Summary
Caglar Dogan is a quantitative researcher at Susquehanna International Group with nine years of software and research experience and a recent MS in Computer Science from NYU’s Courant Institute. He blends theoretical computer science—particularly computation theory and geometric algorithms—with practical ML and quantitative finance work, having developed a subdivision-based algorithm for isotopic approximations of 3D implicit curves during his master’s thesis. Prior roles span systems security research (building and optimizing a custom VM and POSIX interfaces), AV simulation performance tuning with ROS, and backend data pipelines using Scala, Spark, and Cassandra, reflecting strong full-stack and systems instincts. Comfortable moving between rigorous proofs and production optimizations, he has a knack for extending interval-arithmetic techniques to higher co-dimension problems—an example of applying deep theory to concrete computational challenges. Based in New York, he brings a rare mix of academic rigor and hands-on implementation across low-level systems and quantitative trading environments.
9 years of coding experience
3 years of employment as a software developer
International Honors Program (Summer Exchange) Computer Science, International Honors Program (Summer Exchange) Computer Science at Stanford University
Master of Science - MS Computer Science, Master of Science - MS Computer Science at New York University
Bachelor of Science - BS Computer Engineering, Bachelor of Science - BS Computer Engineering at Boğaziçi University
English, Turkish, French, German