Nico Christianson

Bio: Nico Christianson works at the interface of algorithms, AI, and energy. His research focuses on developing new, theoretically-grounded algorithms to enable safely deploying modern AI and machine learning tools to real-world energy and sustainability challenges such as power grid optimization, sustainable computing, and beyond. Nico completed his PhD in Computing and Mathematical Sciences at Caltech, where he was advised by Adam Wierman and Steven Low and supported by an NSF Graduate Research Fellowship and a PIMCO Data Science Fellowship. During his PhD, Nico collaborated with a number of industry partners including Microsoft Research, Beyond Limits, and Amazon. Prior to Caltech, he received an AB in Applied Mathematics at Harvard College.

Postdoctoral research project: Algorithmic Foundations of Safe and Reliable AI in Sustainable Energy Systems. Climate change demands a rapid transformation in how we plan and operate energy systems to achieve decarbonization in the face of growing uncertainties and risks. While AI and machine learning offer enormous potential to handle these uncertainties, current AI tools lack the necessary guarantees to ensure safe and reliable deployment in real-world settings. Nico’s research at Stanford will address this challenge and develop new approaches for reliable AI-driven decision-making in sustainable energy systems. In particular, his work will focus on the broad themes of AI/ML for large-scale power grid optimization, uncertainty quantification for risk management, and integrating sustainability and equity objectives into operations.

Research focus:  Electric Grid Modernization

Advisors:  Ellen Vitercik - Management Science & Engineering  |  Ram Rajagopal - Civil and Environmental Engineering