[Publications]  [Services]  [Research Grants]  [Teaching]  [Award]  [PhD Scholarship]  [Call For Tao Li Award]


Xingquan (Hill) Zhu

I am a Professor in the Dept. of Electrical Engineering and Computer Science , Florida Atlantic University . I received my Ph.D degree in Computer Science from Fudan University, Shanghai, China. My research mainly focuses on Data Mining, Machine Learning, Multimedia Systems, and Bioinformatics. I am an IEEE Fellow (Class of 2023) for contributions to data mining for big data analytics and network representation learning, and a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA).

Address: Dept. of Electrical Engineering and Computer Science
                Engineering East (EE)-503B, Florida Atlantic University
                777 Glades Road, Boca Raton, FL 33431, USA
Phone: +1-561-297-3452;    Email: xzhu3 fau.edu

Selected Publications [Complete List][DBLP][ Google]

Books

Journals

  1. Zhabiz Gharibshah and Xingquan Zhu, User Response Prediction in Online Advertising, ACM Computing Survey. Accepted. In Press [pdf].
  2. Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang, Network Representation Learning: A Survey, IEEE Transactions on Big Data. Accepted. In Press [pdf].
  3. Haishuai Wang, Jia Wu, Xingquan Zhu, Yixin Chen, and Chengqi Zhang, Time-Variant Graph Classification, IEEE Transactions on Systems, Man, and Cybernetics: Ststems . Accepted. In Press [pdf].
  4. Ting Guo, Shirui Pan, Xingquan Zhu, and Chengqi Zhang, CFOND: Consensus Factorization for Co-Clustering Networked Data, IEEE Transactions on Knoweldge and Data Engineering , 31(4):706-719, 2019. [pdf].
  5. Youxi Wu, Yao Tong, Xingquan Zhu, and Xindong Wu, NOSEP: Non-Overlapping Sequence Pattern Mining with Gap Constraints, IEEE Transactions on Cybernetics , 48(10):2809-2822, 2018. [pdf].
  6. Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu, Multiple Structure-View Learning for Graph Classification, IEEE Transactions on Neural Networks and Learning Systems, 29(7):3236-3251, 2018. [pdf] .
  7. Yisen Wang, Shu-Tao Xia, Qingtao Tang, Jia Wu, and Xingquan Zhu, A Novel Consistent Random Forest Framework: Bernoulli Random Forests, IEEE Transactions on Neural Networks and Learning Systems, 29(8):3510-3523, 2018. [pdf].
  8. Wei Wu, Bin Li, Ling Chen, Xingquan Zhu, and Chengqi Zhang, K-Ary Tree Hashing for Fast Graph Classification, IEEE Transactions on Knowledge and Data Engineering, 30(5):936-949, 2018. [pdf].
  9. Lianhua Chi, Bin Li, Xingquan Zhu, Shirui Pan, and Ling Chen, Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts, IEEE Transactions on Cybernetics , 48(5):1591-1604, 2018. [pdf].
  10. Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Xindong Wu, Multi-instance learning with discriminative bag mapping, IEEE Transactions on Knowledge and Data Engineering , 30(6):1065-1080, 2018. [pdf].
  11. Lianhua Chi and Xingquan Zhu, Hashing Techniques: A Survey and Taxonomy, ACM Computing Surveys, 50(1), Article No. 11, 2017. [pdf].
  12. Ting Guo, Jia Wu, Xingquan Zhu, and Chengqi Zhang, Combining Structured Node Content and Topology Information for Networked Graph Clustering, ACM Transactions on Knowledge Discovery from Data , 11(3), Article No. 29, 2017. [pdf].
