I am a Postdoctoral Scholar in the Burdick Group at Caltech doing research with Prof. Joel Burdick and Prof. Aaron Ames. I earned my Ph.D. in Aerospace Engineering at Texas A&M University under the supervision of Prof. Manoranjan Majji, where I focused on developing provably safe control algorithms for autonomous systems. I first became interested in control theory during my undergrad at Cornell University with Prof. Silvia Ferrari. During my graduate studies I worked closely with Dr. Kerianne Hobbs in the safe autonomy team at AFRL. At AFRL, I worked on developing novel safety-critical control methods to allow safe, learning-enabled control on USAF and USSF aerospace systems.
I am interested in problems which merge control theory, optimization, and estimation to enhance the reliability of autonomous systems in complex environments.
Safety guarantees with input constraints and state estimation error using backup CBFs.
Particle-based probabilistic CBF framework that overcomes conservatism by exploiting the sub-Gaussian structure of the barrier function increment.
Safe set expansion using multiple backup sets and controllers to reduce conservatism under input bounds.
Generalizing the backup CBF method by decoupling the set expanding controller from the verified backup controller, and allowing for online adaptation.
Novel class of CBFs for online controlled invariance of uncertain systems with input constraints, whilst reducing conservatism via uncertainty estimators.
Novel state estimation formulation for improving filter stability and statistical consistency for nonlinear systems.
Novel class of CBFs for online controlled invariance of systems with input constraints and unmodelled dynamics disturbances.
Closed-loop GNC framework for spacecraft servicing module deployment, optimizing low-thrust trajectories while addressing state constraints and impact uncertainty.
RL-based on-orbit spacecraft inspection with safety ensured via control barrier functions.
Motion planning framework for directional sensors ensuring target visibility, obstacle avoidance, and efficiency in cluttered environments.
Closed-form controller via optimal interpolation to ensure safety for nonlinear systems with bounded inputs.
Control barrier functions and sensor fusion for safe closed-loop GNC in multi-agent satellite servicing missions.
RTA-enhanced RL training for 6-DOF spacecraft inspection, enforcing safety via control barrier functions.
Collision mitigation for low-thrust spacecraft in a quasi-periodic orbit using high-order control barrier functions.
Safety-critical spacecraft control in the presence of faults and rapidly variable dynamics using sequential estimation and control barrier functions.
RL-based control for on-orbit spacecraft inspection, optimizing illumination and evaluating performance with statistically robust metrics.
Development of spacecraft constraints for multi-agent inspection mission, enforced using control barrier functions.
RL-based control for multirotor tracking of maneuvering ground targets using a fixed optical sensor under occlusions.
Stochastic control barrier functions and stochastic optimal control applied to classical economics problems.