Zachary Hugo

Evaluate AI chatbot mathematical reasoning across algebra, geometry, calculus, and probability, designing diverse and complex problems that maintain 92% reviewer accuracy on 1,100+ solutions monthly. Create detailed scoring rubrics to validate step-by-step logic, reducing review time by 33% and achieving Cohen’s kappa = 0.89 inter-rater agreement across teams. Audit AI outputs for correctness, performance, and logical consistency, identifying systematic reasoning gaps that increased first-pass correctness by 24% in 8 months. Build parametric problem generators in optimization, combinatorics, and statistical inference, expanding evaluation coverage across 12 domains with controlled difficulty scaling. Implement Python QA automation using regex and JSON/CSV specifications, eliminating 94% of computational errors and formatting issues.

Evaluated AI-generated mathematics solutions for logical reasoning accuracy and performance, processing 950+ complex problems monthly with >91% reviewer accuracy. Developed edge-case taxonomies for geometric proof validation, reducing false-positive acceptance rates by 36% through systematic error pattern identification. Partnered with ML engineers to analyze reasoning failure modes, improving multi-step problem-solving accuracy by 21%. Built Airtable dashboards tracking correctness, mathematical rigor, and clarity across probability, statistics, and optimization problems. Led weekly calibration sessions for 12 reviewers, creating SOPs that standardized evaluation criteria across 8 mathematical disciplines.

Designed and tested complex assessment frameworks for optimization, statistics, and geometry problems, achieving 96% domain coverage. Built automated verification tools in Python and SymPy, reducing manual review time by 41% while maintaining high accuracy through QC sampling. Analyzed AI tutoring model reasoning patterns, documenting 19 error types and designing targeted remediation strategies. Created benchmark datasets for combinatorial and rate/time problems, achieving 0.87 correlation with expert human evaluations on 2,400+ test cases.

Conducted systematic error analysis on AI-generated solutions for 1,200+ mathematical scenarios monthly. Developed parametric problem generators with difficulty scaling for algebra, calculus, and discrete math evaluation. Created automated proof verification protocols in Mathematica and Python, reducing expert review workload by 31%. Standardized symbolic computation formats for mathematical notation, achieving 88% consistency in LaTeX solution formatting.

Focus: Optimization Theory, Probability Theory, Linear Algebra, Numerical Analysis, Mathematical Modeling

Magna Cum Laude, Mathematics Honor Society