Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Liang Zhao. "Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey". arXiv preprint arXiv:2305.18703 (2023)

[paper]

Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore. ISBN: 978-981-16-6053-5

[hard copy]
[website]

2024

Chen Ling, Xujiang Zhao, Wei Cheng, Yanchi Liu, Yiyou Sun, Xuchao Zhang, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen Uncertainty Decomposition and Quantification for In-Context Learning of Large Language Models, 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024), accepted, (Long Paper), 2024.

[paper]
[code]

Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Steve Qin, Liang Zhao Controllable Data Generation by Deep Learning: A Review,. ACM Computing Surveys (CSUR), accepted (impact factor: 16.6), 2024.

[paper]
[code]

Nguyen Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My Thai. MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization. 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), (Acceptance Rate: 27.6%), accepted, 2024.

[paper]
[code]

Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, Liang Zhao Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. ACM Computing Surveys (CSUR), accepted (impact factor: 16.6), 2024.

[paper]
[code]

Nahyun Kwon, Tong Sun, Yuyang Gao, Liang Zhao, Xu Wang, Jeeeun Kim, Ray Hong 3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting Experience through Human-AI Collaboration Design. The 27th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW 2024), accepted, 2024.

[paper]
[code]

Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang, Abhinav Angirekula, Allen Zhang, Liang Zhao Non-Euclidean Spatial Graph Neural Network. SIAM Conference on Data Mining (SDM 2024), accepted (acceptance rate: 29.2%), 2024.

[paper]
[code]

Zheng Zhang and Liang Zhao Self-Similar Graph Neural Network for Hierarchical Graph Learning. SIAM Conference on Data Mining (SDM 2024), accepted (acceptance rate: 29.2%), 2024.

[paper]
[code]

Dazhou Yu, Binbin Chen, Yun Li, Suman Dhaka, Yifei Zhang, Zhenke Liu, Minting Zhang, Jie Zhang, and Liang Zhao STES: A Spatiotemporal Explanation Supervision Framework . SIAM Conference on Data Mining (SDM 2024), accepted (acceptance rate: 29.2%), 2024.

[paper]
[code]

Junruo Gao, Chen Ling, Carl Yang, and Liang Zhao. Helper Recommendation with seniority control in Online Health Community. SIAM Conference on Data Mining (SDM 2024), accepted (acceptance rate: 29.2%), 2024.

[paper]
[code]

Qilong Zhao, Chi-Wei Chang, Xiaofeng Yang, and Liang Zhao Robust Explanation Supervision for False Positive Reduction in Pulmonary Nodule Detection. Medical Physics, accepted (impact factor: 4.506), 2024.

[paper]
[code]

2023

Zheng Zhang, Junxiang Wang, Liang Zhao Relational Curriculum Learning for Graph Neural Network. The Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS 2023), accepted (acceptance rate: 26.1%), 2023.

[paper]
[code]

Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao Open-ended Commonsense Reasoning with Unrestricted Answer Candidates. The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Findings track, accepted, 2023.

[paper]
[code]

Lei Zhang, Qisheng Zhang, Zhiqian Chen, Yanshen Sun, Chang-Tien Lu, and Liang Zhao. Infinitely deep graph transformation networks. The IEEE International Conference on Data Mining (ICDM 2023), accepted (acceptance rate: 9.37%), 2023.

[paper]
[code]

Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, and Chang-Tien Lu. Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks. ACM Computing Surveys (Impact Factor: 16.6), accepted, 2023.

[paper]
[code]

Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao. Fast and Adaptive Dynamics-on-Graphs to Dynamics-of-Graphs Translation. Frontiers in Big Data (Impact Factor: 3.1), accepted, 2023.

[paper]
[code]

Yifei Zhang, Siyi Gu, Yuyang Gao, Bo Pan, Xiaofeng Yang, Liang Zhao. MAGI: Multi-Annotated Explanation-Guided Learning. The 36th International Conference on Computer Vision (ICCV 2023), accepted, 2023.

[paper]
[code]

Siyi Gu, Yifei Zhang, Yuyang Gao, Xiaofeng Yang, Liang Zhao. ESSA: Explanation Iterative Supervision via Saliency-guided Data Augmentation. The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023) (Acceptance Rate: 22.10%), accepted, 2023.

