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 focuses on designing practical algorithms to solve the learning and control problems in network systems, with the application of optimization, machine learning and inference methods.
My current expected graduation time is December 2022. I am actively looking for full-time jobs related to cloud networks, recommendation systems, operation research, etc.
Modern network systems often offer limited access and suffer from adversarial attacks. In this project, we focus on networks with unobservable malicious nodes, where the network dynamics, such as external arrivals and control actions of malicious nodes can be adversarial.
Modern networks are complex and may include components that cannot be fully controlled or observed. We propose a control algorithm for such overlay-underlay networks where the network controller can only observe and operate on overlay nodes, and the underlay nodes are neither observable nor controllable.
By applying model-based reinforcement learning and Lyapunov analysis, we design 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.
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.