Jie Yin

Photo of Jie Yin

PhD HKUST; BEng, BA XJTU
Senior Lecturer

Rm 4035
H70 - Abercrombie Building
The University of Sydney
NSW 2006 Australia

Telephone +61 2 8627 7030
jie.yin@sydney.edu.au
Web Jie Yin's site

Bio

Jie Yin is a Senior Lecturer of Business Analytics at the University of Sydney Business School. Prior to joining the University of Sydney in 2018, she worked as a Senior Research Scientist at CSIRO, where she led innovative research on a wide range of research projects across various disciplines such as social media monitoring, healthcare, and environmental monitoring. Jie obtained her Ph.D. in Computer Science from the Hong Kong University of Science and Technology, and her Bachelor of Engineering and Bachelor of Economics from Xi’an Jiaotong University.

Jie Yin’s main research interests include data mining, machine learning, and their applications to text mining, social media mining, sensor data mining, network analytics, and decision support systems. Jie has published more than 50 research papers in top-tier international conferences (AAAI, IJCAI, CIKM, ICDM, PKDD/ECML, WWW, etc.) and journals (IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cybernetics, Data Mining and Knowledge Discovery, ACM Transactions on Information Systems, etc.). She has served as the PC of all major conferences in her area including KDD, ICDM, KDD, AAAI, CIKM, etc. She was co-chair of the International Workshop on Social Web for Disaster Management (SWDM 2015, SWDM 2016, and SWDM 2018).

Research Interests

Jie Yin’s research focuses on developing novel machine learning algorithms for analysing complex modern data, ranging from time series data, spatial-temporal data, geo-social media data, unstructured text, to large-scale networks. Her research has covered a variety of emerging applications, such as text analytics, environmental monitoring, social network mining, graph analytics, and healthcare, etc.

Jie Yin’s research work on time series analysis involves developing Bayesian probabilistic models for prediction and classification, clustering high-dimensional data, and detecting spatio-temporal events. She has also worked on weakly supervised methods for analysing multi-modal sensor data for localization and activity recognition in healthcare applications.

Jie Yin has worked on developing machine learning algorithms to analyse unstructured text (for example, social media). Social media text analysis is difficult given the noisy and informal nature of data, the variation in the ways one can refer to things (synonyms and paraphrase), and difficulties in understanding the role of context in inference. The novel methodologies developed by Jie Yin and her collaborators include burst detection, short text classification, online clustering, and geolocation, which extract useful sights from large volumes of social media data to assist emergency officers in better making informed decisions and acting on emergency responses.

In recent years, Jie Yin has started to conduct research on network analytics because of the ubiquity and importance of networked data. The large scale and dynamic nature of networks, as well as the lack of labelled data make it very challenging to tackle many network analytical tasks. She has worked on active learning and exploration over large-scale networks, supervised sampling, and transfer learning across networks.

Jie Yin has recently focused on developing network representation learning algorithms that embed large-scale networks into a latent, low-dimensional vector space. The core idea is to fully encode network structure and node content features to learn a better network representation. Her research has tackled the heterogeneity of network structure and node content features in two main forms: (1) rich textual features in citation networks or collaborative networks, and (2) sparse and noisy user profiles in social networks.  Together with her collaborators, she has developed a series of network embedding techniques that enable vector-based machine learning algorithms to be directly applied to solve various network analytic tasks, such as node classification, clustering, link prediction, and community detection.

A new surge of Jie Yin’s current and future research interests also includes developing interpretable machine learning models and combining machine learning with decision making. This involves understanding complex deep learning models with interpretation and extracting useful sights to inform policy making in various business and health domains.

Selected publications

2019

Journal Articles

Fang M, Zhou T, Yin J, Wang Y, and Tao D (2019) Data Subset Selection With Imperfect Multiple Labels IEEE Transactions on Neural Networks and Learning Systems, In Press. [More Information]

Zhang D, Yin J, Zhu X, and Zhang C (2019) Network Representation Learning: A Survey IEEE Transactions on Big Data, In Press. [More Information]

2018

Journal Article

Zheng Y, Peng H, Zhang X, Zhao Z, Yin J, and Li J (2018) Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases BMC Bioinformatics, 19, 49-59. [More Information]

Conference Proceedings

Lin Y, Castillo C, and Yin J (2018) The 5th International Workshop on Social Web for Disaster Management (SWDM'2018): Collective Sensing, Trust, and Resilience in Global Crises 11th ACM International Conference on Web Search and Data Mining (WSDM'18); ACM, New York. [More Information]