[paper]
[code]

Minxing Zhang, Dazhou You, Yun Li, Liang Zhao. Deep Spatial Prediction via Heterogeneous Multi-Source Self-Supervision. Transactions on Spatial Algorithms and Systems (TSAS), to appear, 2023.

[paper]
[code]

Zheng Zhang and Liang Zhao. Unsupervised Deep Subgraph Anomaly Detection. 2023 International Joint Conference on Artificial Intelligence (IJCAI 2023), (Best Papers from Sister Conferences Track), to appear, 2023.

[paper]
[code]

Chen Ling, Junji Jiang, Junxiang Wang, My Thai, Lukas Xue, James Song, Meikang Qiu, Liang Zhao. Deep Graph Representation Learning and Optimization for Influence Maximization. Fortieth International Conference on Machine Learning (ICML 2023), accepted,(Acceptance Rate: 27.9%).

[paper]
[code]

Tong Sun, Yuyang Gao, Shubham Khaladkar, Sijia Lu, Liang Zhao, Young-Ho Kim, Ray Hong. DeepFuse: Designing Direct Feedback Loops between Humans and Convolutional Neural Networks through Local Explanations. The 2023 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW 2023), accepted.

[paper]
[code]

Chen Ling, Carl Yang, Liang Zhao. Motif-guided Heterogeneous Graph Deep Generation. Knowledge and Information Systems (KAIS), (Impact Factor: 2.531), 2023, pp.1-26.
[Bests of ICDM]

[paper]
[code]

Guangji Bai, Chen Ling, Liang Zhao. Temporal Domain Generalization with Drift-Aware Dynamic Neural Network. The 11th International Conference on Learning Representations (ICLR 2023), accepted. Oral Paper (Top 5% among the accepted papers).

[paper]
[code]

Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang. Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs. The 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted.

[paper]
[code]

Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao. Saliency-Augmented Memory Completion for Continual Learning. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted.

[paper]
[code]

Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. Sign-regularized multi-task learning. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted.

[paper]
[code]

2022

Xiaojie Guo and Liang Zhao. 2022. A Systematic Survey on Deep Generative Models for Graph Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 24.31), accepted.

[paper]

Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao. 2022. Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework. IEEE Transactions on Neural Networks and Learning Systems (Impact Factor: 14.255), accepted.

[paper]
[code]

Shiyu Wang, Xiaojie Guo, Liang Zhao. Deep Generative Model for Periodic Graphs. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022.

[paper]
[code]

Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, Bill Wuest, Amarda Shehu, Liang Zhao. Multi-objective Deep Data Generation with Correlated Property Control. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022.

[paper]
[code]

Zheng Zhang and Liang Zhao. Unsupervised Deep Subgraph Anomaly Detection. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 9.77%), to appear, 2022.
[Best Paper Award]

[paper]
[code]

Dazhou Yu, Guangji Bai, Yun Li, and Liang Zhao. Deep Spatial Domain Generalization. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022.

[paper]
[code]

Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, and Liang Zhao. DeepGAR: Deep Graph Learning for Analogical Reasoning. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022.

[paper]
[code]

Yuyang Gao, Tong Sun, Sungsoo Hong, and Liang Zhao. Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment. Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), to appear, 2022.

[paper]
[code]

Liming Zhang, Dieter Pfoser, Liang Zhao. Factorized Deep Generative Models for End-to-End Trajectory Generation with Spatiotemporal Validity Constraints. The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022 (ACM SIGSPATIAL 2022) (Acceptance Rate: 23.8%), full paper track, to appear, 2022.
[Best Paper Candidate]

[paper]
[code]

Minxing Zhang, Dazhou Yu, Yun Li, Liang Zhao. Deep Geometric Neural Networks for Spatial Interpolation. The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022 (ACM SIGSPATIAL 2022), poster track, to appear, 2022.
[Best Poster Runner-Up Award]

[paper]
[code]

Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. 2022. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022.

[paper]
[code]

Yuyang Gao, Tong Sun, Guangji Bai, Siyi Gu, Sungsoo Hong, and Liang Zhao. 2022. RES: A Robust Framework for Guiding Visual Explanation. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022.

