Nathan B
- Research Program Mentor
PhD candidate at Princeton University
Expertise
Chemical Engineering, Electrical Engineering, Mechanical Engineering, Optimization. Operations Research, Environmental Engineering, Applied Math
Bio
Academic Passion: I am a third-year Ph.D. candidate in Chemical Engineering at Princeton University, where I focus on developing advanced modeling and solution techniques for thermal power plant operations and grid-wide energy systems optimization. My research centers on enhancing operational flexibility during startup and shutdown dynamics, which has significant implications for reducing costs and improving efficiency in modern energy grids. This work builds on my strong foundation in optimization theory, high-performance computing, and molecular dynamics, combining mathematical rigor with real-world applications that can drive meaningful change in how we approach energy systems. Personal Interests and Community: Beyond my academic pursuits, I find deep fulfillment in teaching and mentoring, having instructed courses ranging from mathematical modeling fundamentals to advanced engineering topics for both traditional and non-traditional students. As a husband and father, I'm passionate about creating balance between intellectual growth and family life, believing that meaningful work and personal fulfillment enhance each other. I enjoy staying active through cycling, exploring culinary adventures in the kitchen, and continuously learning new skills—whether that's mastering a new programming language or perfecting a homemade pizza recipe. This commitment to lifelong learning and community engagement reflects my belief that success comes from pursuing rigorous work while prioritizing health, relationships, and the simple joy of discovery.Project ideas
Simple, Actionable Policies From Noisy, Uncertain Data
We live in an exciting time with AI revolutionizing the world around us. However, the Achilles heel of mainstream AI is that it's what many call a "black box" meaning that no-one knows exactly why or how it outputs the things that it does. But what most people don't know is that there's another way; a way that is perfectly readable, understandable, an mathematically proven to get the best results: Mathematical Optimization. There are loads of directions you could go with this. But one idea is this: When trying to determine an investment strategy (whether for your self planning for retirement, or entire nations trying to invest in climate-positive technologies) there is a lot of uncertainty. Using mathematical optimization (particularly, a sub-field called "stochastic programming") we can harness that uncertainty to still come up with clear, easily understood policies that guide how to make decisions. In this project, we'd explore how to mathematically formulate those policies, collect and generate data, and perform the analysis required to determine the best policies to adopt.