  13. Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Xindong Wu, Positive and Unlabeled Multi-Graph Learning, IEEE Transactions on Cybernetics, 47(4):818-829, 2017. [pdf].
  14. Shirui Pan, Jia Wu, Xingquan Zhu, Guodong Long, and Chengqi Zhang, Task Sensitive Feature Exploration and Learning for Multitask Graph Classification, IEEE Transactions on Cybernetics, 47(3):744-758, 2017. [pdf].
  15. Haishuai Wang, Peng Zhang, Xingquan Zhu, Ivor Wai-Hung Tsang, Ling Chen, Chengqi Zhang, and Xindong Wu, Incremental Subgraph Feature Selection for Graph Classification, IEEE Transactions on Knowledge and Data Engineering , 29(1):128-142, 2017. [pdf].
  16. Meng Fang, Jie Yin, and Xingquan Zhu, Supervised Sampling for Networked Data, Signal Processing, 124:93-102, 2016. [pdf].
  17. Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu, Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification, IEEE Transactions on Knowledge and Data Engineering , 28(3):715-728, 2016. [pdf].
  18. Meng Fang, Jie Yin, and Xingquan Zhu, Active Exploration for Large Graphs, Data Mining and Knowledge Discovery, 30(3):511-549, 2016. [pdf].
  19. Shirui Pan, Jia Wu, and Xingquan Zhu, CogBoost: Boosting for Fast Cost-sensitive Graph Classification, IEEE Transactions on Knowledge and Data Engineering , 27(11):2933-2946, 2015. [pdf].
  20. Bin Li, Xingquan Zhu, Ruijiang Li, and Chengqi Zhang, Rating Knowledge Sharing in Cross-Domain Collaborative Filtering, IEEE Transactions on Cybernetics, 45(5):1054-1068, May 2015. [pdf].
  21. Shirui Pan, Jia Wu, Xingquan Zhu, and Chengqi Zhang, Graph Ensemble Boosting for Imbalanced Noisy Graph Stream Classification, IEEE Transactions on Cybernetics, 45(5):940-954, May 2015. [pdf].
  22. Meng Fang, Jie Yin, Xingquan Zhu, and Chengqi Zhang, TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs, IEEE Transactions on Knowledge and Data Engineering , 27(9):2536-2549, 2015. [pdf].
  23. Jia Wu, Shirui Pan, Xingquan Zhu, and Zhihua Cai, Boosting for Multi-Graph Classification, IEEE Transactions on Cybernetics, 45(3): 430-443, March 2015. [pdf].
  24. Peng Zhang, Chuan Zhou, Peng Wang, Byron J. Gao, Xingquan Zhu, and Li Guo, E-Tree: An Efficient Indexing Structure for Ensemble Models on Data Streams , IEEE Transactions on Knowledge and Data Engineering , 27(2): 461-474, February 2015. [pdf].
  25. Jia Wu, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu, Bag Constrained Structure Pattern Mining for Multi-Graph Classification, IEEE Transactions on Knowledge and Data Engineering, 26(10):2382-2396, October, 2014. [pdf].
  26. Meng Fang and Xingquan Zhu, Active Learning with Uncertain Labeling Knowledge, Pattern Recognition Letter, 43:98-108, 2014. [pdf].
  27. Yifan Fu, Bin Li, Xingquan Zhu, and Chengqi Zhang, Active Learning without Knowing Individual Instance Labels: A Pairwise Label Homogeneity Query Approach, IEEE Transactions on Knowledge and Data Engineering, 26(4):808-822, April, 2014. [pdf].
  28. Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding, Data Mining with Big Data, IEEE Transactions on Knowledge and Data Engineering, 26(1):97-107, January, 2014. [pdf].