Wu H, Wang C, Yin J, Lu K, and Zhu L (2018) Sharing Deep Neural Network Models with Interpretation 27th International World Wide Web Conference (WWW'18); International World Wide Web Conferences Steering Committee, Lyon. [More Information]

Zhang D, Yin J, Zhu X, and Zhang C (2018) SINE: Scalable Incomplete Network Embedding 18th IEEE International Conference on Data Mining (ICDM 2018); Institute of Electrical and Electronics Engineers (IEEE), Singapore, 17-20 November 2018. [More Information] [More Information]

Book Chapters

Yin J, Tran S, and Zhang Q (2018) Human Identification via Unsupervised Feature Learning from UWB Radar Data Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science, vol 10937; Springer, Cham, Switzerland, 322-334. [More Information]

Zhang D, Yin J, Zhu X, and Zhang C (2018) MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science, vol 10938; Springer, Cham, Switzerland, 196-208. [More Information]

2017

Journal Articles

Fang M, Yin J, Hall L, and Tao D (2017) Active Multitask Learning With Trace Norm Regularization Based on Excess Risk IEEE Transactions on Cybernetics, 47 (11), 3906-3915. [More Information]

Freyne J, Yin J, Brindal E, Hendrie G, Berkovsky S, and Noakes M (2017) Push Notifications in Diet Apps: Influencing Engagement Times and Tasks International Journal of Human-Computer Interaction, 33 (10), 833-845. [More Information]

Xiao Y, Liu B, Yin J, and Hao Z (2017) A multiple-instance stream learning framework for adaptive document categorization Knowledge-Based Systems, 120, 198-210. [More Information]

Conference Proceeding

Zhang D, Yin J, Zhu X, and Zhang C (2017) User Profile Preserving Social Network Embedding 26th International Joint Conference on Artificial Intelligence (IJCAI 2017); International Joint Conferences on Artificial Intelligence, Melbourne.

2016

Journal Articles

Fang M, Yin J, and Zhu X (2016) Supervised sampling for networked data Signal Processing, 124, 93-102. [More Information]

Fang M, Yin J, and Zhu X (2016) Active exploration for large graphs Data Mining and Knowledge Discovery, 30 (3), 511-549. [More Information]

Ma J, Ni W, Yin J, Liu R, Yuan Y, and Fang B (2016) Modeling mobile cellular networks based on social characteristics International Journal of Computers Communications and Control, 11 (4), 480-492.

Conference Proceedings

Castillo C, Diaz F, Lin Y, and Yin J (2016) The Fourth International Workshop on Social Web for Disaster Management (SWDM 2016) 25th ACM International Conference on Information and Knowledge Management (CIKM 2016); Association for Computing Machinery (ACM), New York. [More Information]

Fang M, Yin J, Zhu X, and Zhang C (2016) TrGraph: Cross-network transfer learning via common signature subgraphs 32nd IEEE International Conference on Data Engineering (ICDE 2016); Institute of Electrical and Electronics Engineers (IEEE), Piscataway. [More Information]

Yin J, Fang M, Mokhtari G, and Zhang Q (2016) Multi-resident Location Tracking in Smart Home through Non-wearable Unobtrusive Sensors 14th International Conference on Smart Homes and Health Telematics, ICOST 2016; Springer Verlag, China. [More Information]

Yu B, Fang M, Tao D, and Yin J (2016) Submodular Asymmetric Feature Selection in Cascade Object Detection 30th AAAI Conference on Artificial Intelligence (AAAI 2016); AAAI Press, United States.

Zhang D, Yin J, Zhu X, and Zhang C (2016) Homophily, Structure, and Content Augmented Network Representation Learning 16th IEEE International Conference on Data Mining, ICDM 2016; Institute of Electrical and Electronics Engineers (IEEE), Piscataway. [More Information]

Zhang D, Yin J, Zhu X, and Zhang C (2016) Collective classification via discriminative matrix factorization on sparsely labeled networks 25th ACM International Conference on Information and Knowledge Management (CIKM 2016); Association for Computing Machinery (ACM), New York. [More Information]

2015

Journal Articles

Fang M, Yin J, Zhu X, and Zhang C (2015) TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs IEEE Transactions On Knowledge And Data Engineering, 27 (9), 2536-2549. [More Information]

Karimi S, Yin J, and Baum J (2015) Evaluation methods for statistically dependent text Computational Linguistics, 41 (3), 539-548. [More Information]

Conference Proceedings

Ma J, Ni W, Yin J, Lin S, Cui H, Liu R, and Fang B (2015) Modelling Social Characteristics of Mobile Radio Networks IEEE International Conference on Communication Workshop, ICCW 2015; Institute of Electrical and Electronics Engineers (IEEE), United States. [More Information]

Yin J, Karimi S, Lampert A, Cameron M, Robinson B, and Power R (2015) Using social media to enhance emergency situation awareness: Extended Abstract 24th International Joint Conference on Artificial Intelligence (IJCAI 2015); AAAI Press, Buenos Aires.

