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Class of 2027

Sean holds a B.S. in Statistics from UC Davis, where he concentrated in machine learning and built a rigorous foundation in time series analysis, hypothesis testing, and experimental design. His applied work began in earnest during an internship with UC Davis Facilities Management, where he developed predictive and anomaly detection models for campus-wide HVAC energy systems. The work was substantive: collaborating with engineers to architect residual-based anomaly frameworks and deploying LSTM, Random Forest, and Gradient Boosting methods with careful attention to feature engineering and hyperparameter optimization to meaningfully improve fault detection accuracy.

He is currently completing a Master's in Machine Learning and Data Science at Northwestern University, with a focus on scalable machine learning systems designed for production environments. Northwestern's interdisciplinary structure suits his interests well. He is less drawn to ML as an end in itself than to its capacity to drive consequential decision-making across complex, real-world domains.

When he steps away from the work, he trains Muay Thai, seeks out good tea, and plays mahjong.