  29. Hanning Yuan, Meng Fang, and Xingquan Zhu, Hierarchical Sampling for Multi-Instance Ensemble Learning, IEEE Transactions on Knowledge and Data Engineering, 24(12):2900-2905, December 2013. [pdf].
  30. Yifan Fu, Xingquan Zhu, and Ahmed Elmagarmid, Active Learning with Optimal Instance Subset Selection, IEEE Transactions on Cybernetics, 43(2):464-475, April, 2013. [pdf].
  31. Xindong Wu, Kui Yu, Wei Ding, Hao Wang, and Xingquan Zhu, Online Feature Selection with Streaming Features, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(5):1178-1192, May 2013. [pdf].
  32. Yifan Fu, Bin Li, and Xingquan Zhu, A Survey on Instance Selection for Active Learning, Knowledge and Information Systems, 35(2):249-283, May 2013. [pdf].
  33. Xingquan Zhu, Bin Li, Xindong Wu, Dan He, and Chengqi Zhang, CLAP: Collaborative Pattern Mining for Distributed Information Systems Decision Support Systems, 52(1):40-51, 2011. [pdf].
  34. Xingquan Zhu, Wei Ding, Philip S. Yu, and Chengqi Zhang, One-class learning and concept summarization for data streams Knowledge and Information Systems, 28(3):523-553, 2011. [pdf].
  35. Xingquan Zhu, Cross Domain Semi-Supervised Learning Using Feature Formulation, IEEE Transactions on Systems Man and Cybernetics, Part B, 41(6):1727-1638, 2011. [pdf].
  36. Xingquan Zhu, Peng Zhang, Xiaodong Lin, and Yong Shi, Active Learning from Stream Data Using Optimal Weight Classifier Ensemble, IEEE Transactions on Systems Man and Cybernetics, Part B, 40(6):1607-1621, December 2010. [pdf].
  37. Xingquan Zhu and Ying Yang, A Lazy Bagging Approach to Classification, Pattern Recognition, 41(10): 2980-2992, October, 2008. [pdf].
  38. Xindong Wu and Xingquan Zhu, Mining with Noise Knowledge: Error Awareness Data Mining, IEEE Transactions on Systems Man and Cybernetics, Part A, 38(4): 917-932, July, 2008. [pdf].
  39. Xingquan Zhu and Xindong Wu, Class Noise Handling for Effective Cost-Sensitive Learning by Cost-Guided Iterative Classification Filtering, IEEE Transactions on Knowledge and Data Engineering, 18(10): 1435-1440, November, 2006. [pdf].
  40. Xingquan Zhu, Xindong Wu, Ahmed Elmagarmid, Zhe Feng, and Lide Wu, Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective, IEEE Transactions on Knowledge and Data Engineering, 17(5): 665-677, May, 2005. [pdf].
  41. Xingquan Zhu and Xindong Wu, Cost-Constrained Data Acquisition for Intelligent Data Preparation, IEEE Transactions on Knowledge and Data Engineering, 17(11): 1542-1556, November, 2005. [pdf].
  42. Xingquan Zhu, Ahmed Elmagarmid, Xiangyang Xue, Lide Wu, and Ann Catlin, InsightVide: Towards Hierarchical Video Content Organization for Efficient Browsing, Summarization and Retrieval, IEEE Transactions on Multimedia, 7(4): 648-666, November, 2005. [pdf].
  43. Xingquan Zhu and Xindong Wu, Class Noise vs. Attribute Noise: A Quantitative Study of Their Impacts, Artificial Intelligence Review, 22 (3-4):177-210, November 2004. [pdf].
  44. Xingquan Zhu, Jianping Fan, Ahmed Elmagarmid, and Xindong Wu, Hierarchical Video Summarization and Content Description Joint Semantic and Visual Similarity, ACM / Springer Multimedia System Journal, 9(1):31-53, 2003. [pdf].

