John Sigman, Ph.D.
About John Sigman, Ph.D.
John Sigman, Ph.D., is a Principal Data Scientist at Infinia ML, specializing in deep learning and computer vision. He has extensive experience in biotherapeutics particle detection, biometric database solutions, and large-scale project management in various sectors including healthcare and finance.
Work at Infinia ML
John Sigman, Ph.D., has been serving as Principal Data Scientist at Infinia ML since 2019. In this role, he focuses on advanced data science applications, including large language model (LLM) entity extraction, fine-tuning, and self-hosting of open-source LLMs. His work also encompasses healthcare payment remittance entity extraction and vision-based layout detection in documents. His expertise contributes to the company's mission of leveraging machine learning for innovative solutions.
Education and Expertise
John Sigman earned his Ph.D. in Electrical Engineering with a focus on Computational Electromagnetics from Dartmouth College, where he studied from 2012 to 2017. He also holds a Bachelor of Science in Electrical Engineering from the University of Virginia, completed from 2006 to 2010. His educational background is complemented by extensive research in deep learning and computer vision, conducted during his postdoctoral tenure at Duke University from 2017 to 2019.
Background
Prior to his current role, John Sigman worked as a Postdoctoral Researcher at Duke University, where he engaged in vision-based biotherapeutics particle detection and characterization. He also held a part-time position as a Software Contractor at Fannie Mae, focusing on financial engineering from 2012 to 2016. His early career included a role as Associate Engineering Program Manager at Alarm.com from 2010 to 2012.
Achievements
Throughout his career, John Sigman has led various significant projects, including the development of biometric database entity matching solutions and a Medical Record Retrieval Augmented Generation (RAG) project. He has also contributed to forecasting grid use for a large energy services firm and worked on M&A insurance RFP extractive summarization. His research includes 3D volumetric object detection for threat recognition and the development of graph methods for communication recommendations at a tier-one investment bank.