Summary
Chase Goddard is a Princeton PhD researcher blending physics, computer science, and machine learning with a decade of research experience across academic and industry labs. His current work focuses on understanding generalization in ML, informed by a strong background in high-energy physics at Cornell and hands-on experience with the CMS experiment at CERN. Comfortable at the intersection of theory and applied data science, he has also brought analytical rigor to industry as a data science intern at BCG. Based in New Jersey, Chase combines experimental intuition from X-ray spectroscopy and particle physics with computational approaches to probe why models generalize, making him well suited for research-orientated roles that bridge physics and ML. A less obvious strength is his track record of moving between deeply experimental projects and abstract ML questions, giving him a rare perspective on bridging noisy real-world data with principled learning theory.
10 years of coding experience
3 years of employment as a software developer
Bachelor's degree Physics & Computer Science, Bachelor's degree Physics & Computer Science at Cornell University
Rye Country Day School
PhD Physics, PhD Physics at Princeton University
English, Chinese