John Sigman, Ph.D.

John Sigman, Ph.D.

Principal Data Scientist @ Infinia

About John Sigman, Ph.D.

John Sigman, Ph.D., is a Principal Data Scientist at Infinia ML with extensive experience in data science and engineering, particularly in deep learning and entity extraction. He has previously held positions at Fannie Mae, Duke University, and Alarm.com, and has contributed to various projects in healthcare, finance, and energy sectors.

Current Role at Infinia ML

John Sigman serves as the Principal Data Scientist at Infinia ML since 2019. He operates in the Raleigh-Durham, North Carolina Area. In this role, he focuses on advanced data science applications, including the development and implementation of machine learning models. His expertise in large language models (LLMs) and entity extraction contributes to Infinia ML's innovative solutions in various sectors.

Previous Experience at Fannie Mae

Prior to his current position, John worked as a Software Contractor at Fannie Mae in the Financial Engineering department from 2012 to 2016. This part-time role was conducted remotely and involved significant contributions to large language model entity extraction. He focused on fine-tuning and self-hosting open-source LLMs, enhancing the organization's data processing capabilities.

Academic Background

John Sigman has an extensive academic background in Electrical Engineering. He earned his PhD from Dartmouth College, where he specialized in Computational Electromagnetics from 2012 to 2017. He also holds a BS in Electrical Engineering from the University of Virginia, completed from 2006 to 2010. Additionally, he conducted postdoctoral research at Duke University, focusing on deep learning and computer vision from 2017 to 2019.

Research and Development Contributions

Throughout his career, John has engaged in various research and development projects. He contributed to forecasting grid use for a large energy services firm and worked on healthcare payment remittance entity extraction. His work also includes developing biometric database entity matching solutions and leading projects such as Medical Record Retrieval Augmented Generation (RAG) and Legal Contract AI R&D.

Technical Skills and Projects

John Sigman's technical skills encompass deep learning, computer vision, and advanced data extraction techniques. He has developed systems for earnings statement recognition and extraction, and engaged in vision-based layout detection in documents. His experience includes managing large-scale medical insurance coordination of benefits projects and conducting research on 3D volumetric object detection for threat recognition.

People similar to John Sigman, Ph.D.