About

Sarah Hernandez is a Lead Machine Learning Engineer at HyperSpectralAPD with a passion for technology and innovation. With a strong background in computer science and mechanical engineering, Sarah is driven to make the world a better place through her work. She is currently pursuing a Masters in Computer Science at Georgia Tech while working on building deep learning models to detect Covid-19 and beyond. Sarah is excited about the future of voice, autonomous vehicles, and AR, and is always looking for ways to incorporate these emerging technologies into her work. At HyperSpectralAPD, Sarah is responsible for establishing a company-wide machine learning framework for modeling, testing, and verification. She leads and mentors a team of ML Engineers to solve computer vision and ML problems. Sarah's work at HyperSpectralAPD involves adapting models for the next Covid and deploying models to create a faster, cheaper, and more accurate Covid-19 test. Prior to joining HyperSpectralAPD, Sarah worked as a Machine Learning Engineer at Senseye, Inc. where she designed and built an ML pipeline from the ground up, parallelized for optimal runtime. She trained and deployed computer vision models such as UNet and 3DDFA to segment eye and head features, and sent eye features through CNN's and other models to determine cognitive state. Sarah also created, planned, and managed the development of company-wide machine learning pipelines that predict cognitive states. Before that, Sarah worked as a Data Engineer at Senseye, Inc. where she developed production-level code that trains, runs, and tests machine learning models that analyze signals in the human eye. Sarah's education includes a Bachelor of Science in Mechanical Engineering from Rice University and a Master of Science in Computer Science from Georgia Tech. Her tech stack includes ML, Computer Vision, test, Deep Learning, and CNN. With 2.86 years of relevant experience, Sarah is a great communicator and loves baseball, hiking, building furniture, gardening, and Star Trek.