Operations Research & AI for Large-Scale Decision Systems
I'm a Senior Research Scientist at Amazon, where I develop optimization and forecasting systems for last-mile logistics — routing, demand planning, and warehouse operations at the scale of tens of millions of packages daily.
My research combines mathematical optimization, machine learning, and simulation to solve planning problems where decisions are coupled, uncertain, and time-sensitive. Previously, I led operations research at BNSF Railway, contributing to work recognized with the INFORMS Prize (2018).
PhD Supply Chain and Operations Management, UT Austin · 10 USPTO Patents · Published in Production and Operations Management
Recent Highlights
A snapshot of recent talks, patents, publications, and recognition
New patent filed — Multi-agent AI Systems
Reviewer for ITOR and ACL TrustNLP 2026
CSS 2026 — Oral + Poster; INFORMS 2026 Session Chair
2 USPTO Patents Granted — Consensus Planning & Storage Pod Design
Session Chair, POMS — Data Science, Analytics, and AI
Published in Production and Operations Management — Subscription Pricing
Invited Panelist & Tutorial Speaker — INFORMS, POMS
Session Chair & Presenter, INFORMS — Warehouse Operations
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
What I Speak About
Real stories from building optimization and AI systems at global scale
Demand Forecasting at Scale
Your forecasting system works in the lab. But what happens when you need a million models running concurrently across six countries, handling 300% volume surges during peak? This talk covers the architectural, statistical, and organizational challenges of forecasting at massive scale.
For: Data science leaders, supply chain executives, ML engineers
Last Mile Logistics Optimization
The last mile is the most expensive, most complex, and most visible part of the supply chain. This talk explores the science behind optimizing package delivery across 1,000+ stations — from carrier assignment to route densification to cost estimation — and why the hardest problems are often the ones customers never see.
For: Operations leaders, logistics executives, OR practitioners
Bridging OR & ML in Production Systems
The most impactful industry systems don't choose between operations research and machine learning — they combine both. This talk draws on real examples to show how to design architectures that leverage optimization and learning together, and why the gap between these disciplines costs companies billions.
For: Technical leaders, research scientists, engineering managers
Let's Work Together
Whether you're looking for a speaker, a research collaborator, or just want to connect — I'd love to hear from you.
Get in Touch