Kunal Garg
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551 E Tyler Mall ERC 475 Tempe, AZ 85281
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Mail code: 6106Campus: 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
Distributed multi-agent motion planning and control, Provably safe control design using machine learning-based certificates, Foundation models in complex robotic tasks, Failure prediction from limited demonstrations, Human-robot interaction.
I am looking for dedicated, hardworking, and highly self-motivated graduate students to join my research group. Please check my personal website for openings in my group: https://sites.google.com/asu.edu/kunalgarg/bio?authuser=1
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
2026 Spring
| Course Number | Course Title |
|---|---|
| CSE 599 | Thesis |
| MAE 792 | Research |
| MAE 493 | Honors Thesis |
| AEE 313 | Aircraft Dynamics and Control |
| MAE 593 | Applied Project |
| EEE 593 | Applied Project |
| CSE 593 | Applied Project |
| RAS 593 | Applied Project |
2025 Fall
| Course Number | Course Title |
|---|---|
| CSE 599 | Thesis |
| MAE 599 | Thesis |
| MAE 792 | Research |
| MAE 492 | Honors Directed Study |
| MAE 593 | Applied Project |
| MAE 598 | Special Topics |
| MAE 494 | Special Topics |
2025 Spring
| Course Number | Course Title |
|---|---|
| AEE 313 | Aircraft Dynamics and Control |
| AEE 313 | Aircraft Dynamics and Control |