Yeting Ge
About Yeting Ge
Yeting Ge is a software engineer with a PhD in Computer Science from New York University. He has extensive experience in formal verification, model checking, and automated reasoning, currently working at Two Sigma Investments since 2010.
Work at Two Sigma Investments
Yeting Ge has been employed at Two Sigma Investments as a Software Engineer since 2010. In this role, Ge applies expertise in software development and computational problem-solving. The position involves working on complex software systems, leveraging skills in formal verification and automated reasoning to enhance system reliability and performance.
Education and Expertise
Yeting Ge holds a PhD in Computer Science from New York University, where studies spanned from 2003 to 2009. Prior to this, Ge earned a Master of Engineering (MEng) in Computer Science from Nanjing University. This educational background provides a strong foundation in software engineering principles, particularly in formal verification and model checking.
Background in Academia
Before joining Two Sigma Investments, Yeting Ge worked in various academic roles. Ge served as a Postdoctoral Scholar at the University of Iowa for 10 months in 2010. Additionally, Ge was a Teacher at Nanjing University from 2001 to 2003, contributing to the education of students in computer science.
Internship Experience
Yeting Ge gained valuable industry experience through internships at notable organizations. In 2006, Ge interned at NEC Laboratories America for 3 months, followed by a 3-month internship at Microsoft Research in 2008. These experiences provided practical insights into software development and research methodologies.
Technical Skills and Proficiencies
Yeting Ge possesses proficiency in multiple programming languages, including OCaml, C++, and Java. This diverse technical skill set supports the development of robust software solutions. Ge's expertise also extends to automated reasoning and satisfiability modulo theories (SMT), which are essential for addressing complex computational challenges.