Michael Long

Michael Long is a research data scientist with expertise in working with open-source Large Language Models (LLMs) and using Natural Language Processing (NLP) to extract data from unstructured text. In collaboration with the National Institutes of Health (NIH) and other agencies, his knowledge helps teams find innovative ways to maximize efficiency for new and existing projects. 

Mr. Long supports the NIH Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program, teaching data science to and mentoring graduate students and assistant faculty members. As a part of this project, he is also creating a framework for AIM-AHEAD to use open-source LLMs to extract NIH MeSH (Medical Subject Headings) terms from abstracts. In addition, Mr. Long analyzes social determinants of health and its connection with diabetes, supporting an established microsimulation framework to make it more efficient and more generalizable to other public health research. Outside of this work, he is conducting research on how to use NLP to extract text from high school and college transcripts to partially automate the transcription process. 

Mr. Long also previously worked as a software engineer at RTI. During that time, he contributed to a variety of data visualization dashboards, partnering with federal agencies including the U.S. Department of Justice’s Bureau of Justice Statistics and NIH on the National Crime Victimization Survey Data Dashboard (N-DASH) and the MapMECFS data sharing portal, respectively.