Selected Preprints

Xiaojie Guo and Liang Zhao. 2020. A Systematic Survey on Deep Generative Models for Graph Generation. arXiv preprint arXiv:2007.06686.

[paper]

Selected Publications (Full list)

KDD 2021

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

[paper]
[code]

TKDD

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), to appear.

[paper]
[code]

Frontiers in Neurorobotics

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

[paper]
[code]

KAIS

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), to appear.
[Bests of ICDM]

[paper]
[code]

FBD

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, to appear, 2021.

[paper]
[code]

Molecules

Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. Generative Adversarial Learning of Protein Tertiary Structures. Molecules, (impact factor: 3.267), to appear.

[paper]
[code]

WWW 2021

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]

WWW 2021

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]

CSUR

Liang Zhao. Event Prediction in the Big Data Era: A Systematic Survey. ACM Computing Surveys (CSUR), (impact factor: 7.990), accepted.

[paper]

ICLR 2021

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%), to appear.

[paper]
[code]

AAAI 2021

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%), to appear.

[paper]

SDM 2021

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%), to appear.

[paper]

COSB

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 to appear.

[paper]

Pattern Recognit.

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]

ICDM 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]

ICDM 2020

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]


TKDE

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: 3.857), vol. 32, no. 10, pp. 1923-1935, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2912187.

[paper]
[materials]
[data]

I.J. Digital Earth

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]

KDD 2020

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]


TSAS

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), to appear.

[paper]


TIFS

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: 4.332), to appear.

[paper]


TKDE

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: 3.857), to appear.

[paper]

DATE 2020

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%), to appear.

[paper]


DATE 2020

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%), to appear.

[paper]


AAAI 2020

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%), to appear.

[paper]


ICDM 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]


ICDM 2019

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, to appear.

[paper]
[code]


ICDM 2019

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, to appear.

[paper]
[code]


SIGSPATIAL 2019

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, to appear.

[paper]
[code]


CIKM 2019

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, to appear.

[paper]
[code]


CIKM 2019

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, to appear.

[paper]
[code]


FPL 2019

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, to appear.

[paper]
[code]


TSAS

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]


Geoinformatica

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, 443475 (2020). https://doi.org/10.1007/s10707-019-00376-9.

[paper]
[materials]


ICPP 2019

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%), to appear, Kyoto, Japan.

[paper]
[materials]


KDD 2019

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%), to appear, Alaska, USA, Aug 2019.

[paper]
[materials]
[code]


KDD 2019

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%), to appear, Alaska, USA, Aug 2019.

[paper]
[materials]
[data]


KDD 2019

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%), to appear, Alaska, USA, Aug 2019.

[paper]
[materials]
[data]


IJCAI 2019

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%), to appear, Macao, China, Aug 2019.

[paper]
[code]


IJCAI 2019

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%), to appear, Macao, China, Aug 2019.

[paper]
[materials]
[data]


IJCAI 2019

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%), to appear, Macao, China, Aug 2019.

[paper]
[materials]
[data]


TKDD

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), to appear, 2019.

[paper]
[code]


DAC 2019

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), to appear, (acceptance rate: 20%), Las Vegas, US, 2019.

[paper]
WWW 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%), to appear, 2019.

[paper]
[materials]
[data]


AAAI 2019

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, to appear.

[paper]
[materials]
[code]


ICDM 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, to appear.

[paper]
[code]
[data]


ICDM 2018

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, to appear.

[paper]
[code]

ACSAC 2018

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, to appear.

[paper]

KDD 2018

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, to appear.

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


WWW 2018

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, to appear.

[paper]
[code]

IJCAI 2018

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, to appear.

[paper]
[code]

IJCAI 2018

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, to appear.

[paper]
[slides]

BIGDATA 2018

Junxiang Wang, Liang Zhao, and Yanfang Ye. Semi-supervised Multi-instance Interpretable Models for Flu Shot Adverse Event Detection. 2018 IEEE International Conference on Big Data (BigData 2018) (acceptance rate: 18.9%), Seattle, USA, Dec 2018, to appear.

[paper]
[code]

BIGDATA 2018

Lei Zhang, Liang Zhao, Xuchao Zhang, Wenmo Kong, Zitong Sheng, and Chang-Tien Lu. Situation-Based Interpretable Learning for Personality Prediction in Social Media. 2018 IEEE International Conference on Big Data (BigData 2018) (acceptance rate: 18.9%), Seattle, USA, Dec 2018, to appear.

[paper]

AAAI 2018

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]

AAAI 2018

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]

JBMS

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, to appear.

[paper]

ICDM 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]

ICDM 2017

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]

BIGDATA 2017

Xuchao Zhang, Liang Zhao, Zhiqian Chen, Arnold Boedihardjo, Dai Jing, and Chang-Tien Lu. "Trendi: Tracking Stories in News and Microblogs via Emerging, Evolving and Fading Topics". in Proceedings of the IEEE International Conference on Big Data (BigData 2017), regular paper, (acceptance rate: 18%), pp. 1590-1599, Boston, MA, Dec 2017.

[paper]

BIGDATA 2017

Xuchao Zhang, Zhiqian Chen, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. "TRACES: Generating Twitter Stories via Shared Subspace and Temporal Smoothness". in Proceedings of the IEEE International Conference on Big Data (BigData 2017), short paper, pp. 1688-1693, Boston, MA, Dec 2017.

[paper]

SIGSPATIAL 2017

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]

CIKM 2017

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]

PIEEE

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]

IJCAI 2017

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]

TKDE

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: 3.438), vol. 29, no. 5, pp. 1059-1072, May 1 2017.

[paper]
[code]

AAAI 2017

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]

TSAS

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]

ICDM 2016

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]

KDD 2016

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]

KDD 2016

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]

COMCOM

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]

SIGSPATIAL

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]

Geoinformatica

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]

IJGI

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]

KDD 2015

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]

BIGDATA 2015

Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. "Dynamic Theme Tracking in Twitter." in Proceedings of the IEEE International Conference on Big Data (BigData 2015), regular paper (acceptance rate: 16.8%), Santa Clara, California, pp. 561-570, Oct-Nov 2015.

[paper]
[slides]
[materials]

ICDM 2015

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]

SDM 2015

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]

PLOS ONE

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]

KDD 2014

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]

ECE

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]

IEEE COMPUTER

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]

BIGDATA

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]

BIGDATA 2014

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]

KDD 2013

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]