Welcome to my website!

My name is Carmen Amo Alonso and I am a Schmidt Science Fellow. I am affiliated with the Computer Science Department at Stanford, where I work with Chris Ré at the intersection of Control Theory and Deep Learning, particularly for language applications. To learn more about my research, head over to the Research tab!

Before joining Stanford, I spent a year as a postdoctoral fellow at the AI Center at ETH Zurich. I obtained a Ph.D. in Control and Dynamical Systems from the Department of Computing and Mathematical Sciences at Caltech, where I worked under the advice of Prof. John Doyle. My thesis was awarded with the Milton and Francis Clauser Doctoral Prize, which recognizes the best Ph.D. dissertation of the academic year across all disciplines at Caltech. During my Ph.D., I was awarded a D. E. Shaw Exploration Fellowship and an Amazon AI4Science Fellowship. My research has been recognized with the 2024 Best Paper Award at IEEE Transactions on Control of Network Systems, and the 2022 Best Student Paper Award at the IEEE International Conference on Control & Automation. I also had the honor to have been invited to both EECS Rising Stars and CPS Rising Stars workshops, and I am proud to have been named a fellow of the LEAP Alliance, whose goal is to increase diversity across the field of computing. Prior to starting my Ph.D., I received a B.Sc. in Aerospace Engineering from the Technical University of Madrid in 2016 and a M.Sc. in Space Engineering from Caltech in 2017.

I am very proud to be a member of the academic community. I am a strong believer that access to education is a fundamental right, and that an educated society is a more equitable and inclusive one. For this reason, I strive to make my research accesible to anyone who is interested in learning about it. I upload presentations of my papers as Youtube videos for those who prefer watching over reading, and I always make myself available to discuss research: feel free to reach out! I love to participate in teaching and outreach opportunities as well. I also believe that in order for research to be more accessible, it must be easily reproducible. You can find all the code needed to reproduce my papers linked in my Publications tab.