jli's picture
Jiang
Li

Contact

Office: 
2038B Downing Hall
Phone: 
202-806-4861
Email: 
jli@howard.edu

Faculty Details

Rank: 
Associate Professor
Education: 

BS Computer Science, University of Science and Technology of China, Hefei, Anhui, China, 1995
MS Computer Science, University of Science and Technology of China, Beijing, Anhui, China, 1998
Ph.D. Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA 2003

Detailed Information

Publications: 

Jiazhen Zhou, Sankardas Roy, Jiang Li, Qingyang Hu, and Yi Qian, "Minimizing the Average Delay of Messages in Pigeon Networks", IEEE Transactions on Communications, 2013, Volume 61, Issue 8, pp.3349-3361.

Jiazhen Zhou, Jiang Li, Yi Qian, Sankardas Roy, and Kenneth Mitchell, “Quasi-Optimal Dual-Phase Scheduling for Pigeon Networks”, IEEE Transactions on Vehicular Technology, Vol.61. No.9, pp.4157-4169, November 2012.

Hui Guo, Jiang Li, Rose Qingyang Hu and Yi Qian, “HoPM: Multiple Pigeon-assisted Delivery in Delay Tolerant Networks”, Wireless Communications and Mobile Computing, 11: n/a. DOI: 10.1002/WCM.1133.

Jiazhen Zhou, Jiang Li and Kenneth Mitchell, “Adaptive Scheduling of Message Carrying in a Pigeon Network”, Journal of Ubiquitous Systems & Pervasive Networks Volume 1, No. 1 (2010) pp. 29-37. DOI: 10.5383/JUSPN.01.01.004.

News

Howard Team Participates in AMIE Design Challenge

Fri, April 10, 2020

Computer Science Junior Joseph Fletcher, Seniors Kendal Hall and Tyler Ramsey, and Ph.D. student Abdulhamid Adebayo participated in the Advancing Minorities’ Interest in Engineering (AMIE) Design Challenge at the Black Engineer of the Year Awards (BEYA) 2020 STEM Conference. Read More >>

Associate Professor Danda Rawat to Lead Cybersecurity Partnership on $3M NNSA Grant

Tue, March 3, 2020

Howard University is the recipient of a three-year, $3 million grant from the Department of Energy’s National Nuclear Security Administration, alongside two partnering minority-serving institutions, for The Partnership for Proactive Cybersecurity Training, a cybersecurity research project based on human biological system-enabled machine learning models. Read More >>

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