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, social science, cyber security and education. [Research Overview]
Selected funded research projects
- NSF-IIS 1553411: Mining Reliable Information from Crowdsourced Data (CAREER Award)
- NSF-CNS 1742845: Understanding and Modeling Rumor Propagation for Vulnerability Assessment of Social Media Platforms
- NSF-IIS 1747614: Medical Knowledge Graph Construction from Heterogeneous Sources
- NSF-IIS 1319973: Conflicts to Harmony: Integrating Massive Data by Trustworthiness Estimation and Truth Discovery
Selected recent publications Full List Google Scholar
| AAAI20 |
Weak Supervision for Fake News Detection via Reinforcement Learning. AAAI Conference on Artificial Intelligence, New York, NY, February 2020, to appear. |
| KDD18 |
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 |
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 |
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 |
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 |
A Survey on Truth Discovery. SIGKDD Explorations, 17(12): 1-16, December 2015. [Paper] |
| VLDB15 |
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 |
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 |
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] |