[paper]
[code]

Guangji Bai and Liang Zhao. 2022. Saliency-regularized Deep Multi-task Learning. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022.

[paper]
[code]

Chen Ling, Hengning Cao, Liang Zhao. 2022. STGEN: Deep Continuous-time Spatiotemporal Graph Generation. The 33rd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databasesg (ECML-PKDD 2022) (Acceptance Rate: 26%), accepted, 2022.

[paper]
[code]

Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. 2022. Functional Connectivity Prediction with Deep Learning for Graph Transformation. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted.

[paper]
[code]

Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao. 2022. Small Molecule Generation via Disentangled Representation Learning. Bioinformatics (Impact Factor: 6.937), accepted, 2022.

[paper]
[data]
[code]

Junxiang Wang, Hongyi Li, Liang Zhao. 2022. Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. Neurocomputing (Impact Factor: 5.719), accepted.

[paper]
[code]

Junxiang Wang, Junji Jiang, Liang Zhao. 2022. An Invertible Graph Diffusion Model for Source Localization. The 21st Web Conference (WWW 2022), (Acceptance Rate: 17.7%), accepted.

[paper]
[code]

Xiaojie Guo, Lingfei Wu, Liang Zhao. 2022. Deep Graph Translation. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted.

[paper]
[code]

Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao. 2022. Disentangled Spatiotemporal Graph Generative Model. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted.

[paper]
[code]

Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao. 2022. Adaptive Kernel Graph Neural Network. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted.

[paper]
[code]

Liyan Xu, Xuchao Zhang, Zong Bo, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho Choi. 2022. Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted.

[paper]
[code]

Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. 2022. Interpretable Molecular Graph Generation via Monotonic Constraints. SIAM International Conference on Data Mining (SDM 2022), (Acceptance Rate: 26%), accepted.

[paper]
[code]

2021

Zheng Zhang and Liang Zhao. Representation Learning on Spatial Networks. The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), (Acceptance Rate: 26%), accepted.

[paper]
[code]

Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. GraphGT: Machine Learning Datasets for Deep Graph Generation and Transformation. The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Datasets and Benchmarks Track, accepted.

[paper]
[data]

Chen Ling, Carl Yang, and Liang Zhao. Deep Generation of Heterogeneous Networks. The 21st IEEE International Conference on Data Mining (ICDM 2021), (Acceptance Rate: 9.9%), accepted.
[Best Paper Candidate]

[paper]
[code]

Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. GNES: Learning to Explain Graph Neural Networks. The 21st IEEE International Conference on Data Mining (ICDM 2021), (Acceptance Rate: 9.9%), accepted.

[paper]
[code]

Yuyang Gao, Giorgio Ascoli, Liang Zhao. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. Neural Networks, (impact factor: 8.05), accepted.

[paper]
[code]

Yuyang Gao, Tanmoy Chowdhury (co-first author), Lingfei Wu, Liang Zhao. Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities. IEEE Transactions on Knowledge and Data Engineerings (TKDE), (impact factor: 6.977), accepted.

[paper]
[code]

Xiaosheng Li, Jessica Lin, Liang Zhao. Time Series Clustering in Linear Time Complexity. Data Mining and Knowledge Discovery (DMKD), (impact factor: 3.670), accepted.

[paper]
[code]

Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu. Online and Distributed Robust Regressions with Extremely Noisy Labels. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted.

[paper]
[code&data]

Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala. FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers. The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2021), (acceptance rate: 23.6%), accepted.

[paper]
[preprint]
[code]

Xiaojie Guo, Yuanqi Du, Liang Zhao. Deep Generative Models for Spatial Networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), (acceptance rate: 15.4%), accepted.

[paper]
[code]

Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Fanny Ye, Chang-tien Lu, and Naren Ramakrishnan. Spatio-temporal Event Forecasting Using Incremental Multi-source Feature Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted.

[paper]
[code]

Yuyang Gao, Giorgio Ascoli, Liang Zhao. BEAN: Interpretable and Efficient Learning with Biologically-Enhanced Artificial Neuronal Assembly. Frontiers in Neurorobotics, (impact factor: 2.574), accepted.