Conference Proceedings

  1. Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, and Chengqi Zhang, Multi-Graph-View Learning for Graph Classification, Proceedings of the IEEE 14th International Conference on Data Mining (ICDM '14), Shenzhen, China, December 14-17, 2014. (Best Paper Candidate)
  2. Fei Xie, Xindong Wu, and Xingquan Zhu, Document-Specific Keyphrase Extraction Using Sequential Patterns with Wildcards, Proceedings of the IEEE 14th International Conference on Data Mining (ICDM '14), Shenzhen, China, December 14-17, 2014.
  3. Ting Guo, Xingquan Zhu, Jian Pei, and Chengqi Zhang, SNOC:Streaming Network Node Classification, Proceedings of the IEEE 14th International Conference on Data Mining (ICDM '14), Shenzhen, China, December 14-17, 2014.
  4. Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, and Chengqi Zhang, Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM '14) , Nov. 3-7, 2014, Shanghai, China.
  5. Lianhua Chi, Bin Li, and Xingquan Zhu, Context-Preserving Hashing for Fast Text Classification, Proceedings of the 2014 SIAM International Conference on Data Mining (SDM '14), April 24-26, 2014, Philadelphia, Pennsylvania, USA.
  6. Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, and Zhihua Cai, Multi-Graph Learning with Positive and Unlabeled Bags, Proceedings of the 2014 SIAM International Conference on Data Mining (SDM '14), April 24-26, 2014, Philadelphia, Pennsylvania, USA.
  7. Lianhua Chi, Bin Li, and Xingquan Zhu, Fast Graph Stream Classification Using Discriminative Clique Hashing, Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '13), April 14-17, 2013, Brisbane, Australia. (Best Paper Award)
  8. Jia Wu, Xingquan Zhu, Chengqi Zhang, and Zhihua Cai, Multi-Instance Multi-Graph Dual Embedding Learning, Proceedings of the 13th IEEE International Conference on Data Mining (ICDM '13), Dec. 7-10, 2013, Dallas, Texas, USA.
  9. Chun Zhou, Peng Zhang, Jing Guo, Xingquan Zhu and Li Guo, UBLF: An Upper Bound Based Approach to Discover Influential Nodes in Social Networks, Proceedings of the 13th IEEE International Conference on Data Mining (ICDM '13), Dec. 7-10, 2013, Dallas, Texas, USA.
  10. Meng Fang, Jie Yin, and Xingquan Zhu, Transfer Learning across Networks for Collective Classification, Proceedings of the 13th IEEE International Conference on Data Mining (ICDM '13), Dec. 7-10, 2013, Dallas, Texas, USA.
  11. Meng Fang, Jie Yin, Xingquan Zhu, and Chengqi Zhang, Active Class Discovery and Learning for Networked Data, Proceedings of the 13th SIAM International Conference on Data Mining (SDM '13), May 2-4, 2013, Austin, Texas, USA.
  12. Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu, Graph Stream Classification using Labelled and Unlabeled Graphs, Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE '13), April 8-11, 2013, Brisbane, Australia.
  13. Meng Fang, Xingquan Zhu, Bin Li, Wei Ding, and Xindong Wu, Self-Taught Active Learning from Crowds, Proceedings of the 12th IEEE International Conference on Data Mining (ICDM '12), December 10-13, 2012, Brussels, Belgium.
  14. Bin Li, Xingquan Zhu, Lianhua Chi, and Chengqi Zhang, Nested Subtree Hash Kernels for Large-scale Graph Classification over Streams, Proceedings of the 12th IEEE International Conference on Data Mining (ICDM '12), December 10-13, 2012, Brussels, Belgium.
  15. Meng Fang and Xingquan Zhu, I Don't Know the Label: Active Learning with Blind Knowledge, Proceedings of the 21st International Conference on Pattern Recognition (ICPR '12), November 11-15, 2012, Tsukuba, Japan. (Best Student Paper Award)
  16. Shirui Pan and Xingquan Zhu, CGStream: Continuous Correlated Graph Query for Data Streams, Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM '12), Oct. 29 - Nov. 2, 2012, Hawaii, USA.
