Michael Rabadi
About Michael Rabadi
Michael Rabadi is a Quantitative Researcher currently employed at Squarepoint Capital. He has a background in neuroscience and machine learning, holding both a Bachelor's degree and a Ph.D. from New York University, and has held various research and engineering roles at institutions such as Spotify and Balyasny Asset Management.
Current Role at Squarepoint Capital
Michael Rabadi serves as a Quantitative Researcher at Squarepoint Capital, a position he has held since 2024. In this role, he applies his expertise in quantitative analysis and machine learning to develop strategies for financial markets. His work involves analyzing large datasets to inform investment decisions and optimize portfolio performance.
Previous Experience at Balyasny Asset Management
Prior to his current role, Michael worked at Balyasny Asset Management L.P. as a Portfolio Manager and Head of Machine Learning from 2020 to 2023. During his three years there, he focused on integrating machine learning techniques into portfolio management processes, enhancing the firm's investment strategies through data-driven insights.
Academic Background and Education
Michael Rabadi earned his Bachelor’s Degree in Neuroscience and Business from New York University, completing his studies from 2010 to 2014. He furthered his education at the same institution, obtaining a Doctor of Philosophy (Ph.D.) in Neuroscience and Machine Learning between 2014 and 2016. This strong academic foundation supports his work in quantitative research and machine learning applications.
Research Experience at New York University
From 2010 to 2016, Michael worked as a Researcher at New York University. His six years in this role involved conducting research that contributed to the understanding of neuroscience and its applications. This experience laid the groundwork for his later work in machine learning and quantitative analysis.
Teaching Role at Columbia University
Michael served as Adjunct Faculty at Columbia University in the City of New York from 2018 to 2019. In this capacity, he contributed to the academic community by teaching courses related to his areas of expertise, sharing his knowledge of machine learning and quantitative research with students.