Mathematical Optimization
LP, MIP, MINLP, network flows, stochastic & robust optimization, NP-hard combinatorial problems.
I'm an Operations Research scientist who builds things that ship. My PhD is in mixed-integer nonlinear optimization; my day job is leading a team at SAS Institute that turns that kind of mathematics into production systems for Fortune 500 companies — supply chain networks, manufacturing processes, transportation assignments, and more recently, quantum-classical hybrid solvers.
Over the past decade I've worked across the full stack of applied OR: formulating MINLP and MIP models, integrating machine learning into prescriptive pipelines, deploying on SAS Viya and cloud environments, and communicating results to C-suite stakeholders in ways that drive real decisions. Along the way, I've co-invented three US patents across different areas — from ML-integrated manufacturing optimization to quantum-classical hybrid methods — and a long-running Fortune 50 consumer goods partnership earned an Edelman Prize semi-finalist nomination, one of the highest recognitions in Operations Research.
More recently, I've been building and growing teams. As a manager in SAS's Applied AI & Modeling group, I lead data scientists through the full product lifecycle — from ideation and experimentation to production deployment. I launched the company's first independent Strategic Supply Chain Optimization offering and co-led a quantum computing partnership with a Fortune 50 consumer goods company. What drives me is finding problems where rigorous optimization creates measurable impact — and making sure the solution actually reaches the people who need it.
LP, MIP, MINLP, network flows, stochastic & robust optimization, NP-hard combinatorial problems.
Embedding machine learning models into prescriptive analytics pipelines for production-grade decision systems.
Strategic supply chain planning, mixture optimization, production cost minimization under quality constraints.
Applying quantum computing to real-world NP-hard optimization problems, integrated with classical solvers.
Hiring, mentoring, and growing data science teams. Product roadmap ownership, Scrum delivery, executive communication.
Python, Pyomo, CPLEX, Gurobi, AMPL, GAMS, SAS Viya, PROC OPTMODEL, Git, CI/CD.
Led the development of SAS's first independent model offering — a recommendation system for strategic-level supply chain decision-making. Built the product from ideation through production launch, serving as development manager, project manager, and technical lead.
Multi-year Fortune 50 partnership: designed and maintained pooling-process MINLP engines across multiple production releases, delivering significant and sustained cost savings. Modernized the customer environment to cloud-native SAS Viya.
Co-led R&D initiative applying quantum-enabled methods to a real-world NP-hard business problem, integrating quantum annealing approaches with the SAS Optimization classical solver. Resulted in US Patent 12,373,720.
Designed a custom bus-driver-route assignment optimizer using network optimization and constraint programming techniques, resolving a critical operational gridlock. End-to-end delivery on SAS Viya 4 in a cross-team environment.
Built production cost minimization models under quality constraints, integrating ML-based KPI prediction into the MIP optimization framework for global manufacturing clients. Co-invented US Patent 11,055,639 for the ML-optimization integration method.