  17. Guodong Song, Ling Chen, Xingquan Zhu, and Chengqi Zhang, TCSST: Transfer Classification of Short & Sparse Text Using External Data, Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM '12), Oct. 29 - Nov. 2, 2012, Hawaii, USA.
  18. Dan He, Xingquan Zhu, and D. Stott Parker, How Does Research Evolve? Pattern Mining for Research Meme Cycles, Proceedings of the 11th IEEE International Conference on Data Mining (ICDM '11), Dec. 11-14, 2011, Vancouver, Canada.
  19. Peng Zhang, Byron J. Gao, Xingquan Zhu, and Li Guo, Enabling Fast Lazy Learning for Data Streams, Proceedings of the 11th IEEE International Conference on Data Mining (ICDM '11), Dec. 11-14, 2011, Vancouver, Canada.
  20. Peng Zhang, Jun Li, Peng Wang, Byron J. Gao, Xingquan Zhu, and J. Gao, Enabling Efficient Prediction for Ensemble Models on Data Streams, Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '11), San Diego, CA, USA, August 21-24, 2011.
  21. Ruijiang Li, Bin Li, Cheng Jin, Xiangyang Xue, and Xingquan Zhu, Tracking User-Preference Varying Speed in Collaborative Filtering, Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI '11), San Francisco, USA, August 7-11, 2011.
  22. Bin Li, Xingquan Zhu, Ruijiang Li, Chengqi Zhang, Xiangyang Xue, and Xindong Wu, Cross Domain Collaborative Filtering over Time, Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI '11), Barcelona, Spain, July 16-22, 2011.
  23. Peng Zhang, Xingquan Zhu, Jianlong Tan, and Li Guo, Classifier and Cluster Ensembling for Mining Concept Drifting Data Streams, Proceedings of the 10th IEEE International Conference on Data Mining (ICDM '10), Dec. 14-17, 2010, Sydney, Australia.
  24. Zhengyu Lu, Xindong Wu, Xingquan Zhu, and Josh Bongard, Ensemble Pruning via Individual Contribution Ordering, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '10), July 25-28, 2010, Washington, DC, USA.
  25. Xingquan Zhu, Xindong Wu, and Chengqi Zhang, Vague One-Class Learning for Data Streams, Proceedings of the 9th IEEE International Conference on Data Mining (ICDM '09), December 6-9, 2009, Miami, FL, USA.
  26. Peng Zhang, Xingquan Zhu, and Li Guo, Mining Data Streams with Labeled and Unlabeled Training Examples, Proceedings of the 9th IEEE International Conference on Data Mining (ICDM '09), December 6-9, 2009, Miami, FL, USA.
  27. Xingquan Zhu and Ruoming Jin, Multiple Information Sources Cooperative Learning, Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI '09), July 11-17, 2009, Pasadena, California, USA.
  28. Xingquan Zhu, Peng Zhang, Xindong Wu, Dan He, Chengqi Zhang, and Yong Shi, Cleansing Noisy Data Streams, Proceedings of the 8th IEEE International Conference on Data Mining (ICDM '08), December, 2008, Pisa, Italy.
  29. Peng Zhang, Xingquan Zhu, and Yong Shi, Categorizing and Mining Concept Drifting Data Stream, Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '08), August, 2008, Las Vegas, USA.
  30. Xingquan Zhu, Lazy Learning for Classifying Imbalanced Data, Proceedings of the 7th IEEE International Conference on Data Mining (ICDM '07), October 28-31, 2007, Omaha, USA.
  31. Xingquan Zhu, Peng Zhang, Xiaodong Lin, and Yong Shi, Active Learning from Data Streams, Proceedings of the 7th IEEE International Conference on Data Mining (ICDM '07), October 28-31, 2007, Omaha, USA.
  32. Xingquan Zhu and Xindong Wu, Discovering Relational Patterns across Multiple Databases, Proceedings of the 23rd IEEE International Conference on Data Engineering (ICDE '07), April 15-20, 2007, Istanbul, Turkey.
  33. Xingquan Zhu, Xindong Wu, Taghi Khoshgoftaar, and Yong Shi, An Empirical Study of the Noise Impact on Cost-Sensitive Learning, Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI '07), January 6-12, 2007, Hyderabad, India.
  34. Xingquan Zhu and Xindong Wu, Mining Complex Patterns across Sequences with Gap Requirements, Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI '07), January 6-12, 2007, Hyderabad, India.

