I am a robotics engineer specializing in motion planning for autonomous systems. I completed my M.S. in Mechanical Engineering (Robotics & Controls) at Carnegie Mellon University in May 2026, and I now work as a research assistant in Prof. Kenji Shimada’s lab.
My years in industry and research left me with one conviction: autonomous systems rarely fail for lack of sophisticated algorithms; they fail when deployment conditions drift from the assumptions their models were built on. That is what drew me to planning: it is the discipline that makes those assumptions explicit. A well-posed planner searches a defined state space under explicit constraints and returns solutions with provable properties, completeness, bounded suboptimality, and feasibility with respect to the system’s limits, so when an assumption breaks, you know which one and where. That rigor is where my depth lies, built through coursework, implementation, and research that plans against a stochastic physical process. Learning enters where the world resists modeling, and I am building that side deliberately, from reinforcement learning agents to planning with imperfect world models; I come at it as a planning engineer asking how far a planner’s guarantees can extend.
The goal running through all of my work is motion the system can be trusted to execute: trajectories that are feasible within the platform’s kinematic limits, collision-free against the robot’s full geometry and its environment, computed within real-time budgets, and validated in simulation before they run on hardware.
The Research and Academic Projects sections walk through this work in detail. I am currently seeking motion planning software roles. Reach me at dlal1520@gmail.com.
