Suhas Mathur
About Suhas Mathur
Suhas Mathur is a Quantitative Researcher currently at Two Sigma Investments, with a background in electrical engineering and machine learning. He previously worked at AT&T Labs and holds a PhD from Rutgers University, along with multiple patents and awards for his research contributions.
Work at Two Sigma
Suhas Mathur has been employed at Two Sigma Investments as a Quantitative Researcher since 2014. In this role, he focuses on developing and implementing quantitative models that inform investment strategies. His work contributes to the firm's data-driven approach to finance, leveraging statistical methods and machine learning techniques to analyze market trends and optimize portfolio performance.
Previous Experience at AT&T Labs
Prior to joining Two Sigma, Suhas Mathur worked at AT&T Labs, Inc. as a Senior Member of Technical Staff from 2010 to 2014. During his four years in New York, he developed statistical machine learning models aimed at detecting malicious activities and identifying anomalous patterns. His contributions enhanced the security and efficiency of AT&T's systems.
Education and Expertise
Suhas Mathur holds a PhD in Electrical and Computer Engineering from Rutgers, The State University of New Jersey-New Brunswick, where he studied from 2004 to 2010. He also earned a B.Tech in Electrical Engineering from the Indian Institute of Technology, Madras, from 2000 to 2004. Additionally, he studied at New York University's Courant Institute of Mathematical Sciences from 2011 to 2019, furthering his expertise in mathematical sciences.
Patents and Research Contributions
Suhas Mathur holds eight patents, one of which has been licensed to a major automobile manufacturer. His research has been recognized in various prominent publications, including MIT Technology Review, ACM Technews, CBC News, and the NY Times syndicate. His work has contributed to advancements in machine learning and signal processing.
Awards and Recognition
Throughout his career, Suhas Mathur has received several awards from the Association of Computing Machinery (ACM) and New York University for his contributions to the field. These accolades reflect his commitment to excellence in research and development within quantitative analysis and engineering.