Edgar Hildebrandt Rojo | Applied ML & Algorithmic Systems

Applied ML & Decision Systems

I build machine learning systems that evaluate and optimize decisions under uncertainty.

Right now, most of my work sits at the intersection of reinforcement learning, distributed simulation, and LLM evaluation. I don’t just focus on training models; I spend my time engineering the systems around the model—the environments, objective functions, and evaluation frameworks that dictate whether an algorithm actually works in the real world.

At Johns Hopkins Applied Physics Laboratory, I build large-scale simulation and ML pipelines that drive operational decisions for the U.S. Navy and Coast Guard.

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My core focus includes:

Distributed Simulation: Architecting Monte Carlo pipelines scaling to 1,000,000+ episodes on HPC clusters to evaluate policy trade-offs under stochastic conditions.
Reinforcement Learning: Building closed-loop RL environments that integrate real-time system states for constrained resource allocation.
LLM Evaluation: Developing domain-specific evaluation frameworks and schema compliance protocols to rigorously measure hallucination and logical robustness.

An Interdisciplinary Approach

I am fundamentally an interdisciplinary problem solver. My academic background—a triple major in EECS, Applied Math, and Economics from UC Berkeley (Go Bears! 🐻), followed by an M.S. in AI from Johns Hopkins—gives me a unique lens on machine learning.

I don’t just focus on pure theory, and I don’t just write code for deployment. I use my foundation in econometrics and applied mathematics to bridge the two. This allows me to define measurable objectives, model complex trade-offs, and ensure the systems I build actually solve the underlying structural problems.

While I currently apply this toolkit to defense and autonomy programs, the core mathematics of my work are entirely domain-agnostic. Whether I am modeling physical logistics or designing data-driven product architectures, my goal is the same: integrating rigorous, multi-disciplinary math with software engineering to build high-agency systems that drive real-world impact.

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