Milad Kharratzadeh
About Milad Kharratzadeh
Milad Kharratzadeh is a Machine Learning Researcher with extensive experience in applying statistical models to financial data. He has worked at Squarepoint Capital since 2017 and has a strong academic background, including a PhD from McGill University.
Work at Squarepoint Capital
Milad Kharratzadeh has been employed at Squarepoint Capital as a Machine Learning Researcher since 2017. His role involves the application of statistical models to enhance financial data insights. He has contributed to the development of machine learning algorithms specifically tailored for financial data analysis, leveraging his expertise to support the firm's quantitative strategies.
Education and Expertise
Milad Kharratzadeh holds a Doctor of Philosophy (PhD) in Electrical and Computer Engineering from McGill University, which he completed from 2012 to 2016. He also earned a Master's degree in the same field at McGill University from 2010 to 2012. His academic journey began with a Bachelor's degree in Electrical Engineering from Sharif University of Technology, completed from 2006 to 2010. Additionally, he participated in the prestigious Machine Learning Summer School in Kyoto, Japan, further advancing his knowledge in machine learning.
Background
Prior to his current position, Milad Kharratzadeh worked as a Postdoctoral Research Scientist in Bayesian Statistics at Columbia University in the City of New York for seven months in 2016 to 2017. He also served as a Graduate Research Assistant at McGill University from 2010 to 2016, where he engaged in various research projects. His early career included internships at Winton Capital Management as a Research Intern in 2015 and at Appnovation Technologies as a Big Data Intern in 2013.
Achievements
Milad Kharratzadeh has been actively involved in the application of statistical models to financial data since joining Squarepoint Capital. His work focuses on developing machine learning algorithms that enhance data analysis capabilities within the financial sector. His participation in the Machine Learning Summer School in Kyoto reflects his commitment to advancing his expertise in this rapidly evolving field.