Marc Simon, PhD
About Marc Simon, PhD
Marc Simon, PhD, is a Principal Data Scientist at Enolink, where he focuses on applying AI and machine learning to improve healthcare outcomes. He has a strong academic background in physics and extensive experience in research and development within the healthcare sector.
Current Role at Enolink
Marc Simon serves as the Principal Data Scientist at Enolink, a position he has held since 2021. In this role, he focuses on leveraging artificial intelligence and machine learning to address complex challenges within the healthcare sector. His work involves developing personalized models that utilize explainable machine learning techniques to enhance healthcare outcomes. Simon's expertise in federated learning and differentially private systems plays a crucial role in his contributions to the company's projects.
Previous Experience at Karen Clark and Company
Before joining Enolink, Marc Simon worked at Karen Clark and Company from 2016 to 2021. He initially held the position of Research Scientist for one year before advancing to Senior Research Scientist, where he worked for four years. During his tenure in Boston, Massachusetts, Simon contributed to various research initiatives, applying his knowledge in physics and data science to support the company's objectives.
Educational Background in Physics
Marc Simon has a strong educational foundation in physics. He earned his Bachelor's degree from Massachusetts College of Liberal Arts, where he studied from 2008 to 2012. He then pursued further studies at Tufts University, obtaining both a Master's degree and a Doctor of Philosophy (PhD) in Physics. His graduate studies spanned from 2012 to 2016, during which he also served as a Research Assistant at Tufts University, gaining valuable research experience.
Specialization in AI and Machine Learning
Marc Simon specializes in building federated learning and differentially private systems. His focus on artificial intelligence and machine learning enables him to develop innovative solutions that address complex problems in healthcare. By employing explainable machine learning, he aims to create personalized models that improve patient outcomes and drive growth within the sector.