• Obstacle Detection with model based and AI• Trajectory planning• Simulation (Unreal Engine)• Neural Network Acceleration (TensorRT)

• Programmed an Encoder in TensorFlow and used it to classify the images into regenerated or eroded.• Evaluated the forest health of Sierra Gorda from 2015 – 2021 using the Encoder and generated maps of foresthealth, measuring the overall state of the natural reserve in the process.• Used Google Earth Engine’s API and KMZ files provided by non-profit organization GESG to build a data-set of satellite images of Biosphere Reserve Sierra Gorda to train a machine learning model.

• Used NVIDIA’s TensorRT plugins and torch.fx tracer to speed up inference run-time of the Transformer Encoder deep learning architecture in Python.• Obtained a 700% inference speed-up on average, which helped save GPU resources within the company.• Transitioned internship into a full-time offer.

• Developed a genetic algorithm simulation that tested the viability and behavior of a population that communicatedwithout semantics.• Contrasted the non-semantic simulation with another genetic algorithm simulation that communicated with semantics.• Developed a GUI using Pygame to facilitate interaction with the non-semantic simulation.