Professional Career

I have always been deeply fascinated by mathematics, science and engineering. This early curiosity evolved into a more focused academic interest, and by the time I was introduced to control systems during my Bachelor's program, I realized I wanted to pursue the subject further. While my initial focus lay in addressing theoretical gaps in control and specifically developing methodologies for control of complex nonlinear, hybrid systems with formal correctness guarantees, I have recently been interested in more applied directions.

Current Research

Given the fast adoption of AI-based systems in real-world production systems (e.g. robotic systems), I believe that it is important to ensure that these systems are rendered to be safe, reliable, and robust, especially when these systems are consistently deployed in human-centered environments. Thus my research interests have involved into addressing these gaps and developing safe AI-based algorithms for robot learning. In particular, I look into problems in imitation learning and active learning and ensure that robots learn about themselves and the tasks they have to perform while navigating uncertainty in a safe manner. In addition, I try to understand exploratory behavior of motion planning algorithms. By leveraging insights from both theoretical foundations and empirical observations, my goal is to design learning-based control strategies that are both reliable and adaptive across different robotic applications.

Education and Experience

Prior to joining the Technical University of Munich, I was a postdoctoral researcher at the Institute for Systems Theory and Automatic Control, University of Stuttgart (August 2023 – December 2024), where I worked under the supervision of Prof. Dr. Frank Allgöwer. My research there primarily focused on safety verification and controller synthesis for switched and real-time control systems.

I earned my Ph.D. in September 2023 at the Software and Computational Systems Lab, Ludwig-Maximilians-Universität München, under the supervision of Prof. Dr. Majid Zamani. My doctoral thesis focused on the formal analysis of control systems via inductive techniques, with an emphasis on barrier function-based approaches for both verification and control synthesis. The work addressed challenges related to scalability, conservatism, and the design of correct-by-construction controllers for complex logic specifications. In early 2023, I was a visiting researcher at the University of Pennsylvania, where I collaborated with Prof. George Pappas on neural network-based methods for the safety verification of unknown or distributed systems.

I hold a Master of Technology in Systems and Control from the Indian Institute of Technology Roorkee, and a Bachelor of Technology from SRM Institute of Science and Technology, India. From 2018 to 2019, I was awarded the DAAD IIT-MSP scholarship, during which I completed my master’s thesis at the Hybrid Control Systems Lab, Technical University of Munich.