Professor

Phone: 027-87543630

Email: qsliu@hust.edu.cn

Academic Areas: Automated planning, scheduling and optimization, robot task planning, emergency response task planning

Research Interests:

Qingshan Liu is a professor of the School of Automation. He serves as an Associate Editor of the IEEE Transactions on Cybernetics and is a member of the Editorial Board of Neural Networks. His research is focused primarily on computational intelligence, multi-agent optimization, pattern recognition and intelligent systems.

Academic Degrees

Aug.2005 - July 2008, PhD in Computational Intelligence, Mechanical and Automation Engineering,
The Chinese University of Hong Kong
Sept. 2002 - Mar. 2005, MS in Applied Mathematics, Mathematics, Southeast University
Sept. 1997 - June 2001, BS in Mathematics, Mathematics, Anhui Normal University

Professional Experience

May 2014 - present: Professor, School of Automation, Huazhong University of Science and Technology.
Sept. 2008 – Apr. 2014: Associate Professor, School of Automation, Southeast University.
Mar. 2014 - June 2014: Research Associate, Mechanical and Automation Engineering, The Chinese University of Hong Kong.
May 2013 - July 2013: Research Associate, Mathematics and Science, Texas A&M University at Qatar.
June 2011 - Sept. 2011: Postdoctoral Fellow, Systems Engineering and Engineering Management, City University of Hong Kong.
Feb. 2010 - Aug. 2010: Postdoctoral Fellow, Mechanical and Automation Engineering, The Chinese University of Hong Kong.
 

Selected Publications

1. Q. Liu and J. Wang, “L1-minimization algorithms for sparse signal reconstruction based on a projection neural network,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 698–707, Mar. 2016.

2. Q. Liu and J. Wang, “A second-order multi-agent network for bound-constrained distributed optimization,” IEEE Transactions on Automatic Control, vol. 60, no. 12, pp. 3310–3315, Dec. 2015.

3. Q. Liu, T. Huang, and J. Wang, “One-layer continuous- and discrete-time projection neural networks for solving variational inequalities and related optimization problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 7, pp. 1308–1318, July 2014.

4. Q. Liu, C. Dang, and T. Huang, “A one-layer recurrent neural network for real-time portfolio optimization with probability criterion,” IEEE Transactions on Cybernetics, vol. 43, no. 1, pp. 14–23, Feb. 2013.

5. Q. Liu, Z. Guo, and J. Wang, “A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization,” Neural Networks, vol. 26, pp. 99–109, Feb. 2012.

6. Q. Liu and J. Wang, “A one-layer recurrent neural network for constrained nonsmooth optimization,” IEEE Transactions on Systems, Man and Cybernetics-B, vol. 41, no. 5, pp. 1323–1333, Oct. 2011.

7. Q. Liu, C. Dang, and J. Cao, “A novel recurrent neural network with one neuron and finite-time convergence for k-winners-take-all operation,” IEEE Transactions on Neural Networks, vol. 21, no. 7, pp. 1140–1148, July 2010.

8. Q. Liu, J. Cao, and G. Chen, “A novel recurrent neural network with finite-time convergence for linear programming,” Neural Computation, vol. 22, no. 11, pp. 2962–2978, 2010.

9. Q. Liu and J. Wang, “A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming,” IEEE Transactions on Neural Networks, vol. 19, no. 4, pp. 558–570, Apr. 2008.

10. Q. Liu and J. Wang, “A one-layer recurrent neural network with a discontinuous activation function for linear programming,” Neural Computation, vol. 20, no. 5, pp. 1366–1383, 2008.

Awards and Honors

1. Young Researcher Award, Asia Pacific Neural Network Assembly (APNNA), 2012.

2. Outstanding Paper Award, IEEE Transactions on Neural Networks, 2011.

3. Natural Science Award (First Class), Ministry of Education in China, 2011.

Courses Taught

For Undergraduates:
Operations Research

For Graduates:
Nonlinear Programming, Complex Networks and Control

Project

Current Research Projects:
l. Jan. 2015 - Dec. 2018: National Natural Science Foundation of China (Grant NO. 61473333): “Sparse Representation Algorithms Based on Neural Networks and Swarm Intelligence”.

Finished Research Projects:
1. Jan. 2012 - Dec. 2014: National Natural Science Foundation of China (Grant NO. 61105060): “Finite-time Convergence Based Optimal Design, Analysis and Applications of Recurrent Neural Networks”.

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