Feng WANG (王峰)

 

Associate Professor, PhD, IEEE Senior Member

 

Department of Computer and Information Science

The University of Mississippi

University, MS, USA    38677

 

Office: 220 Weir Hall

Tel: +1 662-915-5359    Fax: +1 662-915-5623

Email: fwang@cs.olemiss.edu

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

**News**

 

    I am looking for highly motivated students who would like to work in computer networking area. Interested applicants please contact me with your CV and transcript.

 

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Brief Biography  (IEEE Style)  (Full CV)

 

    I am an Associate Professor in the Department of Computer and Information Science at the University of Mississippi, University, MS, USA. I received my Bachelor of Engineering and Master of Engineering (both in Computer Science) from Tsinghua University, Beijing, China, in 2002 and 2005, respectively. I received my PhD (in Computer Science) from Simon Fraser University, Burnaby (Metro-Vancouver), British Columbia, Canada, in 2012. I am a Senior Member of IEEE. I am a recipient of IEEE ICME Quality Reviewer Award (2011), ACM BuildSys Best Paper Award (2018), and IEEE INFOCOM Distinguished TPC Member Award (2020). I served as Program Vice Chair in International Conference on Internet of Vehicles (IOV) 2014, as TPC co-chair in IEEE ICCC 2014 (Symposium on Wireless Networking and Multimedia), and as TPC co-chair in IEEE CloudCom 2017 (IoT and Mobile on Cloud track). I also serve as Technical Committee Member in Elsevier Computer Communications, and TPC member in various international conferences including IEEE INFOCOM, IEEE/ACM IWQoS, ACM Multimedia, IEEE ICC, IEEE GLOBECOM.

 

 

 

 

 

 

 

 

 

Research Interests

 

    My research interests are mainly in computer networking area, in particular, wireless sensor/mesh networks, peer-to-peer networks, socialized content sharing, cyber-physical systems, internet of things, cloud/edge computing, crowdsourcing, big data, and applications of deep learning in computer networking.

 

 

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