Michel Vazirani

Michel Vazirani has held multiple positions in the software engineering and research fields. In 2022, they began working as a Software Engineer at GLMX - Access, Automate, Analyze. Prior to this, they were a Software Engineer l at FactSet from 2021. From 2019 to 2021, they were a Research Assistant at Columbia University in the City of New York, where they assisted Dr. Nakul Verma and Dr. Ansaf Salleb-Aouissi in developing a system using machine learning techniques to provide students practicing mathematics proofs with instantaneous feedback. Michel also designed algorithms to generate propositional logic proofs as synthetic data for model training, used standard classification and clustering models from scikit-learn, developed custom metric learning algorithms using convex optimization, and used Python SLY package (yacc wrapper) to build a parser to check syntax of propositional logic expressions. In 2018, they were a Revenue Team Intern at Smule, Inc.

Michel Vazirani graduated from Columbia University in 2021 with a Bachelor of Arts in Computer Science.