Selected Professional Services [Full List]

Associate Editors
  • ACM Transactions on Knowledge Discovery from Data (TKDD) (2018-Date)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE) (2008-2012, 2014-2021)
  • International Journal of Social Network Analysis and Mining (SNAM) (2010-Date)
  • Journal of Big Data (JBD) (2013-Date)
  • Network Modeling Analysis in Health Informatics and Bioinformatics (NetMAHIB)(2014-Date)
  • International Journal of Monitoring & Surveillance Technologies Research (IJMSTR)(2014-Date)
  • Advances in Data Warehousing and Mining (ADWM) (2007-Date)
  • Knowledge and Information Systems Journal (KAIS) (2003-2008)
Professional Society Board/Fellow Service
Grant Panelists/Reviewers
  • National Science Foundation, USA
  • National Institute of Health, USA
  • Swiss National Science Foundation, Swiss
  • Natural Sciences and Engineering Research Council, Canada
  • Marsden Fund Council, Royal Society of New Zealand, New Zealand
  • Australian Research Council, Australia
  • Croatian Science Foundation, Croatia
  • National Science Foundation, China
  • Research Grants Council, Hongkong, China
Conference General Co-Chair
  • BigData-2021: The 2021 IEEE International Conference on Big Data (BigData 2021), Orlando, FL, USA, December 15-18, 2021 [Call for paper]
  • ICMLA-2012: The Eleventh IEEE International Conference on Machine Learning and Applications (ICMLA 2012), Boca Raton, USA, December 12-15, 2012
Program Committee Co-Chair
  • CSoNet-2023: The 12th International Conference on Computational Data and Social Networks (CSoNet-2023), Hanoi, Vietnam, Dec 11 - Dec 13, 2023. [Call for papers]
  • ICDM-2022: The 22nd IEEE International Conference on Data Mining (ICDM-2022), Orlando, FL, USA, Nov 30 - Dec 3, 2022. [Call for papers]
  • SSDBM-2021: The 33rd International Conference on Scientific and Statistical Database Management (SSDBM-2021), Tampa, FL, USA, July 6-7, 2021.
  • ADMA-2018: The 14th International Conference on Advanced Data Mining and Applications (ADMA-2018), Nanjing, China, Nov. 16-18, 2018.
  • BIBE-2014: The 14th IEEE International Conference on BioInformatics and BioEngineering (BIBE-2014), Boca Raton, FL, USA, Nov. 10-12, 2014.
  • GRC-2013: The 2013 IEEE International Conference on Granular Computing (GRC-2013), Beijing, China, Dec. 13-15, 2013.
  • ICTAI-2011: The 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2011), Boca Raton, FL, USA, Nov. 7-9, 2011.
  • ICMLA-2010: The Ninth IEEE International Conference on Machine Learning and Applications (ICMLA-2010), Washington DC, USA, Dec. 12-14, 2010.
Program Committee Vice-Chair (Senior PC)
  • KDD-2019: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Anchorage, Alaska, USA, August 4-8, 2019
  • AAAI-2019: The 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, Jan. 27 - Feb. 1, 2019
  • ICDM-2018: The 18th IEEE International Conference on Data Mining, Singapore, November 17-20, 2018
  • KDD-2018: The 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, London, UK, August 21-23, 2018
  • BigData-2017: The IEEE International Conference on Big Data, Boston, USA, Dec. 11-14, 2017
  • ICDM-2017: The 17th IEEE International Conference on Data Mining, New Orleans, USA, Nov. 18-21, 2017
  • ICTAI-2017: The 29th IEEE International Conference on Tools with Artificial Intelligence, Boston, USA, Nov. 6-8, 2017
  • ICTAI-2015: The 27th IEEE International Conference on Tools with Artificial Intelligence, Salerno, Italy, Nov. 9-11, 2015
  • ICDM-2013: The 13th IEEE International Conference on Data Mining, Dallas, Texas, USA, Dec. 8-11, 2013
  • CIKM-2013: The 22nd ACM International Conference on Information and Knowledge Management (KM Track), San Francisco, CA, USA. Oct. 27 - Nov. 1, 2013.
  • ICDM-2011: The 11th IEEE International Conference on Data Mining, Vancouver, Canada, Dec. 11-14, 2011.
  • CIKM-2011: The 20th ACM International Conference on Information and Knowledge Management (KM Track), Glasgow, Scotland, UK, Oct. 24-28, 2011.
  • ICMLA-2011: The 2011 IEEE International Conference on Machine Learning and Applications, Honolulu, Hawaii, USA, on December 18-21, 2011.
  • CIKM-2010: The 19th ACM International Conference on Information and Knowledge Management (KM Track), Toronto, Canada, Oct. 26-30, 2010.
Workshop Chair
  • WISE-2015: The 16th International Conference on Web Information Systems Engineering, Miami, FL USA, October 18-20, 2015.
  • ASONAM-2015: The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France, August 25-28, 2015.
Tutorial Chair
  • ICDM-2013: The 13th IEEE International Conference on Data Mining (ICDM-2013), Dallas Texas, USA, Dec. 8-11, 2013.
  • ADMA-2012: The 8th International Conference on Advanced Data Mining and Applications (ADMA 2012), Dec. 15-18, Nanjing, China 2012.
Finance Chair
  • ICTAI-2011: The 23rd IEEE International Conference on Tools with Artificial Intelligence, Boca Raton, FL, USA, Nov. 7-9, 2011.
  • ICDM-2010: The 10th IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia, Dec. 14-17, 2010.
Student Travel Award Chair
  • KDD-2015: The 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Sydney, Australia, August 10-13, 2015.
Registration Chair
  • KDD-2012: The 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 2012.
Program Committee (over 200 times)
  • AI: The Australian Joint Conference on Artificial Intelligence (2012-2008)
  • ACML: Asian Conference on Machine Learning (2013-2009)
  • ASONAM: The International Conference on Advances in Social Networks Analysis and Mining (2013-2010)
  • CIKM: ACM International Conference on Information and Knowledge Management (2013-2010).
  • ECML/PKDD: The 2011 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2011)
  • ICDM: The IEEE International Conference on Data Mining (2015-2009, 2007-2003)
  • KDD: The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015-2010, 2007)
  • PAKDD: The Pacific-Asia Conference on Knowledge Discovery and Data Mining (2015-2007)
  • SDM: The SIAM International Conference on Data Mining (2015-2012, 2009)
  • SEKE: The International Conference on Software Engineering and Knowledge Engineering (2015-2007)
  • WSDM: ACM International Conference on Web Search and Data Mining (2014, 2013)
  • WAIM: The International Conference on Web-Age Information management (2013, 2012, 2011, 2009)
Research Grants

