Carmen Amo Alonso leads the Control and Intelligent Systems Lab as an Assistant Professor and the Lucy Hsu Faculty Fellow in EECS at UC Berkeley. Her research lies at the intersection of control theory, optimization, and artificial intelligence (AI) with a focus on the principled design of reliable and controllable AI technologies. Carmen's work seeks to adapt mathematical principles from control theory, traditionally used to ensure safety and predictability in engineered systems, to understand, control, and ultimately improve the behavior of AI systems, including generative models for language applications and embodied systems. The impact of her research has led to various collaborations with industry, including Google and Apple; and she was an inaugural AGI Governance Fellow at the Johns Hopkins School of Government and Policy.
Prior to joining UC Berkeley, she was a Schmidt Science Fellow at Stanford University, where she was named an Emerson Consequential Scholar and an Impacts Lab Fellow for the potential of her research to positively impact society. Before Stanford, she was a Fellow at the Artificial Intelligence Center at ETH Zurich. Carmen earned her Ph.D. in Control and Dynamical Systems from Caltech in 2023. Her thesis was awarded the Milton and Francis Clauser Doctoral Prize, which recognizes the best Ph.D. dissertation of the year across all disciplines at Caltech. During her Ph.D., her research received two IEEE best paper awards, was partially funded by Amazon and D. E. Shaw fellowships, and earned her three Rising Star titles (EECS, Cyber-Physical Systems, and Brain and Cognitive Sciences). She holds an M.Sc. in Space Engineering from Caltech (2017) and a B.Sc. in Aerospace Engineering from the Technical University of Madrid (2016).
Besides her research collaborations across academia and industry, Carmen is committed to education for all. As a member of Clubes de Ciencia, she travels to Latin America in the summer to teach underserved students.