Yin J, Zhang Q, and Karunanithi M (2015) Unsupervised daily routine and activity discovery in smart homes 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015); Institute of Electrical and Electronics Engineers (IEEE), Piscataway. [More Information]

2014

Conference Proceedings

Fang M, Yin J, and Tao D (2014) Active learning for crowdsourcing using knowledge transfer 28th AAAI Conference on Artificial Intelligence (AAAI 2014); AAAI Press, Quebec City.

Yin J, Karimi S, and Lingad J (2014) Pinpointing locational focus in microblogs 19th Australasian Document Computing Symposium, ADCS 2014; Association for Computing Machinery (ACM), Australia. [More Information]

2013

Conference Proceedings

Fang M, Yin J, and Zhu X (2013) Active Exploration: Simultaneous Sampling and Labeling for Large Graphs 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013; ACM, United States. [More Information]

Fang M, Yin J, and Zhu X (2013) Knowledge transfer for multi-labeler active learning European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013; Springer, Czech Republic. [More Information]

Fang M, Yin J, and Zhu X (2013) Transfer Learning across Networks for Collective Classification IEEE 13th International Conference on Data Mining (ICDM 2013); Institute of Electrical and Electronics Engineers (IEEE), Piscataway. [More Information]

Fang M, Yin J, Zhu X, and Zhang C (2013) Active Class Discovery and Learning for Networked Data 2013 SIAM International Conference on Data Mining (SDM13); Society for Industrial and Applied Mathematics (SIAM), Texas, USA.

Karimi S, Yin J, and Paris C (2013) Classifying Microblogs For Disasters 18th Australasian Document Computing Symposium, ADCS 2013; ACM, Australia.

Lingad J, Karimi S, and Yin J (2013) Location Extraction From Disaster-Related Microblogs WWW '13 22nd International World Wide Web Conference; International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland.

Yin J (2013) Clustering Microtext Streams for Event Identification Sixth International Joint Conference on Natural Language Processing (IJCNLP'13); Asian Federation of Natural Language Processing, Nagoya, Japan.

2012

Journal Articles

Pan J, Pan S, Yin J, Ni L, and Yang Q (2012) Tracking mobile users in wireless networks via semi-supervised colocalization IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (3), 587-600. [More Information]

Yin J, Lampert A, Cameron M, Robinson B, and Power R (2012) Using social media to enhance emergency situation awareness IEEE Intelligent Systems, 27 (6), 52-59. [More Information]

Conference Proceedings

Cameron M, Power R, Robinson B, and Yin J (2012) Emergency Situation Awareness from Twitter for Crisis Management 21st Annual Conference on World Wide Web, WWW'12; ACM, France. [More Information]

Karimi S, Yin J, and Thomas P (2012) Searching and Filtering Tweets: CSIRO at the TREC 2012 Microblog Track Twenty-First Text REtrieval Conference (TREC'12); US National Institute of Standards and Technology, Gaithersburg, Maryland, USA.

Yin J, Karimi S, Robinson B, and Cameron M (2012) ESA: Emergency Situation Awareness via Microbloggers 21st ACM International Conference on Information and Knowledge Management (CIKM12); Association for Computing Machinery (ACM), New York. [More Information]

Yin J, Thomas P, Narang N, and Paris C (2012) Unifying local and global agreement and disagreement classification in online debates 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis (WASSA'12); Association for Computational Linguistics (ACL), Stroudsburg, USA.

2011

Conference Proceeding

Xiao Y, Liu B, Yin J, Cao L, Zhang C, and Hao Z (2011) Similarity-based approach for positive and unlabeled learning 22nd International Joint Conference on Artificial Intelligence; AAAI Press, Menlo Park, California, USA.

