Bai Liu (刘柏)

bailiu [at] mit.edu   [CV]   [LinkedIn]   [GitHub]  

I am currently a Ph.D. candidate in Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology, advised by Prof. Eytan Modiano.

My research interests lie in learning and control problems in networked systems (data networks, logistic networks etc.), with application of reinforcement learning, stochastic optimization, inference methods.

Publications

Paper

  • Bai Liu, Qiaomin Xie, and Eytan Modiano. RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems. ACM Transactions on Modeling and Performance Evaluation of Computing Systems. Submitted.   [arXiv]
  • Bai Liu, Qiaomin Xie, and Eytan Modiano. Reinforcement Learning for Optimal Control of Queueing Systems. 57th Annual Allerton Conference on Communication, Control, and Computing, September 2019.
  • Bai Liu, Ke Han, and Jianming Hu. Global Optimization Framework for Real-time Route Guidance via Variable Message Sign, May 2016.   [arXiv]

Patent

  • Jianming Hu, Xin Pei, Bai Liu, et al. An Information Distribution Method of Variable Message Sign Based on Prediction Method. Chinese Invention Patent. Publication Number: CN105303856A. Publication Date: 2016.02.03.

Software Copyright

  • Intelligent Networking Transportation Guidance System Platform [INGSP] V1.0. Computer Software Copyright. Registration Number: 2016SR252223. Date: 2016.06.01.

Selected Research Projects

Reinforcement Learning in Network Control   [arXiv]

Bai Liu, Qiaomin Xie, Eytan Modiano

By applying model-based reinforcement learning and Lyapunov analysis, we designe an algorithm for queueing networks with unbounded state spaces. We prove that under our algorithm, the average queue backlog can get arbitrarily close to the optimal result.

Subnetwork Selection of Gaussian Relay Network   [Poster]

Bai Liu, Xiugang Wu, Ayfer Özgür

In wireless communication, some relays can be turned off while the optimal capacity still holds. We can apply traversing search approach to find the trimmed network, but its time cost might be unbearable. To solve the problem, we thoroughly study and rigorously prove properties of layered Gaussian relay network. We then develop and implement an algorithm that can find optimal global subnetwork exponentially faster.

Dynamic Transportation Network Modeling   [arXiv]

Bai Liu, Ke Han

Aiming at improving VMS (variable message sign) display strategy, we formulate transportation network model with feedback scheme. We then design optimization algorithm with linear decision rule and heuristic optimization approach. A simulation case study is conducted on a real-world test network in China, which shows the advantage of the proposed adaptive VMS display strategy.