Active Research Projects:
  • Collaborative Research: III: Small: Taming Large-Scale Streaming Graphs in an Open World, (Role: PI; Sponsor: National Science Foundation), IIS-2236579
  • NSF-CSIRO: Towards Interpretable and Responsible Graph Modeling for Dynamic Systems, (Role: PI; Sponsor: National Science Foundation), IIS-2302786
  • III: Medium: Collaborative Research: KMELIN: Knowledge Mining and Embedding Learning for Complex Dynamic Information Networks, (Role: PI; Sponsor: National Science Foundation), IIS-1763452. [Project Website][News Release]
  • Collaborative Research: Implementation: Medium: Secure, Resilient Cyber-Physical Energy System Workforce Pathways via Data-Centric, Hardware-in-the-Loop Training, (Role: Co-PI; Sponsor: National Science Foundation), OAC-2320972
  • Making the Master's Degree in Artificial Intelligence Accessible to High-Achieving Low-Income Students, (Role: Co-PI; Sponsor: National Science Foundation), DUE-2030854
Expired Research Projects:
  • NSF Student Travel Support for the 2022 IEEE International Conference on Data Mining (IEEE ICDM 2022), (Role: PI; Sponsor: National Science Foundation), IIS-2226627
  • RAPID: COVID-19 Coronavirus Testbed and Knowledge Base Construction and Personalized Risk Evaluation. (Role: PI; Sponsor: National Science Foundation), IIS-2027339. [Project Website][News Release] [News Release] [News Release]
  • NSF Student Travel Grant for the 2021 IEEE International Conference on Big Data (IEEE BigData 2021), (Role: PI; Sponsor: National Science Foundation), IIS-2129417
  • MRI: Acquisition of Artificial Intelligence & Deep Learning (AIDL) Training and Research Laboratory. (Role: PI; Sponsor: National Science Foundation), CNS-1828181. [Project Website][News Release]
  • Development of Curriculum and Hands-on Deep Learning Labs for IoT Cybersecurity, (Role: Co-PI; Sponsor: Cyber Floria)
  • Privacy Preserving Protocols for Big Data Analytics. (Role: Co-PI; Sponsor: FAU College of Engineering \& Computer Science)
  • Real-Time Bidding Price Optimization. (Role: PI; Sponsor: Bidtellect Inc.)
  • NSF I/UCRC: Application of Common Machine Learning Algorithms for Uses Cases in Auto Industry – Phase 2 (Role: PI, Sponsor: JM Family Enterprises Inc.)
  • NSF I/UCRC: Machine Learning Algorithms for Uses Cases in Auto Industry (JM Family Enterprises Inc.), (Role: PI, Sponsor: JM Family Enterprises Inc.)
  • PFI:AIR - TT: A Clinical Predictive Model Based Smart Decision Support System for Congestive Obstructive Pulmonary Disease (COPD) related Re-hospitalization, (Role: Co-PI; Sponsor: National Science Foundation, IIP: 1444949)
  • MRI: Acquisition of Big Data Training and Research Laboratory (Role: Co-PI; National Science Foundation, CNS-1427536)
  • RED-CAKE: Novel Data Mining Approaches for Knowledge Based Skill Matching for Employers (Role: PI; Sponsor: FAU I/UCRC, incVersity)
  • Mining Multiple Information Sources through Collaborative and Comparative Analysis (Role: CI; Sponsor: Australian Research Council)
  • Database-centric data analysis of molecular simulations (Role: PI; Sponsor: NIH-1R01GM086707-01A1, subcontract)
  • Comparative Pancreatic Cancer Study Using Discriminative Gene Regulatory Network (Role: PI; Sponsor: American Cancer Society ACS-IRG, subcontract)
  • Pattern Matching with Wildcards and Length Constraints (Role: Co-PI; Sponsor: National Science Foundation, CCF-0514819)
  • ICHECK: Identifying Deception Data with Impact-Sensitive Instance Ranking (Role: PI; Sponsor: National Science Foundation, subcontract EPS-0236976)
Teaching

Course Offered/Offering at FAU Course Offered at UVM
Award and Membership


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