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Liqiang Wang

Associate Professor, Graduate Coordinator
Department of Computer Science
University of Central Florida
Orlando, FL 32816
HEC 437E

Tel: (407) 823-3187
Fax: (407) 823-5835
E-mail: lwang AT cs.ucf.edu


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Latest News


  • 11/2018: Three papers from our group have been accepted by AAAI 2019 with an acceptance rate of 16.2%!

  • 10/2018: Our paper "A Reinforcement Learning Based Resource Management Approach for Time-critical Workloads in Distributed Computing Environment" (Z. Liu, H. Zhang, B. Rao, L. Wang) was accepted by IEEE Bigdata 2018 (with an acceptance rate of 18.9%)!

  • 08/2018: Our paper "Anomaly Detection from Big Data System Logs Using Convolutional Neural Network" (S. Lu, X. Wei, Y. Li, L. Wang) was awarded a Best Paper Award!

  • 08/2018: A grant from NSF title "Towards End-to-End Resource Optimization for Time-Critical Comput-ing Using Reinforcement Learning and Program Analysis" has been awarded!

  • 07/2018: Our paper "How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization" (Y. Li, L. Wang, T. Yang, B. Gong) has been accepted to ECCV 2018!


  • Short Bio

    Liqiang (Eric) Wang is an associate professor in the Department of Computer Science at the University of Central Florida. He is the director of Big Data Computing Lab. He was a faculty member (2006-2015) in the Department of Computer Science at the University of Wyoming . He received Ph.D. in Computer Science from Stony Brook University in 2006. He was a visiting Research Scientist in IBM T.J. Watson Research Center during 2012-2013.

    His research focuses on integrating deep learning, parallel computing, and program analysis, which includes the following aspects: (1) improving the robustness, accuracy, speed, and scalability of deep learning; (2) optimizing performance, scalability, resilience, and resource management of big data processing, especially on Cloud, GPU, and multicore platforms; (3) using hybrid program analysis to detect and avoid programming errors, execution anomaly, as well as performance defects in large-scale parallel computing systems.

    He received NSF CAREER Award in 2011 and Castagne Faculty Fellowship (2013-2015).

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