[paper]
[code]

Xiaojie Guo, Liang Zhao, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao. Deep Graph Transformation for Attributed, Directed, and Signed Networks. Knowledge and Information Systems (KAIS), (impact factor: 2.936), accepted.
[Bests of ICDM]

[paper]
[code]

Zhiqian Chen, Lei Zhang, Gaurav Kolhe, Hadi Mardani Kamali, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao, Houman Homayoun, Chang-Tien Lu, Liang Zhao. Deep Graph Learning for Circuit Deobfuscation. Frontiers in Big Data, accepted, 2021.

[paper]
[code&data]

Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. Generative Adversarial Learning of Protein Tertiary Structures. Molecules, (impact factor: 4.411), accepted.

[paper]
[code&data]

Liming Zhang, Liang Zhao, Dieter Pfoser, Shan Qin and Chen Ling. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted.

[paper]
[code]

Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. Dr. Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted.
[Best Paper Award Shortlist]

[paper]
[code&data]

Liang Zhao. Event Prediction in the Big Data Era: A Systematic Survey. ACM Computing Surveys (CSUR), (impact factor: 10.28), accepted.

[paper]
[website]

Xiaojie Guo, Yuanqi Du, Liang Zhao. Property Controllable Variational Autoencoder via Invertible Mutual Dependence. The 9th International Conference on Learning Representations (ICLR 2021), (acceptance rate: 28.7%), accepted.

[paper]
[code]

Negar Etemadyrad, Qingzhe Li, Liang Zhao. Deep Graph Spectral Evolution Networks for Graph Topological Evolution. Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2021), (acceptance rate: 21.0%), accepted.

[paper]

Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao. Disentangled Dynamic Graph Deep Generation, SIAM International Conference on Data Mining (SDM 2021), (acceptance rate: 21.3%), accepted.

[paper]
[code]

Pourya Hoseinip, Liang Zhao, and Amarda Shehu. Generative Deep Learning for Macromolecular Structure and Dynamics, Current Opinion in Structural Biology, (impact factor: 7.108), Section on Theory and Simulation/Computational Methods 67: 170-177, 2021 accepted.

[paper]

Qingzhe Li, Liang Zhao, Yi-Ching Lee, Avesta Sassan, and Jessica Lin. CPM: A General Feature Dependency Pattern Mining Framework for Contrast Multivariate Time Series. Pattern Recognition, (impact factor: 7.196),112 (2021): 107711.

[paper]

2020

Junxiang Wang, Zheng Chai, Yue Cheng, and Liang Zhao. 2020. Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework. In Proceedings of the 20th International Conference on Data Mining (ICDM 2020), (acceptance rate: 9.8%), November 17-20, 2020, Virtual Event, Sorrento, Italy, 10 pages.

[paper]
[code]

Yujie Fan, Yanfang (Fanny) Ye, Qian Peng, Jianfei Zhang, Yiming Zhang, Xusheng Xiao, Chuan Shi, Qi Xiong, Fudong Shao, and Liang Zhao. 2020. Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market. In Proceedings of the 20th International Conference on Data Mining (ICDM 2020), (acceptance rate: 9.8%), November 17-20, 2020, Virtual Event, Sorrento, Italy, 10 pages.

[paper]

Liang Zhao, Feng Chen, and Yanfang Ye. Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event Forecasting. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. 32, no. 10, pp. 1923-1935, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2912187.

[paper]

Spatiotemporal Innovation Center Team. 2020. Taking the pulse of COVID-19: a spatiotemporal perspective. International Journal of Digital Earth, (impact factor: 3.097), 25 Aug 2020, https://doi.org/10.1080/17538947.2020.1809723.

[paper]
[dataset]
[website]

Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. 2020. Interpretable Deep Graph Generation with Node-edge Codisentanglement. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), (acceptance rate: 16.8%), August 23-27, 2020, Virtual Event, CA, USA. ACM, New York, NY, USA, 10 pages. https://doi.org/10. 1145/3394486.3403221

[paper]
[code]

Qingzhe Li, Liang Zhao, Jessica Lin and Yi-ching Lee. Contrast Pattern Mining in Paired Multivariate Time Series of Controlled Driving Behavior Experiment. ACM Transactions on Spatial Algorithms and Systems (TSAS), accepted.

