Houping Xiao
Assistant Professor Institute for Insight- Education
- Ph.D., Computer Science, The State University of New York at Buffalo
- B.S., Statistics, Beijing Normal University
- Specializations
- data mining and machine learning
- business data analytics
- FinTech
- Biography
Dr. Xiao is a tenure-track assistant professor in the J. Mack Robinson College of Business at Georgia State University. He is a primary faculty with Institute for Insight. He got his Ph.D. in Computer Science and Engineering from the State University of New York at Buffalo under Dr. Jing Gao. He also holds a Bachelor of Science degree in Statistics from Beijing Normal University.
His research interests lie primarily in data mining and machine learning, especially for large-scale heterogeneous data. He is generally interested in deep learning, text mining, and optimization and their applications in business and education. His work has been applied in Fintech and computerized adaptive testing (CAT).
- Publications
- Xiao, H., Xia, Y., and Baird, A. (2024). Predicting Digital Product Performance With Team Composition Features Derived From A Graph Network. Decision Support Systems.
- Shen, Y., Wang, S., and Xiao, H. (2024). A Two-Step Item Bank Calibration Strategy Based On 1-Bit Matrix Completion for Small-Scale Computerized Adaptive Testing. British Journal of Mathematical and Statistical Psychology.
- Murray, S., Xia, Y., and Xiao, H. (2023). Charting By Machines. Journal of Financial Economics.
- Xiao, H., and Wang, S. (2022). A Joint Maximum Likelihood Estimation Framework for Truth Discovery: A Unified Perspective. IEEE Transactions on Knowledge and Data Engineering (TKDE).
- Xiao, H., and Wang, S. (2022). Toward Quality of Information Aware Distributed Machine Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(6), 1-28.
- Wang, S., Xiao, H., and Cohen, A. (2021). Adaptive weight estimation of latent ability: application to computerized adaptive testing with response revision. Journal of Educational and Behavioral Statistics, 46(5), 560-591.
- Che, L., Long, Z., Wang, J., Wang, Y., Xiao, H., and Ma, F. (2021). Fedtrinet: A pseudo labeling method with three players for federated semi-supervised learning. IEEE International Conference on Big Data (Big Data), 715-724.
- Li, Y., Xiao, H., Qin, Z., Miao, C., Su, L., Gao, J., Ren, K., and Ding, B. (2020). Towards differentially private truth discovery for crowd sensing systems. IEEE International Conference on Distributed Computing Systems (ICDCS), 1156-1166.
- Ma, F., Wang, Y., Gao, J., Xiao, H., and Zhou, J. (2020). Rare Disease Prediction by Generating Quality-Assured Electronic Health Records. SIAM International Conference on Data Mining (SDM). 514-522. Society for Industrial and Applied Mathematics.
- Zhao, K., Xiao, H., and Rai, A. (2020). Toward Effective Mobile Promotion: A Survey of Mobile Prediction Techniques and Applications. Americas' Conference on Information Systems (AMCIS).
- Wang, T., Xiao, H., Ma, F., and Gao, J. (2019). IProWA: A novel probabilistic graphical model for crowdsourcing aggregation. IEEE International Conference on Big Data (Big Data), 677-682.
- Ma, F., Wang, Y., Xiao, H., Yuan, Y., Chitta, R., Zhou, J., and Gao, J. (2019). Incorporating medical code descriptions for diagnosis prediction in healthcare. BMC medical informatics and decision making, 19(6), 1-13.
- Xiao, H., Gao, J., Li, Q., Ma, F., Su, L., Feng, Y., and Zhang, A. (2018). Towards confidence interval estimation in truth discovery. IEEE Transactions on Knowledge and Data Engineering (TKDE), 31(3), 575-588.
- Jin, H., Su, L., Xiao, H., and Nahrstedt, K. (2018). Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems. IEEE/ACM Transactions on Networking (TON), 26(5), 2019-2032.
- Xiao, H., Wang, F., Ma, F., and Gao, J. (2018). eOTD: An efficient online tucker decomposition for higher order tensors. IEEE International Conference on Data Mining (ICDM), 1326-1331.
- Xiao, H., Gao, J., Vu, L., and Turaga, D. S. (2017). Detecting malicious behavior in computer networks via cost-sensitive and connectivity constrained classification. SIAM International Conference on Data Mining (SDM), 117-125. Society for Industrial and Applied Mathematics.
- Xiao, H., Gao, J., Vu, L., and Turaga, D. S. (2017). Learning temporal state of diabetes patients via combining behavioral and demographic data. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2081-2089.
- Ma, F., Meng, C., Xiao, H., Li, Q., Gao, J., Su, L., and Zhang, A. (2017). Unsupervised discovery of drug side-effects from heterogeneous data sources. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 967-976.
- Xiao, H., Gao, J., Li, Q., Ma, F., Su, L., Feng, Y., and Zhang, A. (2016). Towards confidence in the truth: A bootstrapping based truth discovery approach. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 1935-1944.
- Xiao, H., Gao, J., Wang, Z., Wang, S., Su, L., and Liu, H. (2016). A truth discovery approach with theoretical guarantee. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 1925-1934.
- Xiao, H., Li, Y., Gao, J., Wang, F., Ge, L., Fan, W., Vu, L. H., and Turaga, D. S. (2015). Believe it today or tomorrow? detecting untrustworthy information from dynamic multi-source data. SIAM International Conference on Data Mining (SDM), 397-405. Society for Industrial and Applied Mathematics.
- Xiao, H., Gao, J., Turaga, D. S., Vu, L. H., and Biem, A. (2015). Temporal multi-view inconsistency detection for network traffic analysis. International Conference on World Wide Web (WWW), 455-465.