Lawrence Thul, PhD

I hold a PhD in Electrical Engineering from Princeton University, where I specialized in stochastic optimization under Professor Warren B. Powell at the CASTLE Lab (Computational Stochastic Optimization and Learning Lab). My doctoral research focused on multi-agent sequential decision-making for complex physical systems under uncertainty, developing collaborative strategies for agents with complementary skillsets to manage shared objectives in highly dynamic, stochastic environments.

Today, I thrive at the intersection of simulation, AI, and machine learning applied to high-stakes physical problems. I excel at translating sophisticated research into practical, scalable digital twins and commercial simulators that enable complex decisions under uncertainty.

Following my PhD, I joined Optimal Dynamics, a startup founded by Warren and Daniel Powell to commercialize CASTLE Lab’s advanced network optimization algorithms for the trucking industry. Serving as the Director of Artificial Intelligence, I led the Statistics and Machine Learning group through the company’s rapid scaling from its early stages to a Series C organization raising approximately $100M from investors like Koch Disruptive Technologies and Bessemer Venture Partners.

During my tenure, I operated across the full technical and product stack to bridge the gap between deep research and commercial scale. I managed the end-to-end development of proprietary ML models and architected the large-scale ETL pipelines that powered our high-stakes simulation engines. As the technical lead for flagship products including our Bidding, Trailer, and GenAI offerings, I designed systems that managed over $1B in freight recommendations. Additionally, I spearheaded our Agentic AI initiative by integrating complex multi-agent orchestrations into every core product to automate customer calibration and deployment.

Having served in leadership through various stages of growth, I have a unique blend of deep technical expertise and seasoned startup operations. I have experience bridging the gap between advanced AI and optimization research and commercial applications, with a proven track record of leading hybrid engineering and AI teams through the rigors of production-level builds. In March 2026, I decided to move on to pursue new horizons.