About Me
I'm currently a Ph.D student in Nanyang Technological University (NTU), Singapore. I'm an graduate researcher in SCALE lab, supervised by Prof. Tang Xueyan, currently working on Algorithms in Multi-Agent Systems.
Despite my current research topic, my research interest spread widely in operating systems, distributed systems, network security and algorithms. "Practice is the sole criterion of truth", I would like to build effective systems and platforms for researchers, developers and the community. I'm ready to contribute to any small improvement of the infrastructure.
Besides a researcher, I started my life as a programmer and developer quite early. I learned C/C++ and attended NOIP (National Olympiad in Informatics in Provinces) in China and it becomes my most confident language. I developed my interest in other fields in computer science when I tried to find out what exactly happened behind these code I wrote. I was deeply attracted by the open source community and became an active developer in many projects as well.
Learning is the passion of my life, learning new knowledge in different fields, learning new programming languages, frameworks and features, and even learning how to play games all inspire me. My passion in learning can be witnessed by my proficiency in various OSs and programming languages. I'm always curious about the latest language features of C++ and Python; I helped the development of many Java projects, from Swagger API to Minecraft Mods; I developed and maintained web services across many types of Linux distributions during my undergraduate study; I can process data science (my undergraduate minor) tasks in scientific Python, MATLAB, Julia, and Mathematica; I'm also broadening my language family by learning some new ones such as Go and Rust.
As a fullstack developer skilled in Python, Javascript (React), SQL, CSS and many others, I built the theme of this website with Gatsby. The theme is named "gatsby-theme-academic", which is specially designed for academic usage by researchers and students. It is still under development and will be open-sourced soon, please check this post for details.
Education
Interests
Awards & Scholarships
NTU Research Scholarship
Meritorious Winner in Mathematical Contest In Modeling
Selected Research
Multi-Agent Path Execution with Uncertainty
In real-world multi-agent applications, unexpected conditions can break the assumptions made in path planning and degrade the effectiveness of path execution. This paper studies robust and effective execution of multi-agent path plans under uncertainty. To guarantee conflict-freeness and deadlock-freeness, we define a feasibility problem to check whether the remaining portion of a path plan can be successfully executed. We prove that the problem is NP-complete and propose a feasibility test algo ...
Multi-Agent Pickup and Delivery with Individual Deadlines
We study the multi-agent pickup and delivery problem with task deadlines, where a team of agents execute a batch of tasks with individual deadlines to maximize the number of tasks completed by their deadlines. Existing approaches to multi-agent pickup and delivery typically address task assignment and path planning separately. We take an integrated approach that assigns and plans one task at a time taking into account the agent states resulting from all the previous task assignments and path pla ...
Playground: A Safe Building Operating
Building operating systems are an emerging class of system software that provides services to applications running on commercial buildings. The current state-of-the-art requires applications to be trusted and carefully monitored due to a lack of authorization, access control, and execution isolation mechanisms in existing building operating systems. Proposed solutions do not adequately handle the complexity and scale of modern buildings, therefore impeding the adoption of applications that can e ...
Robust Continuous-Time Multi-Agent Path Execution
Most prior work on multi-agent path finding and execution has assumed that time is discretized into timesteps and the agents move in grid maps with uniform action durations. However, in real-world multi-agent applications, agents of different shapes can move in arbitrary directions, and unforeseen failures and anomalies can cause unexpected delays of the agents. These delays are difficult to model and predict, and they can break the assumptions made in path planning and degrade the effectiveness ...