I received B.S. degree in Electrical Engineering from Shanghai Jiao Tong University, Shanghai, China, in 2016, and PhD in Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta, GA, USA in 2022. From Aug. 2022 to Jun. 2023, I was a Lillian Gilbreth Postdoc Fellow, in College of Engineering, Purdue University, West Lafayette, USA. My research interests include robotics, autonomy, and human robot collaboration. I aim to devise practical, computationally-efficient, and provably-correct algorithms that prepare robotic systems to be cognizant, taskable, and adaptive, and can collaborate with humans to co-exist in a complex, ever-changing and unknown environment.
P.S. Pronounciation of my first name is “mung-shway”. You can also call me Meng.
Prospective Students & Postdoc: I am actively looking for talented graduate/undergrad students & postdoc fellows interested in robotics, control & learning. Please visit our lab for more information!
PhD in Electrical and Computer Engineering, 2022
Georgia Institute of Technology, Atlanta, GA, US
BS in Electrical Engineering, 2016
Shanghai Jiao Tong University, Shanghai, China
[08/23] I have started as assistant professor at University of Notre Dame!
[04/23] I am selected to attend the CPS Rising Stars 2023 workshop at University of Virginia!
[04/23] I am co-organizing an invited session on Predictive Control and Planning Methods for Robotics Systems at ACC 2023!
To enable exploration of unstructured and dynamic open worlds, robotic systems have to collaborate with human operators to co-exist in a complex, ever-changing and unknown environment, and should feature behaviors that are cognizant, adaptive, and taskable: the robots need to be aware of their capabilities, identify the changes in environmental dynamics, learn from past experiences to improve system performance, and understand high-level instructions to plan multi-modal strategies that are dependent on the context in which the system is operating. Such features result in the following research questions:
Cognizant: how to represent the agent’s knowledge in an unstructured environment, without a pre-defined set of scene parameters?
Taskable: how to efficiently discover useful multi-modal distributed strategies for human-robot teams?
Adaptive: how to learn from past sensory data to build skills that can adapt over time to the particularities of the environment?
In this context, my research focuses on foundational advances in robotics and autonomy. I aim to devise practical, computationally-efficient, and provably-correct algorithms that prepare autonomous systems working synergistically with human operators to explore unknown, unstructured and dynamic environments. I will also seek to develop robotic platforms to validate the autonomy algorithms. The underlying hypothesis in my research is that the interactions between agents and the environment provide rich information: on one hand, the robot can leverage its actions and observed effects to train a high-fidelity prediction model (Thrust A). The learned model enables planning and control synergies of interaction policy for human-robot team (Thrust B). On the other hand, the robot can also use the historical perception data to directly learn the skills to achieve efficient sensorimotor understanding and planning (Thrust C).
Please visit ROAR (Robotics and Autonomy Research) lab website for more information.
Teaching at University at Notre Dame
EE 20221-Signal and Information Systems, Spring 2024
Teaching at Georgia Institute of Technology
ECE Vertically Integrated Projects (VIP), Course Instructor, Fall 2018 - Spring 2019
ECE 2026, Introduction to Signal Processing, Teaching Assistant, Fall 2016 - Summer 2017