Moreton Capital Partners — Mexico City, México

Engineered a comprehensive model evaluation pipeline utilizing decile monotonicity and interactive clustermaps to track feature space collapse, monitor correlation dynamics, and isolate independent signals. Designed feature selection funnels to analyze information retention across the pipeline, mapping the progression from raw feature pools through correlated subsets to final representative features. Focused R&D specifically on LLMs and AI-Agents to enhance established non-deterministic models (LightGBM) with an experimental agentic layer, engineering orthogonal feature selection to feed specialized “analyst persona” agents non-redundant subsets (top 20, 50, and 100 ranked by IC and t-stats), ensuring inputs strictly augmented rather than duplicated the base model’s exposures.

Tec Monterrey Quant Society — Querétaro, México

Established the institution’s first student organization dedicated to quantitative finance, scaling membership to 30+ students in its inaugural semester. Teaching foundational quantitative finance concepts to lower-semester members while establishing the framework for the club’s upcoming collaborative group research initiatives. Directed a 5-person algorithmic trading team to finish in the Top 1.5% globally in the IMC Prosperity 4 challenge.

Instituto Via Diseño — Querétaro, México

Architected a constraint satisfaction scheduling system for a 150+ user campus environment, formalizing combinatorial resource allocation as a CSP and applying backtracking search with constraint propagation to guarantee feasibility under competing institutional constraints. Engineered PostgreSQL indexing strategies that reduced query latency by 40% under high-cardinality constraint evaluations, and surfaced scheduling state through a real-time React visualization layer.

Developed a two-stage backtesting engine for 10 commodity sectors over 162 weekly periods using SLSQP min-variance optimization, achieving max 7.7% annual volatility. Enforced KKT optimality for stable weight constraints and mplemented Ledoit-Wolf covariance shrinkage and a Stage A signal blending / Stage B cross-sector allocation pipeline that reduced Max Drawdown by 15%.

Syngenta @ START Hack — Top 5 Finalist (30 teams)

Engineered a real-time computer vision system to monitor crop health in Martian greenhouses, utilizing AWS SageMaker for autonomous stress detection. Developed a multi-modal data pipeline to process environmental telemetry and spectral imagery, enabling automated nutrient deficiency diagnosis in extraterrestrial environments.

Originally designed as a predictive ML model for drought patterns in CDMX using hydrological data. Pivoted to a crowdsourced reporting system to bridge data gaps. Designed the data ingestion pipeline to validate user reports against historical meteorological norms.

@ NASA Space Apps Hack

Engineered a LightGBM-based predictive model leveraging multi-spectral satellite imagery (GEE) and NASA POWER meteorological data. Conducted rigorous dimensionality reduction across 44 distinct ecological indicators to classify global bloom patterns with high precision (AUC/F1 validation).

Simulated autonomous agent behavior using Python (Mesa) and Unity to analyze strategic decision-making dynamics. Designed reward-based optimization functions within constrained state spaces, applying Monte Carlo sampling to identify emergent Nash equilibrium patterns.