Science for Decision-Making at Scale
Optimization, forecasting, and AI for complex interconnected systems.
I build systems that make high-stakes decisions under uncertainty — routing, demand planning, capacity allocation, and multi-agent coordination. The common thread is combining mathematical optimization and machine learning to solve planning problems where decisions are coupled, stochastic, and time-sensitive.
At Amazon, this powers the optimization and forecasting behind last-mile delivery across a global network. Previously, I led operations research at BNSF Railway, contributing to work recognized with the INFORMS Prize (2018). More recently, my research has expanded into agentic AI systems and coordination in multi-agent environments.
PhD Supply Chain and Operations Management, UT Austin · 2 U.S. patents granted, 8 pending · Published in Production and Operations Management
Recent Highlights
A snapshot of recent talks, patents, publications, and recognition
CSS 2026 — Oral (OR & Optimization) + Poster (Machine Learning)
Patent Filed — Multi-agent coordination systems
2 USPTO Patents Granted — Consensus Planning & Storage Pod Design
Published in Production and Operations Management — Subscription Pricing
Invited Panelist & Tutorial Speaker — INFORMS, POMS
Top 2 OR Paper Award — Amazon Machine Learning Conference
Systems That Ship
Production systems I've built or led — deployed science at scale
Package Selection Systems
Deployed across Amazon's US delivery network
A family of optimization systems that determine which packages to assign to which delivery stations and routes, maximizing network density while balancing cost, speed, and capacity constraints across the entire fulfillment-to-doorstep pipeline.
Millions of packages daily across 1,000+ stations
Demand Forecasting Systems
6 regions, 4 time horizons, 10M+ weekly forecasts
A large-scale forecasting platform managing concurrent prediction models across multiple global regions, combining classical statistical, deep learning, and tree-based ensemble methods with hierarchical reconciliation to drive capacity and staffing decisions.
1M+ concurrent models across 6 global regions
Capacity Planning & Supply Chain Coordination
End-to-end supply chain, multiple planning horizons
Systems that allocate delivery capacity under demand uncertainty and coordinate across supply chain stakeholders — from warehouse labor planning to network-wide resource balancing — ensuring service targets are met even as conditions shift.
Multi-region network coordination under demand uncertainty
Selected Research
Publications, patents, and workshop papers with authoritative links
Production and Operations Management, 33(4), 943–961, 2024
MILETS 2026 / KDD Workshop — Oral Presentation · Amazon Science
COLM 2026 Workshop on Agent Behavior
US 12,499,399 B1 · Granted 2025 · Google Patents
US 12,504,281 B1 · Granted 2025 · Google Patents
Speaking
I speak on demand forecasting at scale, last-mile logistics optimization, the intersection of OR and ML in production systems, and the path from research to deployed decision systems.
Research, Speaking & Collaboration
Whether it's a research collaboration, a speaking invitation, or just a good conversation about OR and AI — I'd welcome the connection.
Get in Touch