2010

Conference Proceedings

Liu B, Yin J, Xiao Y, Cao L, and Yu P (2010) Exploiting Local Data Uncertainty to Boost Global Outlier Detection 10th IEEE International Conference on Data Mining (ICDM 2010); Institute of Electrical and Electronics Engineers (IEEE), USA. [More Information]

Xiao Y, Liu B, Cao L, Yin J, and Wu X (2010) SMILE: A Similarity-Based Approach for Multiple Instance Learning 10th IEEE International Conference on Data Mining (ICDM 2010); Institute of Electrical and Electronics Engineers (IEEE), USA. [More Information]

2009

Conference Proceedings

Hu D, Zhang X, Yin J, Zheng V, and Yang Q (2009) Abnormal Activity Recognition Based on HDP-HMM Models Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09); AAAI Press, Menlo Park, California.

Yin J, Hu D, and Yang Q (2009) Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09); AAAI Press, Menlo Park, California.

2008

Journal Articles

Yin J, Yang Q, and Ni L (2008) Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation IEEE Transactions on Mobile Computing, 7 (7), 869-883. [More Information]

Yin J, Yang Q, and Pan J (2008) Sensor-Based Abnormal Human-Activity Detection IEEE Transactions On Knowledge And Data Engineering, 20 (8), 1082-1090. [More Information]

Yin J, Yang Q, Shen D, and Li Z (2008) Activity recognition via user-trace segmentation ACM Transactions on Sensor Networks, 4 (4), 19:1-19:34. [More Information]

Conference Proceeding

Yin J, and Gaber M (2008) Clustering Distributed Time Series in Sensor Networks Eighth IEEE International Conference on Data Mining (ICDM'08); Institute of Electrical and Electronics Engineers (IEEE), Pisa, Italy. [More Information]

2007

Journal Article

Yang Q, Yin J, Ling C, and Pan T (2007) Extracting Actionable Knowledge from Decision Trees IEEE Transactions On Knowledge And Data Engineering, 19 (1), 43-56. [More Information]

2006

Journal Articles

Chen Y, Yang Q, Yin J, and Chai X (2006) Power-efficient access-point selection for indoor location estimation IEEE Transactions On Knowledge And Data Engineering, 18 (7), 877-888. [More Information]

Shen D, Pan T, Sun J, Pan J, Wu K, Yin J, and Yang Q (2006) Query Enrichment for Web-Query Classification ACM Transactions of Information Systems, 24 (3), 320-352. [More Information]

2005

Journal Article

Shen D, Pan T, Sun J, Pan J, Wu K, Yin J, and Yang Q (2005) Q2C@UST: our winning solution to query classification in KDDCUP 2005 SIGKDD Explorations, 7 (2), 100-110. [More Information]

Conference Proceedings

Yin J, and Yang Q (2005) Integrating hidden Markov models and spectral analysis for sensory time series clustering Fifth IEEE International Conference on Data Mining (ICDM'05); Institute of Electrical and Electronics Engineers (IEEE), Houston, USA. [More Information]

Yin J, Shen D, Yang Q, and Li Z (2005) Activity Recognition through Goal-Based Segmentation Twentieth National Conference on Artificial Intelligence (AAAI-05); Association for the Advancement of Artificial Intelligence, Pittsburgh, USA.

Yin J, Yang Q, and Ni L (2005) Adaptive Temporal Radio Maps for Indoor Location Estimation Third IEEE International Conference on Pervasive Computing and Communications (PerCom'05); Institute of Electrical and Electronics Engineers (IEEE), Kauai Island, Hawaii, USA. [More Information]

2004

Conference Proceeding

Yin J, Chai X, and Yang Q (2004) High-Level Goal Recognition in a Wireless LAN 19th National Conference on Artificial Intelligence, AAAI-2004; AAAI Press, Menlo Park, Calif.

Book Chapter

Yang Q, Chen Y, Yin J, and Chai X (2004) LEAPS: A Location Estimation and Action Prediction System in a Wireless LAN Environment Network and Parallel Computing. NPC 2004. Lecture Notes in Computer Science, vol 3222; Springer, Berlin, Heidelberg, 584-591. [More Information]

2003

Conference Proceeding

Yang Q, Yin J, Ling C, and Chen T (2003) Post-Processing Decision Trees to Extract Actionable Knowledge Third IEEE International Conference on Data Mining - ICDM; Institute of Electrical and Electronics Engineers (IEEE), Los Alamitos. [More Information]

Selected grants

2018

Recent Units Taught

  • BUSS6002 Data Science in Business

  • QBUS6850 Machine Learning for Business