Welcome to Jiangtao's Homepage
Dr. Jiangtao Wang
Associate Professor
(Equivalent to Reader)
Center for Intelligent Healthcare
Coventry University, United Kingdom
Richard Crossman Building, Coventry, CV1 5RW, UK
Email: jiangtao.wang [AT] coventry[DOT] ac [DOT] uk
BRIEF BIOGRAPHY
Jiangtao is currently a Tenured Associate Professor with Center for Intelligent Healthcare, Coventry University. Before joining Coventry, he was a lecturer in Lancaster University and Peking University, respectively. His research interests include Pervasive and Mobile Computing, Crowdsensing, Digital Health, Internet of Things, and Smart Cities. Jiangtao has developed a strong international reputation for his research on Mobile Crowdsensing/Crowdsourcing systems, specifically within the domains of cost and quality joint optimization. Funded by major funding bodies in the UK and China such as EPSRC and NSFC, he is among the pioneering researchers who introduce the novel mechanisms to optimize sensing quality and cost in task allocation – a fundamental research issue and a maker or breaker in urban crowd sensing. Jiangtao has achieved a number of top conference and journal publications (e.g, UbiComp, PerCom, CSCW, WWW, AAAI, IJCAI, ICDM, IPSN, CIKM, IEEE Trans. on Mobile Computing, IEEE Trans. on Human-Machine Systems, IEEE Computer, and so forth). The results have been validated extensively using real-world data showing superior performance. These works are highly cited and recognized as evident by distinguished talk invitations from prestigious universities such as Nanyang Technological University (Singapore), National University of Singapore, and the University of New South Wales (Australia). As key author of these works, have been invited to organize and chair three IEEE international workshops on Crowdsensing. Jiangtao also serves for the journal editorial board such as Personal and Ubiquitous Computing, Frontiers in Sustainable Cities, etc, and PC members of multiple top international conferences (e.g., CSCW, AAAI, CIKM, etc.)
Jiangtao's group is always looking for talented potential PhD students worldwide. If you are interested in Jiangtao research, please do not hesitate to contact him. Also, the group strongly welcomes students and scientists from China to visit my group through the support of CSC.
News
[Sept 2021] PhD studentship for AI and healthcare research is available to apply.
[Aug 2021] Research Fellow of AI and Health Data Science (postdoc) position is available to apply.
[May 2021] Our research on AI and health crowdsensing has been funded by EPSRC.
[Feb 2021] Welcome two students to start their PhD in my group.
[Jan 2021] Our paper entitled 'Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones' has been accepted by UbiComp 2021 (to appear in IMWUT soon). (CORE A*, CCF A)
[Jan 2021] Two papers on crowdsourced health data analysis (one for Compressive Population Health, and one for Covid-19) have been accepted by WWW 2021 (CORE A*, CCF A).
[Dec 2020] Our paper entitled 'GRASP: Generic Framework for Health Status Representation Learning Based on Incorporating Knowledge from Similar Patients' got accepted by AAAI (CORE A*, CCF A).
[Oct 2020] I have been invited to serve as an Associate Chair for for CSCW 2021 Program Committee.
[Sept 2020] Our paper entitled 'Understanding and Predicting the Burst of Burnout via Social Media' got accepted by CSCW 2020 (CCF A).
[July 2020] Two PhD studentships in AI and healthcare become available. One is eligible for UK/EU/International students, and the other is for UK/EU students.
[May 2020] I have joined the Coventry University as an Associate Professor (Accepting new PhD students).
[March 2020] Good news about our paper "Will Online Digital Footprints Reveal Your Relationship Status? An Empirical Study of Social Applications for Sexual-Minority Men" accepted by UbiComp 2020 (CORE A*, CCF A), which will appear on the March issue of Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).
[Feb 2020] Our paper entitled "Participants Selection for From-Scratch Mobile Crowdsensing via Reinforcement Learning" has been accepted by IEEE International Conference on Pervasive Computing and Communications (PerCom 2020) , a highly competitive CORE A* conference.