[paper]

Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification over Encrypted Wi-Fi Traffic. IEEE Transactions on Information Forensics and Security (TIFS), (impact factor: 7.178), accepted.

[paper]

Qingzhe Li, Amir A. Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, and Liang Zhao. Large-scale Cost-aware Classification Using Feature Computational Dependency Graph. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted.

[paper]

Han Wang, Hossein Sayadi, Avesta Sasan, Houman Homayoun, Liang Zhao, Tinoosh Mohsenin, Setareh Rafatirad. Mitigating Cache-Based Side-Channel Attacks through Randomization: A Comprehensive System and Architecture Level Analysis. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted.

[paper]

Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Chang-Tien Lu, Sai Dinakarrao, Houman Homayoun, Liang Zhao. Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted.

[paper]

Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, Chang-Tien Lu. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted.

[paper]
[materials]

2019

Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, and Sai Dinakarrao. Deep Multi-attributed Graph Translation with Node-Edge Co-evolution. The 19th International Conference on Data Mining (ICDM 2019), long paper, (acceptance rate: 9.08%), Beijing, China.
[Best Paper Award]

[paper]
[code]

Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, and Lingfei Wu. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted.

[paper]
[code]

Yuyang Gao, Lingfei Wu, Houman Homayoun, and Liang Zhao. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted.

[paper]
[code]

Kaiqun Fu, Taoran Ji, Liang Zhao, and Chang-Tien Lu."TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction", the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2019 (SIGSPATIAL 2019), long paper, (acceptance rate: 21.7%), Chicago, Illinois, USA, accepted.

[paper]
[code]

Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. Deep Classifier Cascades for Open World Recognition. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted.

[paper]
[code]

Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao."Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework",The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted.

[paper]
[code]

Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi, Sara Bondi, Sai Manoj Pudukotai Dinakarrao, Liang Zhao, Avesta Sasan, Houman Homayoun, and Setareh Rafatirad,."Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design", 28th International Conference on Field Programmable Logic and Applications (FPL 2019), (acceptance rate: 18%), Barcelona, Spain, accepted.

[paper]
[code]

Liang Zhao, Olga Gkountouna, and Dieter Pfoser. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5, 3, Article 19 (September 2019), 28 pages. DOI:https://doi.org/10.1145/3339823.

[paper]
[code]

Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. Online Flu Epidemiological Deep Modeling on Disease Contact Network. GeoInformatica (impact factor: 2.392), 24, 443–475 (2020). https://doi.org/10.1007/s10707-019-00376-9.

[paper]
[materials]

Maria Malik, Hassan Ghasemzadeh, Tinoosh Mohsenin, Rosario Cammarota, Liang Zhao, Avesta Sasan, Houman Homayoun, Setareh Rafatirad. ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications. The 48th International Conference on Parallel Processing (ICPP 2019), (acceptance rate: 20%), accepted, Kyoto, Japan.

[paper]
[materials]

Junxiang Wang, Fuxun Yu, Xiang Chen, and Liang Zhao. ADMM for Efficient Deep Learning with Global Convergence. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019.

[paper]
[materials]
[code]

Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji and Charu Aggarwal."Efficient Global String Kernel with Random Features: Beyond Counting Substructures", In the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019.

[paper]
[materials]
[data]

Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia and Charu Aggarwal, "Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding", In the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019.

[paper]
[materials]
[data]

Xiaosheng Li, Jessica Lin, and Liang Zhao. Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019.

[paper]
[code]

Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu. iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019.

[paper]
[materials]
[data]

Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen. Interpreting and Evaluating Neural Network Robustness. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019.

[paper]
[materials]
[data]

Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu, "Robust Regression via Heuristic Corruption Thresholding and Its Adaptive Estimation Variation", ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 1.98), accepted, 2019.

[paper]
[code]

Zirui Xu, Fuxun Xu, Liang Zhao, and Xiang Chen. ReForm: Static and Dynamic Resource-Aware DNN Reconfiguration Framework for Mobile Devices. the 56th Design Automation Conference (DAC 2019), accepted, (acceptance rate: 20%), Las Vegas, US, 2019.

Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong. Your Style Your Identity: LeveragingWriting and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network, The Web Conference (WWW 2019), short paper, (acceptance rate: 20%), accepted, 2019.

