021 I am invited to serve as a member of the novel Program Committee Board (PCB) of IJCAI.
06/2021 Our new preprint Is Homophily a Necessity for Graph Neural Networks? is online.
06/2021 Our new preprint Automated Self-Supervised Learning for Graphs is online.
06/2021 Our new preprint Towards the Memorization Effect of Neural Networks in Adversarial Training is online.
06/2021 I am invited to serve as the Volunteer for ICML 2021.
06/2021 I am invited to serve as a PC member for ICLR 2022 and WSDM 2022.
05/2021 Our new preprint Graph Feature Gating Networks is online.
05/2021 Two papers are accepted in ICML 2021!
Elastic Graph Neural Networks is accepted for oral (long) presentation (3% ≈ 166/5513).
To be Robust or to be Fair: Towards Fairness in Adversarial Training is accepted for spotlight presentation (21% ≈ 1184/5513).05/2021 Two tutorials are accepted to be held in KDD 2021.
Graph Representation Learning: Foundations, Methods, Applications, and Systems
Adversarial Robustness in Deep Learning: From Practices to Theories05/2021 Present our work on distributed machine learning in 2021 MSU Engineering Graduate Research Symposium.
05/2021 Present our work Linear Convergent Decentralized Optimization with Compression in ICLR virtual conference. We show a decentralized optimization algorithm that works perfectly with communication compression. Check the paper, slide, and poster for details.
04/2021 Our research on large-scale machine learning is covered by The Institute for Cyber-Enabled Research (ICER at MSU), which provides a solid infrastructure with advanced computational systems such as high-performance computing platforms (HPCC). Refer to the newsletter Faster Distributed Machine Learning for Free for more details.
04/2021 Our tutorial on Communication Efficient Distributed Learning is accepted to be held in IJCAI 2021. The tutorial website is under construction.
04/2021 I am invited to serve as a PC member for NeurIPS 2021 and CIKM 2021.
04/2021 I am invited to serve as the Research Track session chair (on graph algorithms) for The Web Conference 2021.
03/2021 I am invited to serve as the Volunteer for ICLR 2021.
03/2021 I am honored to receive the Student Scholarship Award from The Web Conference 2021.
01/2021 Our paper Linear Convergent Decentralized Optimization with Compression is accepted by ICLR 2021.
01/2021 Our paper Yet Meta Learning Can Adapt Fast, it Can Also Break Easily is accepted by SDM 2021.
12/2020 I was invited to serve as a reviewer for ICML 2021.
12/2020 I was invited to serve as a senior PC member for IJCAI 2021.
10/2020 Our new work A Unified View on Graph Neural Networks as Graph Signal Denoising is online.
10/2020 Our new work To be Robust or to be Fair: Towards Fairness in Adversarial Training is online.
08/2020 I was invited to serve as a PC member for WWW 2021 and AAAI 2021.
07/2020 Our paper Linear Convergent Decentralized Optimization with Compression is online. This is the first algorithm to achieve linear convergence with communication compression in decentralized optimization. Welcome to check it!
06/2020 I was invited as a PC member for CIKM 2020.
06/2020 I start my research internship at Kwai AI Lab working with Dr. Xiangru Lian and Dr. Ji Liu.
05/2020 Our paper Graph Structure Learning for Robust Graph Neural Networks is accepted by KDD 2020.
04/2020 I was invited as a PC member for NeurIPS 2020 and ICONIP 2020.
01/2020 Our paper A Double Residual Compression Algorithm for Efficient Distributed Learning is accepted by AISTATS 2020.
Our paper Deep Adversarial Canonical Correlation Analysis is accepted by SDM 2020.
Our paper A Double Residual Compression Algorithm for Efficient Distributed Learning for highly efficient distributed optimization is online.
Our paper Epidemic Graph Convolutional Network for efficient training of GCN using epidemic modeling is accepted by WSDM 2020!
Our paper Deep Adversarial Network Alignment for network alignment via deep adversarial generative model is online.
Our paper Weight Loss Prediction in Social-Temporal Context received ICHI 2019 Best Paper Honorable Mention Award!
Our paper A Survey on Dialogue Systems: Recent Advances and New Frontiers is accepted by ACM SIGKDD Explorations Newsletter!
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