[Jan 2020] Our paper entitled "HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing" has been accepted by IEEE Trans. on Mobile Computing (CORE A*, CCF A).
[Jan 2020] I am now organizing a special issue called "Active Crowdsensing" in the Springer Personal and Ubiquitous Computing (PUC) journal.
[Jan 2020] “VLD: Smartphone–assisted Vertical Location Detection for Vehicles in Urban Environments” gets accepted to 19th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN 2020), which is a highly competitive CORE A* conference (acceptance rate of 21%).
[Nov 2019] Our two papers about crowdsourced data analytics in individual health got accepted by AAAI 2020 (CORE A*, CCF A).
[Aug 2019] Our two papers about crowdsensing data analysis entited "CAMP: Co-Attention Memory Networks for Diagnosis Prediction in Healthcare" and "STAR: Spatio-Temporal Taxonomy-Aware Tag Recommendation for Citizen Complaints" got accepted by IEEE ICDM 2019 (CORE A*, CCF B) and ACM CIKM 2019 (CORE A, CCF B), respectively.
[June 2019] Our two papers for task assignment optimization of crowdsensing, which are entitled "Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors" and "Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing", have got accepted by IEEE Trans. on Mobile Computing (CORE A*, CCF A)
[May 2019 ] Our paper entitled "MLRDA: A Multi-Task Semi-Supervised Learning Framework for Drug-Drug Interaction Prediction" was accepted by IJCAI 2019 (CORE A*, CCF A).
[Feb 2019] I have left Peking University and joined the School of Computer and Communications in Lancaster University as an Assistant Professor.
Impact Highlights
Crowd sensing and computing: fundamentals and systems
I have been leading a variety of research projects on ubiquitous computing and mobile crowdsensing. Some highly cited and recognized works are listed as follows.
Multi-objective and multi-task crowdsensing task assignment frameworks. (PSAllocator framework presented in ACM CSCW 2017, PSTasker framework published in IEEE Trans on Mobile Computing, MTPS framework in IEEE IOT journal)
Approaches to ease cold-start problem in crowdsensing (Social-Network-Assisted worker recruitment approach published by IEEE Trans on Mobile Computing, and RL-Recruiter framework presented in PerCom 2020)
HyTasker framework: propose a hybrid crowdsensing model by combining both opportunistic and participatory sensing modes (published by IEEE Trans on Mobile Computing)
In collaboration with Peking University and the industiral partner Digital China, our crowdsensing/crowdsourcing techniques has been adopted on the smart city platforms across more than 10 cities.
Transforming UbiComp and AI into Digital Health
I collaborate closely with clinicians, industrial partners, and government policy makers to move ubiquitous computing and AI technology into the pathways of health care in terms of both individual and population level.
Leverage crowdsourced healthcare data and machine learning models to perform clinic prediction tasks for both physical and mental health (e.g., mortality prediction and intelligent diagnosis in AAAI 2020 and ICDM 2019, drug-drug interaction inference in IJCAI 2019, stress assessment in UIC 2019)
Use ubiquitous computing techniques to understand the behaviour of sex minority group health (UbiComp 2020/IMWUT);
Propose a novel cost-effective data collection framework called Compressive Population Health, which has been evaluated by real-world London datasets provided by the NHS.
Selected Publications
Journal Publications
[ACM Health] Dawei Chen, Jiangtao Wang, Wenjie Ruan, Qiang Ni, and Sumi Helal. 2021. Enabling Cost-Effective Population Health Monitoring By Exploiting Spatiotemporal Correlation: An Empirical Study. ACM Transaction on Computing for Healthcare 2, 2, Article 11 (January 2021), 19 pages.
