Responsible for lane change infraction detection task and the creation of an end-to-end evaluation protocol for project lifecycle management.• Developed Deep Learning models • Conducted in-depth studies on state-of-the-art articles in the literature, sharing insights with the computer vision team.• Designed pipelines and algorithms for research and product development.• Led dataset creation and experimentation, data processing and analysis.

Engaged in a project titled "Development of Neural Approaches Resilient to Biases" across computer vision, natural language processing, and multimodal tasks. This research was funded by Motorola Mobility, a Lenovo Company.• Participated in the AAAI Conference 2023 held in Washington DC, USA.• Delivered a presentation of our paper at BRACIS 2022 in Campinas, USP.• Published a paper titled "Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey".

•Dataset Creation: Played a pivotal role in the entire dataset creation process for computer vision tasks, including activities such as data collection, frame selection, data augmentation among others.• Deep Learning Model Experimentation: Actively participated in deep learning model experimentation, refining and fine-tuning models for optimal performance.• Task Automation: automation solutions, particularly in the context of tasks such as face detection and automated blur application.

Helping students for better understanding about data structures and algorithms such as: time complexity, linked lists, queues, stacks and trees.

Developed a chat bot to enhance employee access to vital HR information.