Dr. Xiaorui Liu
Dr. Xiaorui Liu
Department of Computer Science
North Carolina State University
Office: Room 2296, Engineering Building II, 890 Oval Dr, NC 27606
Email: xliu96 at ncsu dot edu
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About Me
About Me
Xiaorui Liu is a tenure-track Assistant Professor of Computer Science at North Carolina State University. He joined NC State in August 2022 after completing his Ph.D. in Computer Science at Michigan State University, advised by Dr. Jiliang Tang. He earned his Master's and Bachelor's degrees from the South China University of Technology.
His research develops trustworthy, efficient, and scalable machine learning. Much of his work addresses large-scale and distributed computing for graph neural networks and large language models. A second thread focuses on trustworthy AI: making models robust and safe in the open world. Alongside these, he studies generative AI across vision, language, and graphs, with applications spanning networking, cybersecurity, manufacturing, biology, and healthcare — see the research page to learn more.
His work has been recognized by an NSF CAREER Award (2025), the IJCAI-ECAI Early Career Spotlight (2026), the Goodnight Early Career Innovators Award (2025), the AAAI New Faculty Highlight (2025), the ACM SIGKDD Outstanding Dissertation Award (Runner-up, 2023), and three Amazon Research Awards (2023, 2024, 2026), among other honors — see the award page for the full list.
News
News
06/2026 Our paper "Harnessing Trust in Directed Graphs: Redefining Robustness of Graph Learning" has been accepted for publication in ACM Transactions on Knowledge Discovery from Data (TKDD). It explores the trust implications of graph directionality for trustworthy and robust graph learning, offering a new perspective beyond existing research.
05/2026 My students, Weizhi Gao and Zhichao Hou, begin their summer internships at Qualcomm and Amazon.
04/2026 Honored to be selected for the Early Career Spotlights (ECS) program at IJCAI-ECAI 2026, recognizing early career researchers with significant contributions and strong future impact in AI.
03/2026 Thrilled to receive the Amazon Research Award for our research on neurosymbolic reasoning in large language models (LLMs).
01/2026 Two papers on Scalable Adversarial Attacks and Molecule Generation are accepted at ICLR 2026.
01/2026 Our paper "Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks" has been accepted by IEEE Transactions on Networking.
12/2025 Thrilled to receive the Goodnight Early Career Innovators Award for the recognition of excellence in STEM research, education, innovation, and impact (CS News).
08/2025 Excited to launch the NCSU CSC AI Seminar Series — bringing together cutting-edge research and innovation in AI!
07/2025 Thrilled to receive the NSF CAREER Award to protect machine learning in the realistic open world (NCSU College of Engineering News and CS News). Thanks, NSF!
05/2025 One paper on supporting efficient large language model (LLM) training on large-scale graphs has been accepted in KDD 2025 Research Track.
05/2025 Two papers on Accelerating Diffusion Models and Large-scale GNN Training have been accepted at ICML 2025.
02/2025 My students, Weizhi Gao and Zhichao Hou, will begin their summer internships at Oak Ridge National Laboratory (ORNL) and Amazon, focusing on frontiers of Generative AI.
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. 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 Excited to be selected for AAAI-2025 New Faculty Highlights.
10/2024 An interdisciplinary journal paper on fiber length measurement was accepted in Fibers.
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 at 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 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.
05/2024 My students, Zhichao Hou and Weizhi Gao, received the Summer Graduate Merit Awards. Zhichao also started his summer 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.
03/2024 Our paper "Manufacturing Service Capability Prediction with Graph Neural Networks" has been accepted by Journal of Manufacturing Systems.
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.
01/2024 Our paper "Linear-Time Graph Neural Networks for Scalable Recommendations" has been accepted at WWW (TheWebConf) 2024. The code is available.
01/2024 My students Weizhi and Zhichao gave Research Lightning Talks on Large Language Models and AI Security to our undergraduates.
09/2023 Congratulations to two undergraduate students, Sumaita Rahman and Kshithija Golla, for receiving the Research Experiences for Undergraduates (REU) Award ($3,000) to support their 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 KDD 2023 (website and slides).
08/2023 Excited to share that I've received the ACM SIGKDD Outstanding Dissertation Award (Runner-Up).
06/2023 Received a seed grant from the Faculty Research and Professional Development Program.
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.
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.
08/2022 Start my new position as a Tenure-Track Assistant Professor at NC State.
08/2022 Grateful to receive the Chinese Government Award for Outstanding Self-Finance Students Abroad.
Older News
Older News
05/2022 Our paper Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective is accepted at KDD 2022.
05/2022 Our paper Trustworthy AI: A Computational Perspective is accepted at 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 Our paper Graph Trend Filtering Networks for Recommendations is accepted at SIGIR 2022.
01/2022 Two papers are accepted at 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 at the Web Conference (WWW 2022).
12/2021 One paper "Learning from Imbalanced Crowdsourced Labeled Data" is accepted at SDM 2022.
09/2021 Our paper Graph Neural Networks with Adaptive Residual is accepted at 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.
08/2021 Three papers are accepted in CIKM 2021!
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.
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.
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.
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.
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.
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 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.
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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