[IMWUT/Ubicomp] Dai Shi, Dan Tao, Jiangtao Wang et al. Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones. UbiComp 2021 (ACM IMWUT) (CORE A*, CCF A)
[IMWUT/Ubicomp] Jiangtao Wang, Junyi Ma, Yasha Wang, Ning Wang, Leye Wang, Daqing Zhang, Feng Wang, and Qin Lv. 2020. Will Online Digital Footprints Reveal Your Relationship Status? An Empirical Study of Social Applications for Sexual-Minority Men. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 1, Article 29 (March 2020), 23 pages. (CORE A*, CCF A)
[TMC] Jiangtao Wang, Feng Wang, Yasha Wang, Leye Wang, Zhaopeng Qiu, Daqing Zhang, Bin Guo, and Qin Lv. "HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing." IEEE Transactions on Mobile Computing. Volume: 19, issue 3, March 1, 2020. (JCR Section 1, CORE A*, CCF A)
[TMC] Jiangtao Wang, Feng Wang, Yasha Wang, Daqing Zhang, Brian Lim, Leye Wang. Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors. IEEE Transactions on Mobile Computing, Volume: 18, Issue: 9, Sept 2019, pp. 1979 -1991 (JCR Section 1, CORE A*, CCF A)
[TMC] Jiangtao Wang, Feng Wang, Yasha Wang, Daqing Zhang, Leye Wang. Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing. IEEE Transactions on Mobile Computing, Volume: 18, Issue: 7, July 2019, pp.1661 - 1673. (JCR Section 1, CORE A*, CCF A)
[TMC] Jiangtao Wang, Yasha Wang, Daqing Zhang , ... & Zhaopeng Qiu. Multi-Task Allocation in Mobile Crowd Sensing with Individual Task Quality Assurance. IEEE Transactions on Mobile Computing, Volume: 17, Issue: 9, Sept, 2018. (JCR Section 1, CORE A*, CCF A)
[WCM] Jiangtao Wang, Yasha Wang, Daqing Zhang, Qin Lv, and Chao Chen. "Crowd-Powered Sensing and Actuation in Smart Cities: Current Issues and Future Directions." IEEE Wireless Communications 26, no. 2 (2019): 86-92. (Impact Factor: 11, JCR Section 1)
[CommMag] Jiangtao Wang, Yasha Wang, Daqing Zhang, Sumi Helal. Energy Saving Techniques in Mobile Crowd Sensing: Current State and Future Opportunities. IEEE Communications Magazine (2018). (Impact Factor: 10.3, JCR Section 1)
[IOTJ] Jiangtao Wang, Yasha Wang, Daqing Zhang, Haoyi Xiong, Leye Wang, Sumi Helal, Yuanduo He, Feng Wang: Fine-Grained Multi-Task Allocation for Participatory Sensing with a Shared Budget. IEEE Internet of Things Journal, vol. 3, no. 6, December 2016. (Impact Factor: 9.5, JCR Section 1)
[IOTJ] Jiangtao Wang, Leye Wang, Yasha Wang, Daqing Zhang, Linghe Kong. Task Allocation in Mobile Crowd Sensing: State of the Art and Future Opportunities. IEEE Internet of Things Journal, 2018. (Impact Factor: 9.5, JCR Section 1)
[WWWJ] Jiangtao Wang, Yasha Wang, Leye Wang, Yuanduo He. GP-selector: a generic participant selection framework for mobile crowdsourcing systems. Springer World Wide Web, 1-24(2017). (CORE A)
[COMPUTER] Jiangtao Wang, Yasha Wang, Qin Lv. Crowd-Assisted Machine Learning: Current Issues and Future Directions. IEEE COMPUTER, 2019. (flagship publication of IEEE computer society)
[THMS] Jiangtao Wang, Yasha Wang, Yafei Wang, "CAPFF: A Context-Aware Assistant for Paper Form Filling," in IEEE Transactions on Human-Machine Systems. pp 903-908.47.6, 2017. (CCF B)
Conference Publications
