My research develops computational methods and integrated software workflows for system-level design and assessment of aerospace systems, with a focus on aircraft design and alternative-energy propulsion. My group works at the intersection of computational systems design, multidisciplinary design analysis and optimization (MDAO), and model-based systems engineering (MBSE), with an emphasis on creating reusable, traceable, and decision-focused tools.
Methodologically, our work centers on MDAO for large, tightly coupled problems that are naturally mixed-variable: key architecture and topology decisions are discrete, while sizing and operational variables are continuous. We develop formulations and algorithms that make these problems tractable at scale, combining multi-fidelity modeling with surrogate modeling and machine-learning predictors to accelerate design space exploration, screening, and sensitivity analysis. A key theme is robust design and decision-making under uncertainty: rather than optimizing a single nominal best-case point, we represent uncertainty in model inputs, boundary conditions, and operating context and quantify how design choices propagate to performance, feasibility, and constraint satisfaction. This enables solutions that are not only high-performing but also stable across plausible variations in assumptions, environments, and use scenarios.
A second core thrust is digital engineering. We digitize and connect engineering and systems engineering workflows by building traceable digital threads that link requirements, architectures, assumptions, analysis models, and generated datasets. These threads are often implemented through SysML-based MBSE environments coupled to analysis and optimization, enabling consistent data exchange, reproducible trade studies, and improved design traceability across teams and tools. We also develop AI-augmented workflow automation to reduce manual handoffs and accelerate iteration, supporting engineers as they navigate architectures, models, and trade spaces while preserving traceability back to the underlying computations and decisions.
Please describe one or two of your most interesting projects.
One of my most impactful projects is my work on electrified aircraft propulsion under NASA’s Electrified Aircraft Propulsion (EAP) program. In this project, we developed computational design methods and open-source software (e.g., FAST) that enable system-level design and analysis of conventional and electrified aircraft. The key challenge is that propulsion architecture, energy storage, thermal management, and mission performance are tightly coupled. Our work enables rapid exploration of thousands of design configurations, allowing engineers to evaluate trade-offs between fuel burn, weight, energy use, and system feasibility early in the design process. These tools have been used in collaboration with industry and government partners, demonstrating direct impact on how next-generation aircraft concepts are evaluated.
What makes you excited about your data science and AI research?
What excites me most is the opportunity to fundamentally change how we explore and make decisions in complex engineering design. Many of the most important design problems are not limited by lack of models, but by our inability to navigate massive, coupled, and uncertain design spaces in a systematic way. Data-driven methods and machine learning allow us to move beyond evaluating a handful of expert-selected options toward discovering and reasoning over thousands or millions of possibilities.
I am particularly excited about using AI not just as a predictive tool, but as a collaborator in the design process, helping to structure problems, synthesize workflows, and guide trade-offs while maintaining traceability to underlying data and models. This opens the door to more scalable, reproducible, and decision-focused engineering, where we can quantify uncertainty, understand why a design works, and adapt quickly as requirements change.
More broadly, what motivates me is that these methods are not limited to aerospace. The same computational and AI-enabled approaches can be applied to energy systems, infrastructure, and other complex domains, enabling a new class of research and design problems that were previously out of reach.
What are 1-3 interesting facts about yourself?
Although my research is mostly computational, I’m very hands-on in my personal life. I enjoy DIY projects (from construction to woodworking) and building furniture when I have time.