Daniel Khalil

Daniel Khalil is an experienced software engineer and researcher with a strong background in machine learning and algorithm development. At NASA, Daniel contributed to logistical decision-making through Python-based risk analysis as a Software Engineer Intern. A Machine Learning Intern at Jefferson Lab, Daniel developed neural networks for data quality analysis of particle accelerators. Daniel now works as a Research Intern at Google, focusing on machine learning for Project Starline, and also serves as a researcher in both the Perona Lab and Yue Lab at Caltech, engaging in advanced projects like self-supervised keypoint prediction and protein generation. Previous experience includes data analysis and risk management during a FOCUS Fellowship at Jane Street and creating machine learning models for robotic applications at Carnegie Mellon University.