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Peng Sun

Assistant Professor of Data Science at Duke Kunshan University
DKU Faculty

Overview


Dr. Sun has a Bachelor of Engineering in Electrical Engineering and Automation for Soochow University, an M.Sc. in Electrical and Computer Engineering (Supervisor: Hassan Naser) from Lakehead University, Canada, and a Ph.D. in Electrical and Computer Engineering (Supervisor: Nancy Samaan) from the University of Ottawa, Canada, where he also served as a post-doctorate researcher from 2016 to 2020. Before joining Duke Kunshan, he was a research associate of PARADISE Research Laboratory at the University of Ottawa, which is led by Professor Azzedine Boukerche.

He is a senior member of IEEE (SMIEEE) and the author of more than 100 peer-reviewed journal and conference papers. He worked as the research associate for many research projects supported by the National Defense of Canada, Communications Research Centre Canada, and Natural Sciences and Engineering Research Council of Canada. Currently, he is an active reviewer for many prestigious journals, e.g., IEEE Transactions on Wireless Communications, Elsevier Ad Hoc Networks, ACM Computing Survey, etc. He also served as Technical Program Committee Co-chair/member in many top-tier conferences, e.g., ACM DIVANet'18~24, IEEE ISCC'19, ACM MSWiM'17~24, ACM/IEEE DS-RT’20 and IEEE ICC'20~24, etc. He services as an Editor for ACM International Conference Proceedings Series (ICPS) Editorial Board. He is the recipient of Best Paper Award at IEEE GLOBECOM in 2019. His work, entitled “A Novel Hierarchical Two-Tier Node Deployment Strategy for Sustainable Wireless Sensor Networks”,  was published in the prestigious IEEE Transactions on Sustainable Computing, and it was selected as one of the spotlight articles published in all IEEE Transactions as highlighted in the IEEE Computer V. 52(3), pp. 4-5, March 2019.

Current Appointments & Affiliations


Assistant Professor of Data Science at Duke Kunshan University · 2020 - Present DKU Faculty

Recent Publications


Memory-enhanced spatial-temporal encoding framework for industrial anomaly detection system

Journal Article Expert Systems with Applications · September 15, 2024 The development of modern manufacturing has raised greater demands on the accuracy, response speed, and operating cost of industrial accident warnings. Compared to conventional contact sensors, surveillance cameras can contactlessly capture spatial–tempora ... Full text Cite

The evolution of detection systems and their application for intelligent transportation systems: From solo to symphony

Journal Article Computer Communications · September 1, 2024 The emergence of autonomous driving technologies has been significantly influenced by advancements in perception systems. Traditional single-agent detection models, while effective in certain scenarios, exhibit limitations in complex environments, necessit ... Full text Cite

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

Journal Article ACM Computing Surveys · July 31, 2024 Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupe ... Full text Cite
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Education, Training & Certifications


Ottawa University · 2016 Ph.D.

External Links


Google scholar