Liang Zhao

Assistant Professor
Department of Information Science and Technology
George Mason University
Telephone: +1 703-993-5910
Email: lzhao9@gmu.edu
Address: Room 5343, Engineering Building,
4400 Univ. Dr., Fairfax, VA 22030

 

 



Biography

Liang Zhao is an assistant professor in the department of Information Science and Technology at GMU. Dr. Zhao received the Ph.D. in Computer Science at Virginia Tech, in 2016. His research interests include data mining And machine learning, with special interests in spatiotemporal data mining, social event forecasting, sparse feature learning, and social media mining.

Highly motivated MS and PhD students who want to do research under my advisory are encouraged to contact me by my email.


News

  • The website of our KDD 2019 tutorial on "Spatiotemporal Societal Event Forecasting and Precusor Learning" is here. Welcome!
  • I am the Co-chair of IEEE ICDM 2019 Workshop of DeepSpatial 2019 on Deep Learning for Spatiotemporal Data, Algorithms, and Sytems. Welcome!
  • I am the Co-chair of ACM SIGSPATIAL 2019 Workshop of GeoAI 2019. Welcome!
  • I am the Co-chair of IEEE Big Data 2019 Workshop of DGLMA 2019 on Deep Graph Learning: Methodologies and Applications, the CFP is here. Welcome!
  • The code of our papers of TKDD, IJCAI 2019, and AAAI 2019 has been uploaded. Welcome to use!
  • One paper on spatial network inference for spatial autoregressive has been accepted by TSAS.
  • One paper on Deep learning for spatial contact network has been accepted by Geoinformatica.
  • One paper on Cost-efficient HPC has been accepted by ICPP 2019
  • Three papers are accepted in IJCAI 2019, papers and code will be available soon.
  • Three papers are accepted in KDD 2019, papers and code will be available soon.
  • Our Tutorial on "Spatiotemporal Societal Event Forecasting and Precusor Learning" in KDD 2019 is accepted, materials will be available soon.
  • Our Tutorial on "Big Data Analytics for Societal Event Forecasting" in IEEE International Conference on Big Data 2018.
  • Call for papers for 2nd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News (LENS 2018). Deadline: September 12, 2018. Welcome submissions and participations!
  • Research Interests

  • Societal event forecasting
  • Interpretable machine learning
  • Sparse feature learning
  • Social media mining
  • Spatial data mining
  • Nonconvex distributed optimization
  • Deep learning on graphs

  • Selected Research Papers

    Preprints

    Xiaojie Guo, Lingfei Wu, and Liang Zhao. Deep graph Translation. CoRR, arXiv:1805.09980.

     

     

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    Junxiang Wang, Fuxun Yu, Xiang Chen and Liang Zhao. The Global Convergence of the Alternating Minimization Algorithm for Deep Neural Networks Optimization. arXiv:1811.04187.

     

     

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    Published

    [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), to appear.

     

     

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    [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), to appear.

     

     

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    [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.

     

     

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    [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.

     

     

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    [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.

     

     

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    [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.

     

     

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    [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.

     

     

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    [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.

     

     

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    [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.

     

     

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    [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: 2.775), to appear, 2019.

     

     

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    [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.

     

     

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    [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%=72/361), to appear, 2019.

     

     

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    [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.

     

     

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    [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.

     

     

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    [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.

     

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    [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.

     

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    [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.

     

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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.

     

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    [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: 1.747), 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: 1.94), vo. 47, no. 12 (2014): 90-94.

     

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    [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]


    Selected Research Projects

  • Liang Zhao (Co-PI, PI: Dr. Chaowei Yang), "Phase II I/UCRC [George Mason University]: Center for Spatiotemporal Thinking, Computing and Applications", NSF, $750,000. March 15, 2019-February 29, 2024.
  • Liang Zhao (PI), "CRII: III: Interpretable Models for Spatio-Temporal Event Forecasting using Social Sensors", NSF CISE Research Initiation Initiative (CRII), $174,990. August 1, 2018-July 31, 2020.
  • Liang Zhao (PI), Yi-Ching Lee, and Jessica Lin, "Interpretable Temporal Mining for Contrastive Driving Behaviors". Multi-Interdisciplinary Research Grant Sep, 2017-Oct, 2018, George Mason University. $40,000.

  • Courses

    • AIT 724 Data Analytics in Social Media. Spring 2019
    • AIT 664 Information: Representation, Processing and Visualization. Fall 2018
    • AIT 664 Information: Representation, Processing and Visualization. Spring 2018
    • AIT 724 Data Analytics in Social Media. Spring 2017
    • AIT 690 Data Analytics in Social Media. Fall 2016

    Current Graduate Students

    • Junxiang Wang, PhD student (2017-present)
    • Yuyang Gao, PhD student (2015-present)
    • Xiaojie Guo, PhD student (2017-present)
    • Qingzhe Li, PhD student (2013-present)
    • Negar Etemadyrad, PhD student (2019-present)
    • Cuie Yang, visting PhD student (2018-2019)
    • Shan Qin, visiting PhD student (2018-2019)