- Developing driverless capabilities for the 2024 Formula Student Germany Driverless Cup- Working with the ROS2 framework in C++ and Python- Perception pipeline from a LiDAR sensor and cameras running a trained YOLO cone detection model- Fused vehicle state estimation with an Extended Kalman Filter- SLAM, local/global path planning, validation in simulation- Interfacing with vehicle sensors and actuators via CAN bus and reducing set point error with PID

- Developing driverless capabilities for the 2023 Formula Student Germany Driverless Cup- Working with the ROS2 framework and C++- Validating in SITL simulation environment in Gazebo- Running environment with Docker, visualizing results with RQt and RViz- Implementing trajectory rollout and an occupancy grid for autonomous navigation

- Created 3D CAD models of the aerodynamic package of the car using SolidWorks- Simulated and analyzed aerodynamic effects using the CFD software STAR CCM+ - Assisted team members with the manufacturing of carbon fibre to create the bodywork on the car

Team: Data & AI - MDM/Match360 Engine and PipelineTools: Docker & Kubernetes, RedHat Openshift, Java, Bash scripts, Jenkins, Travis, REST APIs, Postman, JMeter- Automated CI tasks for the team's pipeline using Jenkins, Travis API, Github CLI, and Bash scripting to reduce developer workload and increase testing coverage- Debugged REST API issues with different request commands and Postman- Managed and automated dependency update tasks with Maven- Tested API requests and version changes locally and on cloud instances after setting up Match360 on test clusters- Investigated, researched, and debugged issues with RabbitMQ, Elasticsearch, and Kibana- Created multi-threaded API test plans with dynamic payloads with JMeter

- Managing timelines, tasks, goals, selecting hardware, and setting test procedures for a subteam of 5 members - Interfacing with an autonomous, fixed-wing drone using QGroundControl, PX4 firmware, MAVLink, and ROS - Programming autonomous navigation and communication tasks using MAVROS and RQt, to send commands, read telemetry, stream a video feed, detect and localize a ground target- Validating flight scripts using SITL simulations with Gazebo and PX4 source code

- Working in the avionics division for a fixed-wing drone to compete in the 2021 SAE Aero Design competition- Programming path planning and communication tasks for the autonomous aircraft using MissionPlanner, ArduPilot, MAVLink messages, Lua scripts, and Pymavlink in Python and C++ to send commands, read telemetry, etc.- Validating programs using SITL Simulations with ArduPilot and MissionPlanner in a WSL environment- Working with CV for landing zone target detection using the DepthAI API, cv2 and NumPy

- Data used in paper: "Effect of Periodic Longitudinal Gusts on Airfoil Performance Under Separated-Flow Conditions" by Suraj Bansal, Philippe Lavoie, DOI: 10.2514/6.2022-4104- NSERC URSA Grant Recipient-Supervisor: Philippe Lavoie, Suraj Bansal- Experimentally investigating the unsteady aerodynamic behaviour of an airfoil for a wide range of laminar and turbulent longitudinal gusts with smoke flow visualization- Automated and expanded capabilities of a smoke flow visualization set up in a wind tunnel using a microcontroller circuit with Python and C++ code- Enabled precise actuation of smoke flow equipment for unsteady aerodynamics with an active grid- Studied steady-state and unsteady-state laminar separation bubble characteristics

- Led and founded the school’s robotics club with a membership of over 70 students- Appointed executive members, planned timelines, led robotics teams in competitions, and mentored new members- Organized financing, secured thousands of dollars of sponsorships from different companies, purchased parts- Managed the construction and design of my team's robot- Led our team to its best-ever season, where we placed 2nd place in one competition- Received multiple awards, and competed at the provincial championship- In 2021, its teams placed 3rd and 4th in the world championship

- Tutored Grade 9, 10 and 11 students in math.- Developed teaching materials, monitored student progress, and identified areas needing improvement- Assisted students with homework and prepared them for tests and exams.