Kunal Garg
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551 E Tyler Mall ERC 475 Tempe, AZ 85281
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Campus: Tempe
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Kunal Garg is an assistant professor in the mechanical and aerospace engineering program at the School for Engineering of Matter, Transport and Energy at ASU. He received his master's degree in engineering and doctoral degree in aerospace engineering from the University of Michigan in 2019 and 2021. Before joining ASU, he was a postdoctoral associate in the Laboratory for Information & Decision Systems (LIDS) and the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology. Garg was a 2022 DAAD AInet Fellow for the Postdoctoral Networking Tour in Artificial Intelligence in the AI and Robotics domain. He also received the Professor Pierre T. Kabamba Award for Excellence in Control Systems, and the Richard and Eleanor Towner Prize for Distinguished Academic Achievement for his PhD research work in 2021. Garg's research interests include robust control synthesis for multi-agent coordination using machine learning methods, finite- and fixed-time control synthesis for spatiotemporal specifications, and continuous-time optimization.
PhD. Aerospace Engineering, University of Michigan, Ann Arbor, 2021
MS. Aerospace Engineering, University of Michigan, Ann Arbor, 2019
BTech. Aerospace Engineering, Indian Institute of Technology, Bombay, 2016
Robust control synthesis for multi-agent coordination using machine learning methods, finite- and fixed-time control synthesis for spatiotemporal specifications, and continuous-time optimization
I am looking for dedicated, hardworking, and highly self-motivated graduate (MS and PhD) students to join my research group starting in Fall '25. If you are interested in the broader topics of robotics, machine learning, control theory, and optimization, contact me via email. More specific topics that I will be interested in working on are:
- Multi-agent robotics
- Team robotics with aerial and ground robots
- Learning-based methods for safe robotics
- Optimization-based methods for safe robotic control
- Reinforcement learning for multi-objective robotic applications
- Large-Language Models (LLMs) in robotic applications
The pre-requisites and background I am looking for are as follows:
- Strong interests in control, autonomy, and robotics
- Strong mathematical background in Linear Systems and Linear Algebra, ODEs, Probability, and Calculus
- Programming efficiency: Python (C/C++ is optional, but will be a good add-on as we will plan to conduct hardware experiments)
- Hands-on experience with ML packages (PyTorch or JAX)
- Hands-on experience with ROS or ROS2
Email me your CV along with a one or two-page document on why you are interested in working with me, what topic(s) you are interested in working on, what motivates you to work on those, which of the pre-requisites listed above you meet, and what's your plan for meeting the rest of the requirements.
Courses
2025 Spring
| Course Number | Course Title |
|---|---|
| AEE 313 | Aircraft Dynamics and Control |
| AEE 313 | Aircraft Dynamics and Control |