Lianhua Chi

A Introduction of My PhD Research

My research focuses on making sense of big data with effective and agile data analytics, especially with hashing techniques. In particular, I worked on graph stream classification and hashing technique innovation. I’ve built a data mining framework leveraging advanced hashing algorithms to efficiently process massive data without changing the nature of the data much. This research has provided state-of-the-art technologies in big data processing and has a strong impact on data utilisation for social and economic benefits.

My main research interest is in making sense of big data with effective, agile and interactive data analytics, especially in Health care area. Currently, I am working on AI on Autism Screening.

Hashing on Big Data Analysis

Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts, Lianhua Chi, Bin Li, Xingquan Zhu, Shirui Pan and Ling Chen. In Journal of IEEE Transactions on Cybernetics, 2017.
Hashing Techniques: A Survey and Taxonomy, Lianhua Chi and Xingquan Zhu. In Journal of ACM Computing Survey, 2017.
Hashing for large-scale structured data classification, Lianhua Chi. Ph.D. Thesis, 2015.
Context-Preserving Hashing for Fast Text Classification, Lianhua Chi, Bin Li and Xingquan Zhu. SDM, pp. 100--108, 2014.
Fast graph stream classification using discriminative clique hashing, Lianhua Chi, Bin Li and Xingquan Zhu. Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 225--236, 2013.[Best long paper]
Graph hashing and factorization for fast graph stream classification, Ting Guo, Lianhua Chi and Xingquan Zhu. Proceedings of the 22nd ACM international conference on Conference on information and knowledge management, pp. 1607--1612, 2013.
Nested subtree hash kernels for large-scale graph classification over streams, Bin Li, Xingquan Zhu, Lianhua Chi and Chengqi Zhang. IEEE 12th International Conference on Data Mining, pp. 399--408, 2012

Geolocation Prediction in Social Media

End-to-end Network for Twitter Geolocation Prediction and Hashing, Jey Han Lau, Lianhua Chi, Khoi-Nguyen Tran and Trevor Cohn. In the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017).
Geolocation Prediction in Twitter using Location Indicative Words and Textual Features, Lianhua Chi, Kwan Hui Lim, Nebula Alam and Christopher J Butler. In the 2nd Workshop on Noisy User-generated Text (W-NUT), 2016.
Temporal Modelling of Geospatial Words in Twitter, Bo Han, Antonio Jose Jimeno Yepes, Andrew MacKinlay and Lianhua Chi. In Australasian Language Technology Association Workshop, 2016, pp. 133.

Event Impact Analysis

A Novel Method for Assessing Event Impacts on Event-Driven Time Series, Lianhua Chi, Saket Sathe, Bo Han and Yun Wang. IEEE International Workshop on Data Mining for Service (DMS2016) in ICDM, 2016.
Open Problem: Accurately Measuring Event Impacts on Time Series, Lianhua Chi, Bo Han and Yun Wang. The 2nd SIGKDD Workshop on Mining and Learning from Time Series in KDD, 2016.

Time Series Analysis

Comprehensive and efficient discovery of time series motifs, Lianhua Chi, Hehua Chi, Yucai Feng, Shuliang Wang, Zhongsheng Cao. In Journal of Zhejiang University SCIENCE C 12(12), 1000--1009, Springer, 2011.
QoM: An Effective Querying Method for Time Series Database, Yucai Feng, Lianhua Chi, Hehua Chi, Chuanlu Liu and Zhuo Liu. Granular Computing (GrC), 2012 IEEE International Conference on, pp. 129--134, 2012.
Face Image Recognition based on Time Series Motif Discovery, Lianhua Chi, Yucai Feng, Hehua Chi and Ying Huang. Granular Computing (GrC), 2012 IEEE International Conference on, pp. 72--77, 2012.
Mining Time Series Data based Upon Cloud Model, Hehua Chi, Jeubo Wu, Shuliang Wang, Lianhua Chi, Meng Fang. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II, 2010.

Healthcare, Database Security and Face Recognition based on Cloud Model

Improving Multi-Label Chest X-ray Disease Diagnosis by exploiting disease and health labels dependencies. In the 4th International Symposium on Artificial Intelligence and Robotics, 2019.
Autism Screening Using Deep Embedding Representation. In Proceedings of the International Conference on Computational Science 2019
Machine Learning and Optimisation Techniques in Managing Operating Theatres, Hamideh Anjomshoa, Olivia Smith and Lianhua Chi. In 28th European Conference on Operational Research, 2016.
Machine Learning to Predict Patient Demand for Surgeries, Hamideh Anjomshoa and Lianhua Chi. In Proceedings of the 24th National Conference of the Australian Society for Operations Research, pp. 53, 2016.
Efficient authorisation update on cloud data, Zongmin Cui, Hong Zhu, Jie Shi, Lianhua Chi and Ke Yan. In International Journal of Web and Grid Services 12(2), 109--141, Inderscience Publishers (IEL), 2016.
Lightweight Key Management on Sensitive Data in the Cloud, Zongmin Cui, Hong Zhu and Lianhua Chi. In Security and communication networks 6(10), 1290--1299, Wiley Online Library, 2013.
Lightweight Management of Authorization Update on Cloud Data, Zongmin Cui, Hong Zhu, Jie Shi, Lianhua Chi and Ke Yan. In Parallel and Distributed Systems (ICPADS), 2013 International Conference on, pp. 456--461, 2013.
A Cloud Model-based Approach for Facial Expression Synthesis, Juebo Wu, Hehua Chi and Lianhua Chi. In Journal of Multimedia 6(2), 217--224, 2011.
Facial Expression Synthesis based on Cloud Model, Juebo Wu, Hehua Chi, Shuliang Wang and Lianhua Chi. In Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on, pp. 1--4, 2010.
Facial Expression Recognition based on Cloud Model, Hehua Chi, Lianhua Chi, Meng Fang and Juebo Wu. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II, 2010.