Trajectory optimization and planning for L2/L2+/L2++ autonomous vehicles.

Trajectory optimization and planning for L2/L2+/L2++ autonomous vehicles.

Localization team.

• Extended the use of GNSS in the localizer.

• Integrated odometer tick measurements into the localizer.

• Improved and benchmarked LIDAR segmentation for more robust ICP.

Graduate level:

• CS 229: Machine Learning (Profs. M. Charikar, T. Ma, and C. Ré, Spring 2020, ~500 students)

• CME 204: PDEs in Engineering (Prof. S. Lele, Winter 2020, ~50 students)

• CS 229: Machine Learning (Profs. M. Charikar, A. Ng, and C. Ré, Fall 2019, ~400 students)

Undergraduate level:

• CME 102: ODEs for Engineers (Prof. H. Le, Spring 2019, ~50 students)

• CME 106: Introduction to Probability and Statistics for Engineers (Prof. V. Khayms, Winter 2019, ~200 students)

• Developed robotics software (Python, ROS) in the Wayfinder team, Airbus' R&D effort to enable self-piloted flight.

• Post-processed in-flight GNSS, INS, ADS-B and RGB data for analytical aircraft labeling via geographic projections.

• Mitigated delay-induced errors via interpolation of aircraft trajectories.