|Jie Lu, Assistant Professor|
Tel: (021) 20685398
Distributed optimization algorithms
Large-scale optimization methods and applications
Multi-agent coordination and decision making
UAV navigation and control algorithms
Jie Lu is currently the Deputy director of Curriculum and Teaching Committee.
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.
X. Wu and J. Lu, Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks, IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4629–4636, 2019.
X. Wu and J. Lu, “Improved Convergence Rates of P-EXTRA for Non-smooth Distributed Optimization,
Proc. IEEE International Conference on Control and Automation, pp. 55–60, Edinburgh, Scotland, 2019. (Best Student Paper Finalist)
X. Wu, K. C. Sou, and J. Lu, Fenchel Dual Gradient Methods Enabling a Smoothing Technique for Nonsmooth Distributed Convex Optimization, Proc. IEEE Conference on Decision and Control, pp. 1757–1762, Miami, FL, 2018.
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.
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.
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.