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CSE department

SUNY Buffalo

Jing Gao University at Buffalo

Associate Professor

Department of Computer Science and Engineering

University at Buffalo

350 Davis Hall, Buffalo, NY 14260

Phone: (716)645-1586

Email: jing@buffalo.edu


Brief Bio

I am currently an associate professor in the Computer Science and Engineering Department of the University at Buffalo. I got my PhD from Computer Science Department at University of Illinois at Urbana Champaign in 2011 under the supervision of Prof. Jiawei Han. I received M.E. and B.E. from the Computer Science and Technology Department at Harbin Institute of Technology in China.

For Prospective Students

I am looking for motivated Ph.D. students who are interested in data mining and related areas. Please send me your CV and/or apply to CSE buffalo if you are interested. Graduate admission information can be found here.

Research Interests

I am broadly interested in data and information analysis with a focus on data mining. In particular, I am interested in truth discovery, crowdsourcing, knowledge graphs, multi-source data analysis, anomaly detection, information network analysis, transfer learning and data stream mining. I am also interested in various data mining applications in health care, bioinformatics, transportation, cyber security and education. [Research Overview]

Selected funded research projects

Selected recent publications        Full List       Google Scholar

KDD18

Fenglong Ma, Jing Gao, Qiuling Suo, Quanzeng You, Jing Zhou and Aidong Zhang. Risk Prediction on Electronic Health Records with Prior Medical Knowledge. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, August 2018, 1910-1919. [Paper in PDF]

KDD18

Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, Jing Gao. EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, August 2018, 849-857. [Paper in PDF]

KDD17

Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao. Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada, August 2017, 1903-1911. [Paper in PDF]

KDD16

Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, Aidong Zhang. Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, August 2016, 1935-1944. [Paper in PDF]

Survey

Yaliang Li, Jing Gao, Chuishi Meng, Qi Li, Lu Su, Bo Zhao, Wei Fan, Jiawei Han. A Survey on Truth Discovery. SIGKDD Explorations, 17(12): 1-16, December 2015. [Paper]

VLDB15

Qi Li, Yaliang Li, Jing Gao, Lu Su, Bo Zhao, Murat Demirbas, Wei Fan, Jiawei Han. A Confidence-Aware Approach for Truth Discovery on Long-Tail Data. International Conference on Very Large Data Bases, Kohala Coast, HI, August 2015, 8(4): 425-436. [Paper in PDF]

KDD15

Fenglong Ma, Yaliang Li, Qi Li, Minghui Qui, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Heng Ji, Jiawei Han. FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia, August 2015, 675-684. Acceptance Rate: 159/819 = 19.4%. [Paper in PDF]

SIGMOD14

Qi Li, Yaliang Li, Jing Gao, Bo Zhao, Wei Fan, Jiawei Han. Resolving Conflicts in Heterogeneous Data by Truth Discovery and Source Reliability Estimation. ACM SIGMOD International Conference on Management of Data, Snowbird, UT, June 2014, 1187-1198. [Paper in PDF] [Code&Data in ZIP]


Last updated: January 2019. Copyright (c) 2019 Jing Gao. All rights reserved.