PhD Candidate in Operations Research
North Carolina State University
My research focuses on optimization methods and their applications to ranking, clustering, and decision-making problems. I am particularly interested in developing algorithms that bridge theoretical foundations with practical applications in sports analytics and beyond.
Mixed-integer programming, combinatorial optimization, and algorithm design for complex decision problems.
Preference aggregation, tier construction, and cut imbalance methods for hierarchical groupings.
Applying operations research methods to team ranking, player evaluation, and strategic analysis.
Management Science, 2024
We provide the first approximation algorithm for dynamic inventory management on a network with stochastic demand and backlogging. Under a mild cost condition, we prove the cost of a specially designed base-stock policy is less than 1.618 times the cost of an optimal policy. We develop a novel stochastic programming analysis and demonstrate our policy performs within 1% of optimal on average across a wide range of problem instances.
We introduce cut imbalance clustering, a method for partitioning ranked items into meaningful tiers based on pairwise preference data. Our approach identifies natural tier boundaries by maximizing the cumulative cut imbalance across partitions.
Interactive demonstrations and applications of my research methods.
Applying cut imbalance clustering to rank and tier NFL teams using 2024 season statistics.
View AnalysisMonte Carlo simulation evaluating go-for-it, punt, and field goal decisions using team-specific NFL data.
Try SimulatorQuantifying the $1.26B revenue gap between primary and secondary ticket markets across all 32 NFL teams.
View AnalysisInteractive analysis of 3.8M athletes across 16 years, examining whether height, weight, and gender predict performance.
View AnalysisInteractive visualization tracing the intellectual lineage of inventory management from Harris (1913) through Clark & Scarf to modern approximation algorithms.
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