Ana Larrañaga
Acting Assistant Professor · Mechanical Engineering
- Machine learning and data science for modeling of complex multiphysics and multiscale systems
- Model reduction, sparse sensing, physics-based simulations, optimization
- Expertise in fluid dynamics, thermodynamics, heat transfer, and combustion
I’m currently an Acting Assistant Professor in the Department of Mechanical Engineering at the University of Washington. Before that, in 2025, I was a postdoctoral scholar at the AI Institute in Dynamic Systems, working with Professor Steve Brunton on data-driven modeling and control of complex multiphysical systems. My research centers on data-driven model discovery and using machine learning to solve messy, expensive optimization problems that engineering loves to throw at us.
I earned a bachelor’s in Energy Engineering from the University of Vigo (2019) and a master’s in Industrial Mathematics (2021) from a consortium of five Spanish institutions — UVigo (Vigo), UDC (A Coruña), USC (Santiago de Compostela), UPM (Madrid), and UC3M (Madrid) — which is a lot of acronyms for one degree. In 2024, I completed my Ph.D. in the Energy Technology Group (GTE) at the University of Vigo, developing data-driven surrogate models for optimizing enhanced heat transfer applications.
I’ve never turned down an excuse to work with researchers abroad. As an undergraduate, I spent a full year at the Norwegian University of Science and Technology under Armin Hafner and Yosr Allouche, where I helped develop a cold thermal energy storage system integrated into an R744 supermarket refrigeration system. Later, during my Ph.D., I spent three months at the University of Melbourne with Richard Sandberg’s group, building surrogate models for microfinned surfaces using the group’s in-house evolutionary-algorithm framework, EVE3. The results of that collaboration live in my publications — feel free to have a look!
Highlighted Research
The Fuel Optimizer
An inverse-design framework for sustainable aviation fuels that identifies optimal fuel formulations to break the NOₓ–CO trade-off and minimize pollutant emissions, pairing surrogate modeling with genetic-algorithm optimization.
New preprint · 2026How Low Can You Go?
An active-learning strategy for discovering governing equations in the ultra-low-data limit, using E-SINDy uncertainty to sample only the most informative data for both ODEs and PDEs.
News
March 2026
Featured in Faro de VigoReally grateful for the opportunity to be interviewed by Faro de Vigo, where I reflect on my journey in academia so far. It's a little outside my comfort zone, but I hope sharing my experience can inspire others along the way!
Awards and Recognitions
- BARC Research Excellence Award as a senior member of the Data-Driven Engineering Enabling R&D Team Boeing Advanced Research Collaboration, University of Washington — 2026
- Second Runner-up, National Awards for University Education Graduates (Academic year 2018–2019) Ministry of Science, Innovation, and Universities, Spain — 2025
- Runner-up, Extraordinary Engineering Doctoral Award University of Vigo — 2025
- Recognition for Young Researchers of the EEI Faculty of Industrial Engineering — 2021
- Academic Excellence Award for students beginning doctoral studies (2020/2021) University of Vigo — 2020
- Extraordinary End-of-Degree Award University of Vigo — 2019
- End-of-Degree Award Xunta de Galicia — 2019
- Academic Excellence Award for undergraduate students with the best grades in the Galician University System (2017/2018) Xunta de Galicia — 2018