system science department
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qingshan liu is currently a professor at the school of mathematics, southeast university, nanjing, china.
working experience
- 2018/04-now, school of mathematics, southeast university, professor
- 2014/04-2018/04, school of automation, huazhong university of science and technology, professor
- 2008/09-2014/04, school of automation, southeast university, associate professor
- 2016/05-2016/06, department of computer science, city university of hong kong, research fellow
- 2013/05-2013/07, mathematics and science, texas a&m university at qatar, research associate
- 2010/02-2010/08, mechanical and automation engineering, the chinese university of hong kong, postdoctoral fellow
- 2009/08-2009/11, manufacturing engineering and engineering management, city university of hong kong, senior research associate
education background
- 2005/08-2008/07, mechanical and automation engineering,
the chinese university of hong kong, ph.d
- 2002/09-2005/03, mathematics, southeast university, m.s.
- 1997/09-2001/07, mathematics, anhui normal university, b.s.
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research interests
neural networks; computational intelligence; distributed optimization; multi-agent systems; complex networks; machine learning; multi-robot systems
selected journal publications
q. liu, x. le, and k. li, “a distributed optimization algorithm based on multiagent network for economic dispatch with region partitioning,” ieee transactions on cybernetics, vol. 51, no. 5, pp. 2466-2475, may 2021. k. li, q. liu, s. yang, j. cao, and g. lu, “cooperative optimization of dual multiagent system for optimal resource allocation,” ieee transactions on systems, man, and cybernetics: systems, vol. 50, no. 11, pp. 4676-4687, nov. 2020. s. yang, j. wang, and q. liu, “cooperative-competitive multiagent systems for distributed minimax optimization subject to bounded constraints,” ieee transactions on automatic control, vol. 64, no. 4, pp. 1358–1372, apr. 2019. b. xu, q. liu, and t. huang, “a discrete-time projection neural network for sparse signal reconstruction with application to face recognition,” ieee transactions on neural networks and learning systems, vol. 30, no. 1, pp. 151–162, jan. 2019. q. liu, s. yang, and y. hong, “constrained consensus algorithms with fixed step size for distributed convex optimization over multiagent networks,” ieee transactions on automatic control, vol. 62, no. 8, pp. 4259–4265, aug. 2017. q. liu, s. yang, and j. wang, “a collective neurodynamic approach to distributed constrained optimization,” ieee transactions on neural networks and learning systems, vol. 28, no. 8, pp. 1747–1758, aug. 2017. 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. 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. q. liu and j. wang, “a projection neural network for constrained quadratic minimax optimization,” ieee transactions on neural networks and learning systems, vol. 26, no. 11, pp. 2891–2900, nov. 2015. 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. q. liu and j. wang, “a one-layer projection neural network for nonsmooth optimization subject to linear equalities and bound constraints,” ieee transactions on neural networks and learning systems, vol. 24, no. 5, pp. 812–824, may 2013. 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.
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honors and awards 2019, wuwenjun artificial intelligence science and technology award (natural science award, second class), chinese association for artificial intelligence 2018, “333 engineering”, jiangsu province
2015, natural science award (second class), hubei province 2015, outstanding reviewer award, ieee transactions on cybernetics 2012, new century excellent talents in university, ministry of education of china 2012, young researcher award, asia pacific neural network assembly (apnna) 2011, natural science award (first class), ministry of education of china 2011, outstanding paper award, ieee transactions on neural networks 2009, young science star, science news and elsevier 2006, excellent master thesis award, jiangsu province
research grants national natural science foundation of china (grant no. 61876036), 'distributed optimization algorithms based on model decomposition and collective neurodynamic networks', 2019/01-2022/12, lead pi national natural science foundation of china (grant no. 61473333), 'sparse representation algorithms based on neural networks and swarm intelligence', 2015/01-2018/12, lead pi new century excellent talents in university of china (grant no. ncet-12-0114), 'computational intelligence', 2013/01-2015/12, lead pi national natural science foundation of china (grant no. 61105060), 'finite-time convergence based optimal design, analysis and applications of recurrent neural networks', 2012/01-2014/12, lead pi natural science foundation of jiangsu province of china (grant no. bk2011594), 'optimization neural networks modelling and finite-time convergence analysis', 2012/01-2014/12, lead pi doctoral program of ministry of education (grant no. 20090092120026), 'coupling optimization neural network design based on multi-objective programming conditions', 2010/01-2012/12, lead pi
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associate editor 2018-, ieee transactions on neural networks and learning systems 2015-, ieee transactions on cybernetics 2012-, neural networks 2020-, neural processing letters
guest editor
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