[paper]
[materials]
[data]

Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting.Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), (acceptance rate: 16.2%), Hawaii, USA, Feb 2019, accepted.

[paper]
[materials]
[code]

2018

Junxiang Wang, Yuyang Gao, Andreas Zufle, Jingyuan Yang, and Liang Zhao. Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), regular paper (acceptance rate: 8.9%), Singapore, Dec 2018, accepted.

[paper]
[code]
[data]

Xuchao Zhang, Liang Zhao, Zhiqian Chen, and Chang-Tien Lu. Robust Regression via Online Feature Selection under Adversarial Data Corruption. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), short paper (acceptance rate: 19.9%), Singapore, Dec 2018, accepted.

[paper]
[code]

Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Jiabin Wang, and Qi Xiong. iDetective: An Intelligent System for Automatic Identification of Key Actors in Online Hack Forums. the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted.

[paper]

Liang Zhao, Amir Alipour-Fanid, Martin Slawski and Kai Zeng. Prediction-time Efficient Classification Using Feature Computational Dependencies. in Proceedings of the 24st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018), research track (acceptance rate: 18.4%), London, United Kingdom, Aug 2018, accepted.

[paper]
[code]
[materials]
[data]

Junxiang Wang and Liang Zhao. Multi-instance Domain Adaptation for Vaccine Adverse Event Detection.27th International World Wide Web Conference (WWW 2018), (acceptance rate: 14.8%), Lyon, FR, Apr 2018, accepted.

[paper]
[code]

Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, and Chang-TIen Lu. Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted.

[paper]
[code]

Ting Hua, Chandan Reddy, Lijing Wang, Liang Zhao, Lei Zhang, Chang-Tien Lu, and Naren Ramakrishnan. Social Media based Simulation Models for Understanding Disease Dynamics. the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted.

[paper]
[slides]

Liang Zhao, Junxiang Wang, and Xiaojie Guo. Distant-supervision of heterogeneous multitask learning for social event forecasting with multilingual indicators. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), pp. 4498-4505, New Orleans, US, Feb 2018.

[paper]
[slides]
[materials]

Yuyang Gao and Liang Zhao. Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. 2999-3006, New Orleans, US, Feb 2018.

[paper]
[code]
[materials]

Junxiang Wang, Liang Zhao, Yanfang Ye, and Yuji Zhang. Adverse event detection by integrating Twitter data and VAERS. Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted.

[paper]

2017

Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. "Online and Distributed Robust Regressions under Adversarial Data Corruption", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. 625-634, New Orleans, US, Dec 2017.

[paper]
[code]

Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao. "A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. 41-50, New Orleans, US, Dec 2017.

[paper]
[code]

Qingzhe Li, Jessica Lin, Liang Zhao and Huzefa Rangwala. "A Uniform Representation for Trajectory Learning Tasks", 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2017), short paper, DOI=10.1145/3139958.3140017, Redondo Beach, CA, USA, Nov 2017.

[paper]

Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu, and Naren Ramakrishnan. "Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data" The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017) , (acceptance rate: 21%), pp. 507-516, Singapore, Nov 2017.

[paper]

Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. “Spatial Event Forecasting in Social Media with Geographically Hierarchical Regularization”. Proceedings of the IEEE (impact factor: 9.237), vol. 105, no. 10, pp. 1953-1970, Oct. 2017.

[paper]

Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu. "Robust Regression via Heuristic Hard Thresholding". in the proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), (acceptance rate: 26%), pp. 3434-3440, Melbourne, Australia, Aug 2017.

[paper]
[code]

Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. “Feature Constrained Multi-Task Learnings for Event Forecasting in Social Media." IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. 29, no. 5, pp. 1059-1072, May 1 2017.

[paper]
[code]

Rupinder Khandpur, Taoran Ji, Yue Ning, Liang Zhao, Chang-Tien Lu, Erik Smith, Christopher Adams and Naren Ramakrishnan. Dynamic Tracking and Relative Ranking of Airport Threats from News and Social Media. Thirty-First AAAI Conference on Artificial Intelligence, pp. 4701-4707, San Francisco, California, USA, Feb 2017.

