About

Thomas Chase Corcoran is a highly experienced Data Scientist and Machine Learning Engineer with a passion for improving the US healthcare system. With over 6 years of relevant experience, he has developed expertise in Natural Language Processing (NLP), Computer Vision, and Machine Learning, which he leverages to extract unstructured information from clinical text. Currently, Thomas is working at Ciitizen, where he holds the position of Data Scientist / Machine Learning Engineer. His primary focus is on improving the US healthcare system through the use of NLP, Computer Vision, and Machine Learning. He is responsible for extracting unstructured information from clinical text and using it to develop innovative healthcare solutions. Prior to joining Ciitizen, Thomas worked at Change Healthcare as a Data Scientist. Here, he was responsible for transformer-based document classification using Apache Spark to sort 100M pages per year. He also developed Named Entity Recognition (NER) for medical records and designed and built an in-house labeling and active learning platform using Docker and Serverless on AWS. Thomas also has experience working at Automation Anywhere, where he held the position of Lead Machine Learning Engineer. Here, he led research and development on document digitization and understanding, using a variety of techniques such as statistical analysis, computer vision, NLP, and deep learning. Thomas holds a Nanodegree in Machine Learning Engineering from Udacity, which he completed in 2016. He also has a Bachelor of Arts in Linguistics from the University of California, Santa Cruz, which he completed in 2014. Thomas is highly skilled in ML, Research Scientist, Data Scientist, NLP, Computer Vision, AWS, Spark, Docker, and Deep Learning. His passion for using his skills to improve the healthcare system makes him a valuable asset to any team.