[WWW 2021] Yujie Feng, Jiangtao Wang, Yasha Wang and Sumi Helal. Completing Missing Prevalence Rates for Multiple Chronic Diseases by Jointly Leveraging Both Intra- and Inter-Disease Population Health Data Correlations. World Wide Web Conference (WWW 2021) (CORE A*, CCF A)
[WWW 2021] Liantao Ma, Xinyu Ma, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Chaohe Zhang, Wenjie Ruan, Yasha Wang, Wen Tang and Jiangtao Wang. DistCare: Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis. World Wide Web Conference (WWW 2021) (CORE A*, CCF A)
[AAAI 2021] Chaohe Zhang, Xin Gao, Liantao Ma, Yasha Wang, Jiangtao Wang, Wen Tang. GRASP: Generic Framework for Health Status Representation Learning Based on Incorporating Knowledge from Similar Patients. AAAI Conference on Artificial Intelligence (AAAI 2021) (CORE A*, CCF A)
[PerCom 2020] Yunfan Hu, Jiangtao Wang, Bo Wu, Sumi Helal, Participants Selection for From-Scratch Mobile Crowdsensing Via Reinforcement Learning. IEEE International Conference on Pervasive Computing and Communications (PerCom 2020). (CORE A*, CCF B)
[CSCW 2017] Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Yuanduo He, Liantao Ma: PSAllocator: Multi-Task Allocation for Participatory Sensing with Sensing Capability Constraints. ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1139-1151), 2017. (CORE A, CCF A)
[CSCW 2020] Jue Wu, Junyi Ma, Yasha Wang, and Jiangtao Wang. 2021. Understanding and Predicting the Burst of Burnout via Social Media. Proc. ACM Hum.-Comput. Interact. 4, CSCW3, Article 265 (December 2020), 27 pages. (CORE A, CCF A)
[AAAI 2020] Liantao Ma, Junyi Gao, Yasha Wang, Chaohe Zhang, Jiangtao Wang, Wenjie Ruan, Wen Tang, Xin Gao, Xinyu Ma. AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration. AAAI 2020. (CORE A*, CCF A)
[AAAI 2020] Liantao Ma, Chaohe Zhang, Yasha Wang, Wenjie Ruan, Jiangtao Wang, Wen Tang, Xinyu Ma, Xin Gao, Junyi Gao. ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context. AAAI 2020. (CORE A*, CCF A)
[IJCAI 2019] Xu Chu, Yang Lin, Yasha Wang, Leye Wang, Jiangtao Wang, Jingyue Gao. MLRDA: A Multi-Task Semi-Supervised Learning Framework for Drug-Drug Interaction Prediction. IJCAI 2019. (CORE A*, CCF A)
[ICDM 2019] Jingyue Gao, Xiting Wang, Yasha Wang, Zhao Yang, Junyi Gao, Jiangtao Wang, Wen Tang, and Xing Xie. CAMP: Co-Attention Memory Networks for Diagnosis Prediction in Healthcare. IEEE ICDM, 2019. (CORE A*, CCF B)
[IPSN 2020] Xiong W, Linghe K, … Jiangtao Wang, Chenren Xu. Smartphone–assisted Vertical Location Detection for Vehicles in Urban Environments. The International Conference on Information Processing in Sensor Networks (IPSN 2020). (CORE A*, CCF B)
To see the full publication, please visit my Google Scholar , DBLP, or Research Gate.
Teaching
[1] Human Computer Interaction. Spring Semester, Lancaster University, 2019, 2020.
[2] Advanced Programming. Spring Semester, Lancaster University, 2020.
My office hour is setting at 1:00 PM~2:00 PM of each Monday. If you would like to discuss any issues about the module learning or your research work, please make an appointment through my email.
I only provide recommendation letters for undergraduate or graduate students (1) with whom I have worked in research, or (2) who had significant face-to-face interaction with me in the class.
