Postdoctoral Research Fellow (July 2017~present)
College of Computer Science and Technology, Zhejiang University
Working with Prof. Gang Panszhao@zju.edu.cn
Research Interests: Ubiquitous Computing, Smartphone Sensing,
Data Mining, Machine Learning.
Google Scholar Research Gate
I am currently a Postdoctoral Research Fellow of the College of Computer Science and Technology, Zhejiang University. I received the Ph.D. degree in computer science from Zhejiang University in 2017, under the supervision of Prof. Gang Pan. I visited the Human-Computer Interaction Institute at Carnegie Mellon University as a visiting PhD student from September 2015 to September 2016, working with Prof. Anind K. Dey. I won the Best Paper Award of ACM UbiComp’16.
2019/02: Our paper "Discovering Individual Life Style from Anonymized WiFi Scan Lists on Smartphones" is accepted by IEEE Access.
2018/10: Our workshop AppLens2018 was successfully held on October 8, 2018, Singapore
2018/09: Our paper "AppUsage2Vec: Modeling Smartphone App Usage for Prediction" is accepted by ICDE 2019 (CCF-A).
2018/06: Our workshop proposal "AppLens: Mining and Learning from Smartphone Apps for Users" is accepted by the ACM UbiComp 2018 Workshop.
2016/09: Our paper "Discovering Different Kinds of Smartphone Users Through Their Application Usage Behaviors" wins the ACM UbiComp 2016 Best Paper Award.
Sha Zhao, Julian Ramos, Jianrong Tao, Ziwen Jiang, Shijian Li, Zhaohui Wu, Gang Pan, and Anind Dey. Discovering Different Kinds of Smartphone Users Through Their Application Usage Behaviors. The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) [Best Paper Award] (CCF Rank: A) Full paper
Sha Zhao, Zhiling Luo, Ziwen Jiang, Haiyan Wang, Feng Xu, Shijian Li, Jianwei Yin, and Gang Pan. AppUsage2Vec: Modeling Smartphone App Usage for Prediction. The 35th IEEE International Conference on Data Engineering (ICDE2019) (CCF Rank: A) (Accepted)
Sha Zhao, Gang Pan, Yifan Zhao, Jianrong Tao, Jinlai Chen, Shijian Li, and Zhaohui Wu. Mining User Attributes Using Large-scale APP lists of Smartphones. IEEE SYSTEMS JOURNAL (IF = 4.337) (JCR Section II) Full paper
Sha Zhao, Julian Ramos, Jianrong Tao, Ziwen Jiang, Shijian Li, Zhaohui Wu, Gang Pan, and Anind Dey. Who Are the Smartphone Users? -- Identifying User Groups with Apps Usage Behaviors. ACM SIGMOBILE Mobile Computing and Communications Review Full paper
Sha Zhao, Zhe Zhao, Runhe Huang, Zhiling Luo, Shijian Li, Jianrong Tao, Shiwei Cheng, Jing Fan, and Gang Pan. Discovering Individual Life Style from Anonymized WiFi Scan Lists on Smartphones. IEEE Access (IF = 3.557) (JCR Section II)
Sha Zhao, Zhe Zhao, Yifan Zhao, Runhe Huang, Shijian Li, and Gang Pan. Discovering People's Life Patterns from Anonymized WiFi Scanlists. The 11th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2014) (CCF Rank: C) Full paper
Sha Zhao, Yifan Zhao, Zhiling Luo, Runhe Huang, Shijian Li, and Gang Pan. Characterizing a User from Large-scale Smartphone-sensed Data. ACM Workshop on The 2017 International Joint Conference on Pervasive and Ubiquitous Computing (UbiMI 2017)
Sha Zhao, Feng Xu, Zhiling Luo, Shijian Li, and Gang Pan. Demographic Attributes Prediction Through App Usage Behaviors on Smartphones. ACM Workshop on The 2018 International Joint Conference on Pervasive and Ubiquitous Computing (AppLens 2018)
Gang Pan, Zhiwen Yu, Sha Zhao *, Anind K. Dey. Proposal for Workshop on AppLens: Mining and Learning from Smartphone Apps for Users. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers.
Pengfei Li, Hua Lu, Nattiya Kanhabua, Sha Zhao, and Gang Pan. Location Inference for Non-geotagged Tweets in User Timelines. IEEE Transactions on Knowledge and Data Engineering Computing (TKDE) (CCF Rank: A) (IF=2.775)
Sha Zhao, Longbiao Chen, Shijian Li, and Gang Pan. iCPS-Car: An Intelligent Cyber-Physical System for Smart Automobiles. 2013 IEEE International Conference on Cyber, Physical and Social Computing (CPSCom 2013), 818-825. Full paper
Sha Zhao, Shijian Li, Gang Pan, and Hong Li. Design and Implementation of a driving simulation environment. 2011 Chinese Conference on Pervasive Computing (PCC 2011) [in Chinese]
Yang Xu, Shijian Li, Sha Zhao, and Gang Pan. A Novel Telematics Framework Based on Driving Behavior. 2013 Chinese Conference on Pervasive Computing (PCC 2013) [in Chinese]
Longbiao Chen, Yaochun Li, Zeming Zhegn, Li Zhang, Dan He, Xiaolong Li, Sha Zhao, Shijian Li, and Gang Pan. WaterLady: A Case Study for Connecting Physical Devices into Social Networks. The 9th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2012) (CCF Rank: C) Full paper
1. National Natural Science Foundation of China (NSFC), Young Scientist Fund, 2019-2021
2. China Postdoctoral Science Foundation (Special), 2018-2020
3. China Postdoctoral Science Foundation (First class), 2017-2020
We organized a workshop "AppLens-Mining and Learning from Smartphones Apps for Users", in conjunction with the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing. More details can be seen at the AppLens2018 website.
Smartphone applications (abbr. apps) are ubiquitous in our daily life. Abundant apps provide useful services in almost every aspect of modern life. Easy to download and often free, apps can be fun and convenient for playing games, getting turn-by-turn directions, and accessing news, books, weather, and more. Apps on smartphones can be considered as the entry point to access everyday life services such as communication, shopping, navigation, and entertainment. Since a smartphone is linked to an individual user, apps in smartphones can sense users' behavior and activities.
Researchers can use smartphone apps to record users' personal history data, and use the data to analyze apps and understand users. In this workshop (AppLens 2018), we bring researchers together with an interest in understanding users from their use of smartphone apps (e.g., interests, personality, and life routines), discovering cultural and social phenomenon (e.g., social event detection) through analyzing app usage, recognizing app usage behaviors (e.g., app overuse detection), and studying smartphone apps (e.g., app categorization and app popularity prediction). We want researchers to share their experiences, success and frustrations on conducting research in such topics so as to capture a state-of-art on theories, models, and methodologies that cope with these challenges. This workshop will include paper sessions, invited talks, a panel session, and Best paper award.