Andréa Papaleo
About Andréa Papaleo
Andréa Papaleo is a software engineer currently employed at the Broad Institute of MIT and Harvard and Focused Financing, with a strong background in computational physics and extensive experience in developing web-based scientific software.
Work at Broad Institute
Andréa Papaleo has been employed as a Software Engineer at the Broad Institute of MIT and Harvard since 2022. In this role, she focuses on developing web-based scientific software aimed at the classification and segmentation of biological images. Her work contributes to advancing research in the life sciences through innovative software solutions.
Current Role at Focused Financing
Since 2018, Andréa Papaleo has also served as a Software Engineer at Focused Financing. In this position, she applies her skills in software development to support financial solutions. Her experience in this role complements her work at the Broad Institute, showcasing her versatility in the software engineering field.
Educational Background
Andréa Papaleo completed her Bachelor of Science in Engineering Physics/Applied Physics at Binghamton University from 2015 to 2018. She further pursued her education at the same institution, earning a Doctor of Philosophy in Materials Science from 2018 to 2021. Additionally, she holds an Associate of Science in Computer Science from Suffolk County Community College, which she completed from 2013 to 2015.
Previous Experience at Binghamton University
Andréa Papaleo has held multiple positions at Binghamton University. She worked as a Graduate Research Assistant from 2017 to 2022, where she contributed to research projects. Prior to that, she served as an Undergraduate Research Assistant from 2016 to 2017 and as a High Performance Computing Assistant from 2021 to 2022. These roles provided her with a strong foundation in computational physics and software development.
Technical Skills and Expertise
Andréa Papaleo possesses a robust skill set in both front-end and back-end technologies, including proficiency in Tensorflow. Her strong background in computational physics enhances her problem-solving capabilities, allowing her to develop precise and efficient software applications. She is committed to continuous learning and improving her skills in software engineering.