Grants and Awards
[1] Compressive Population Health: Cost-Effective Profiling of Prevalence for Multiple Non-Communicable Diseases via Health Data Science, Principal Investigator, EPSRC New Investigator Award. 2021-2023. (~ 280K £)
[2] Key Techniques in Data-Reuse-Oriented Quality-and-Cost Optimization for Mobile Crowd Sensing, Principal Investigator, National Natural Science Foundation of China, 2019-2022. (Equivalent to UKRI standard grant)
[3] Research on Key Techniques in Composite Mobile Crowd Sensing, Principal Investigator, National Natural Science Foundation of China, 2018. (Equivalent to UKRI new investigator grant)
[4] Key Techniques for Worker Selection in Mobile Crowd Sensing. Principal Investigator, Chinese Postdoctoral Science Foundation, 2016 (equivalent to UKRI postdoctoral fellowship)
[5] Outstanding graduate Award, Peking University, China, 2015
[6] Best paper award, Pervasive Computing Conference of CCF, 2018
Invited Talks
[8] Cost-Effective Crowdsensing and Its Opportunities in Population Health. Zhejiang University, Dec 2019, China.
[7] Cost-Effective Crowdsensing in Smart Cities. Hunan University, Dec 2019, China.
[6] Optimizing Task Allocation in Mobile Crowdsensing: New research perspectives, NTU-PKU workshop on AI meets IOT, Oct 2018, Singapore.
[5] Optimization of Mobile Crowdsensing. Young scholar forum in PCC 2018, Tianjin, China.
[4] Mobile Crowd Computing: From Urban Sensing to Population Health Management, CCF Forum on Smart Sensing and Urban Computing, Chongqing University, Chongqing, China, May. 2018
[3] Task Allocation in Mobile Crowd Sensing, CCF Forum on Smart Sensing and Urban Computing, Fuzhou University, Fuzhou, China, Oct. 2017.
[2] Mobile Crowd Sensing: Systems and Algorithms, National University of Singapore, Singapore, 2017
[1] People-Centric Sensing in the era of Big Data, Tencent PhD forum in Big Data, Beijing, China, July, 2014
Academic Services
Editorial Board:
[1] Guest Editor of Personal and Ubiquitous Computing, Special Issue on Active Crowd Sensing, 2020.
[2] Review Editor for Frontiers in Sustainable Cities, from 2020.
[3] Guest Editor of IEEE Transactions on Automation Science and Engineering, Special Issue on Artificial Intelligence for Autonomous Unmanned System Applications.
Conference/ Workshop Organization: Chair, CISC 19@Beijing. Chair, CISC 16@France. Publicity Chair, SPLC 16 @China. Workshop Chair, UIC19@UK. Poster Chair, IPCCC 19@UK.
Conference/ Workshop TPC Member: AAAI 21, ICDCS 21, CSCW 21, CIKM 20, EUSPN 16, HCDCSC 16, UIC 17, CrowdSense 17, UIC 19, GPC 2020.
Journal Reviewers: IEEE Trans. on Mobile Computing, IEEE Trans. on Sensor Networks, ACM IMWUT/UbiComp, ACM CSCW, IEEE Trans.on Human-Machine Systems, IEEE Pervasive Computing, International Journal of Human Computer Studies, ACM Trans. on Intelligent Systems, IEEE Communications Magazine, etc.
Student Supervision
Ke Xu, PhD student, Coventry University
Xu Wang, PhD Student, Coventry University
Yunfan Hu, Visiting Student, Peking University
Dawei Chen, Visiting Student, USTC
Collaborators
Prof. Daqing Zhang, Chair Professor, Peking University.
Prof. Sumi Helal, Chair Professor, Lancaster University.
Prof. Yasha Wang, Full Professor, Peking University.
Prof. Salil Kanhere, Full Professor, UNSW Sydney.
Prof. Qin Lv, Associate Professor, University of Colorado Boulder.
Dr. Brian Y. Lim, Assistant Professor, National University of Singapore.
Prof. Chao Chen, Full Professor, Chongqing University.
Dr. Leye Wang, Assistant Professor, Peking University.
Dr. Haoyi Xiong, Senior Research Scientist, Baidu Research
Dr. Wenjie Ruan, Lecturer, Lancaster University.