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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

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Latest News

  • 08/2021: Congratulations on the graduations of Yandong Li and Muhammad A. Jamal! They will join Google.
  • 07/2021: Congratulations to Muhammad A. Jamal for his paper "A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning" accepted by ICCV 2021!
  • 07/2021: Congratulations to Bingbing Rao for his paper "SODA: A Semantics-Aware Optimization Framework for Data-Intensive Applications Using Hybrid Program Analysis" accepted by IEEE Cloud Computing 2021!
  • 03/2021: Congratulations to Yandong Li for his paper "Ranking Neural Checkpoints" accepted by CVPR 2021!
  • 03/2021: Our deep learning based COVID-19 model has been included in the CDC COVID-19 Forecasting Hub (UCF Today News).
  • 01/2021: Our project on Deep Learning based Motion Tracking was funded by ARMY!
  • 12/2020: Congratulations to Dongdong Wang for his paper "Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19" accepted by AAAI/IAAI 2021!
  • 12/2020: Congratulations to Yifan Ding for her paper "Analyzing Deep Neural Network's Transferability via Frechet Distance" accepted by WACV 2021!
  • 08/2020: Our project on COVID-19 was funded by UCF.
  • 08/2020: Our project (NSF-1952792, Co-PI, $1,225,000), was funded by NSF.

  • Short Bio

    Liqiang (Eric) Wang is an associate professor and the Graduate Coordinator 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 big data computing and analytics techniques in the following aspects: (1) improving accuracy and security of big data analysis models; (2) optimizing performance and scalability of big data processing and parallel computing systems, including multi-threading, HPC, Cloud and GPU platforms; (3) using program analysis and deep learning techniques to detect and prevent programming errors and execution anomaly in big data and/or parallel programs.

    He received NSF CAREER Award in 2011, Castagne Faculty Fellowship (2013-2015), and Mid-Career Refresh Award (UCF, 2020).

    Research Hightlight