As the Director of Machine Learning at Surgical Data Science Collective (SDSC), a non-profit startup that is utilizing computer vision to improve surgical skills, I am passionate about applying technology to make a positive impact. With a background in computer science and expertise in machine learning, I have dedicated my career to optimizing algorithms that can automate and streamline complex processes. My work involves leading research focused on utilizing video data from surgeries to develop tools that can provide surgeons with immediate feedback and insights on their performance. This is a critical area of need, as surgical training is currently very limited and physicians often rely on feedback solely from their mentors. By creating an AI-powered platform, our vision is to help surgeons improve their skills and ultimately provide better outcomes for patients. I am excited to be part of this innovative startup and look forward to driving impact in the medical field by leveraging the power of cutting-edge technology. In the past, I have worked on developing spatially explicit models for risk assessment, computer vision models for automatic tree measurements (https://www.earthshot.eco/biome), and generative adversarial networks for visual simulation of reforestation (https://climate-viz.github.io/). My research interests include deep learning, computer vision, software engineering, product development, image processing, and the application of AI for climate and healthcare-related issues. I have worked on several projects using deep learning for climate issues, such as deforestation detection in the Congo Basin, crop yield prediction in Senegal, and classification of facility rooftops to determine solar installation potential. I am a science writer and a strong advocate for diversity in STEM. I am happy to announce that I am a part of the Lesbians Who Tech Leadership SQUAD 2023 Cohort.