I build robots that reason about the physical world — specializing in manipulation, dynamics modeling, and bridging simulation to reality. Currently finishing a PhD in Robotics Engineering at WPI.
I'm a PhD candidate at Worcester Polytechnic Institute, where I research active perception and parameter estimation for robot manipulation. My work focuses on letting robots figure out the physical properties of objects they've never encountered before — without being told in advance.
My background is rooted in analytical, model-based methods — robot dynamics, sim2real transfer, and model-based control. I'm increasingly combining these with modern machine learning to build systems that are both physically grounded and data-driven.
Before my PhD, I deployed and programmed autonomous mobile robots for Fortune 500 clients at Seegrid. I hold a B.S. in Mechanical Engineering from Columbia University. I'm looking for research engineer roles where physical grounding matters — where simulation meets hardware and systems ship.
Also presented at New England Manipulation Symposium 2025 — oral presentation of the CASE 2025 work.
Selected independent and course projects, now surfaced as visual snapshots with links to code, reports, and deeper writeups where available.
A simulated bin-sorting robot revealed how imitation policies can memorize shortcuts instead of grounding actions in scene geometry.
A MuJoCo environment where a robot arm learns to estimate unknown object mass and inertia during manipulation.
A graph-based multi-robot task allocation system for distributing robots around a manipulated payload in real time.
A low-level OpenGL haptics simulation that models contact forces and physical constraints for real-time tactile feedback.
A DQN controller trained in simulation to drive grasp selection for a Franka robotic arm pick-and-place task.
A Columbia capstone rover prototype using tensile integrity to survive impacts without a rigid outer shell.
I'm actively looking for research engineer and applied scientist roles in robotics and robot learning, based in Boston. Open to relocation for the right opportunity.