Patricia holds a Ph.D. from UC Berkeley. Her thesis “Learning and Control Systems for the Integration of Renewable Energy into Grids of the Future” (PhD ’20) was co-advised by Daniel Kammen and Claire Tomlin. She obtained an M.S. from the Energy and Resources Group, UC Berkeley (2016). She graduated with highest honors as an Industrial and Electrical engineer from Pontificia Universidad Católica of Chile.
She is an NSF GRFP fellow, Siebel Scholar in Energy, Rising Star in EECS, and has been awarded the UC Berkeley GOP, and the Outstanding Graduate Student Instructor Award (for teaching Convex Optimization with professors Laurent El Ghaoui and Alex Bayen).
Her work focuses on high penetration of renewable energy using optimization, control theory and machine learning. Patricia co-developed a stochastic power system expansion model to study the Western North America’s grid under climate change uncertainty. She also works on frequency regulation with low inertia. Her collaborations have included the California Energy Commission, LBNL, NREL, E3, and NRDC. She served as Best Paper Session Judge for the session “Power System Stability, Phasor Measurements, Protection, and Control” at the 2019 IEEE Power & Energy Society General Meeting (PESGM). Patricia also serves as an IEEE reviewer for the Transactions on Power Systems journal, Conference on Decision and Control, American Control Conference, and PESGM.