[paper]

Before 2016

Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. "Online Spatial Event Forecasting in Microblogs.", ACM Transactions on Spatial Algorithms and Systems (TSAS), (Acceptance Rate: 11%), Volume 2 Issue 4, Acticle No. 15, pp. 1-39, November 2016.

[paper]

Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. "Multi-resolution Spatial Event Forecasting in Social Media." in Proceedings of the IEEE International Conference on Data Mining (ICDM 2016), regular paper, (acceptance rate: 8.5%), pp. 689-698, Barcelona, Spain, Dec 2016.

[paper]
[slides]
[code]

Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. “Hierarchical Incomplete Multisource Feature Learning for Spatiotemporal Event Forecasting.” in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), research track (acceptance rate: 18.2%), San Francisco, California, pp. 2085-2094, Aug 2016.

[paper]
[slides]
[code]

Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena et al. "EMBERS at 4 years:Experiences operating an Open Source Indicators Forecasting System." in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), applied data science track, accepted (acceptance rate: 19.9%), pp. 205-214, San Francisco, California, Aug 2016.

[paper]
[video]
[poster]

Liang Zhao, Ting Hua, Chang-Tien Lu, and Ing-Ray Chen. "A Topic-focused Trust Model for Twitter." Computer Communications, (impact factor: 3.34), Elsevier, vo. 76, pp. 1-11, Feb 2016.

[paper]

Ting Hua, Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. "How events unfold: spatiotemporal mining in social media." SIGSPATIAL Special (invited paper), vo. 7, no. 3, pp. 19-25, 2016.

[paper]

Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. "Automatic Targeted-Domain Spatiotemporal Event Detection in Twitter." Geoinformatica, (impact factor: 2.392), Volume 20, Issue 4, pp 765-795, Oct 2016.

[paper]

Shi, Y., Deng, M., Yang, X., Liu, Q., Zhao, L., & Lu, C. T. "A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter." ISPRS International Journal of Geo-Information (IJGI), (impact factor: 1.502), 5.10 (2016): 193.

[paper]

Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. "Multi-Task Learning for Spatio-Temporal Event Forecasting." in Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015), research track, (acceptance rate: 19.4%), Sydney, Australia, pp. 1503-1512, Aug 2015.

[paper]
[slides]
[code]

Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. "SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learning." in Proceedings of the IEEE International Conference on Data Mining (ICDM 2015), regular paper (acceptance rate: 8.4%), Atlantic City, NJ, pp. 639-648, Nov 2015.

[paper]
[slides]

Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. "Spatiotemporal Event Forecasting in Social Media." in Proceedings of the SIAM International Conference on Data Mining (SDM 2015), (acceptance rate: 22%), Vancouver, BC, pp. 963-971, Apr-May 2015.

[paper]
[slides]
[materials]

Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, and Naren Ramakrishnan. "Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling." PLOS ONE (impact factor: 3.534), vo. 9, no. 10 (2014): e110206.

[paper]
[slides]

Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, et al.. "'Beating the news' with EMBERS:forecasting civil unrest using open source indicators." In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2014), industrial track, pp. 1799-1808. ACM, 2014.

[paper]
[slides]

Jinliang Ding, Liang Zhao, Changxin Liu, and Tianyou Chai. "GA-based principal component selection for production performance estimation in mineral processing." Computers & Electrical Engineering (impact factor: 2.189), vo. 40, no. 5 (2014): 1447-1459.

[paper]

Fang Jin, Wei Wang, Liang Zhao, Edward Dougherty, Yang Cao, Chang-Tien Lu, and Naren Ramakrishnan. "Misinformation Propagation in the Age of Twitter." IEEE Computer (impact factor: 3.564), vo. 47, no. 12 (2014): 90-94.

[paper]
[code]

Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." Big data Journal (impact factor: 1.489), vo. 2, no. 4 (2014): 185-195.

[paper]

Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. "The EMBERS architecture for streaming predictive analytics." In Proceedings of the IEEE International Conference on Big Data (BigData 2014), pp. 11-13. IEEE, 2014.

[paper]

Hua, Ting, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. "STED: semi-supervised targeted-interest event detectionin in twitter." InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2013), demo track, pp. 1466-1469. ACM, 2013.

[paper]