Robotics and controls engineer, MEng in Control of Robotic & Autonomous Systems at UC Berkeley (graduating May 2027). I work on model-based control and physics-informed learning for robots, with a mechanical engineering background in simulation, dynamics and prototyping.
At the Agile Robotics and Perception Lab (Prof. Giuseppe Loianno) I am developing an energy-aware nonlinear MPC for fixed-wing dynamic soaring as my MEng capstone. That code stays private while the work is in progress.
- ur5e-neural-kinematics: neural inverse kinematics for a UR5e, trained through a differentiable forward-kinematics model in PyTorch. 0.19 mm on held-out test targets and 0.53 ms per solve inside a camera-guided Webots pick-and-place loop, benchmarked on the same pose problem against closed-form, damped-least-squares and IKPy solvers. Preprint.
- helmholtz-resonator-solver: two independent finite-difference solvers for an open Helmholtz resonator, verified by manufactured solutions and grid convergence, compared with published measurements. Preprint.
- copv-type4-multifidelity: a CalculiX composite-shell model of a Type IV hydrogen vessel run on 384 designs and an MLP corrector for a fast sizing model in an existing genetic-algorithm optimizer, followed by an audit showing the learned dome peak was not mesh-converged. Preprint.
- ducted-fan-aeroacoustics and ventilator-modal-analysis-petgcf: URANS with Ffowcs Williams-Hawkings acoustics in OpenFOAM, and modal and impact analysis in Code_Aster, for a fan studied at Polytechnia, computed on the small Linux cluster I set up. Acoustics preprint.
Predictive Control Systems (Borrelli), Introduction to Robotics (Horowitz), Drone Digital Twins (Zohdi), Physics-Inspired Machine Learning (Krishnapriyan).