Jie Lu, Assistant Professor

Jie Lu, Assistant Professor

Tel:  (021) 20685398
Email: lujie@@shanghaitech.edu.cn
Office: Room 1D-201B, SIST Building
Major: EES
Website: http://faculty.sist.shanghaitech.edu.cn/faculty/lujie/
Education: Ph.D., The University of Oklahoma, USA
Jie Lu Research Group Recruitment (Click Here)

RESEARCH INTERESTS

  • Distributed optimization algorithms

  • Large-scale optimization methods and applications

  • Multi-agent coordination and decision making

  • UAV navigation and control algorithms


BIOGRAPHY

Jie Lu received the B.S. degree in Information Engineering from Shanghai Jiao Tong University, China in 2007 and the Ph.D. degree in Electrical and Computer Engineering from the University of Oklahoma, USA in 2011. From 2012-2015 she was a postdoctoral researcher with the Automatic Control Lab, ACCESS Linnaeus Centre at KTH Royal Institute of Technology, Sweden and with the Department of Signals and Systems at Chalmers University of Technology, Sweden. Since 2015 she has been a tenure-track assistant professor, PI in the School of Information Science and Technology at ShanghaiTech University. Her research focuses on developing efficient algorithms, computational models, and mathematical tools for large-scale optimization and control that enable intelligent decision making.


SELECTED PUBLICATIONS

  1. X. Wu and J. Lu, “Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks”, accepted to IEEE Transactions on Automatic Control, 2019.

  2. J. Lu and C. Y. Tang, “A Distributed Algorithm for Solving Positive Definite Linear Equations over Networks with Membership Dynamics”, IEEE Transactions on Control of Network Systems, vol. 5, no. 1, pp. 215–227, 2018.

  3. T. Yang, J. Lu, D. Wu, J. Wu, G. Shi, Z. Meng, and K. H. Johansson, “A Distributed Algorithm for Economic Dispatch over Time-Varying Directed Networks with Delays”, IEEE Transactions on Industrial Electronics, vol. 64, no. 6, pp. 5095-5106, 2017.

  4. J. Lu and M. Johansson, “Convergence Analysis of Approximate Primal Solutions in Dual First-Order Methods”, SIAM Journal on Optimization, vol. 26, no. 4, pp. 2430-2467, 2016.

  5. J. Lu and C. Y. Tang, “Zero-Gradient-Sum Algorithms for Distributed Convex Optimization: The Continuous-Time Case”, IEEE Transactions on Automatic Control, vol. 57, no. 9, pp. 2348–2354, 2012.