Christian Cavener

Selected to participate in a SIG Discovery Day, which provided exposure to the firm’s culture, technology stack, and proprietary approach to quantitative trading Took part in a competitive market-making game, applying fundamental market-making concepts, estimation, and quick decision-making to test knowledge of and intuition for the proprietary trading conducted by SIG

(Caltech First-Year Success Research Institute (Caltech FSRI)) Implemented a quantitative framework to model SQL assignment Halstead query complexity (QC) distributions across peer institutions to identify opportunities to align Caltech’s SQL curriculum with industry standards Developed SQL problems and solutions with higher average QC scores (20.21 to 31.16) while maintaining manageability, applying Bloom’s taxonomy to scaffold cognitive difficulty and target higher-order problem-solving skills Leveraged real-world datasets and advanced SQL concepts to evaluate pedagogical effectiveness, improving both rigor and practical applicability

Selected as 1 of 61 scholars (≈1.5% acceptance rate) for MITES Summer, an intensive six-week STEM program in calculus, physics, biology, machine learning, and humanities at MIT Completed 50+ hours of machine learning coursework and an end-of-summer machine learning research project, developing applied skills in modeling, training, and evaluation Ranked Top Scholar in Calculus 1 and Physics 1, recognized as Most Improved Scholar in Machine Learning

GPA: 4.0

Class Rank: 1 out of 519 (Valedictorian)