Welcome to my website!
My name is Carmen and I am an (incoming 2027) Assistant Professor and the Lucy Hsu Faculty Fellow in the Electrical Engineering and Computer Sciences Department at UC Berkeley, where I direct the Control and Intelligent Systems Lab.
Before moving to Berkeley, I spent time at Stanford as a Schmidt Science Fellow and at the AI Center at ETH Zurich as a postdoctoral fellow. I obtained a Ph.D. from Caltech in Control and Dynamical Systems. My research has been awarded the best Ph.D. dissertation of the year at Caltech2023 Milton and Francis Clauser Doctoral Prize and two IEEE best paper awards2024 Best Paper Award at IEEE Transactions on Control of Network Systems
2022 Best Student Paper Award at the IEEE International Conference on Control & Automation. My work has also been recognized for its interdisciplinary contributions2025 Rising Star in Brain and Cognitive Science (MIT)
2022 Rising Star in Electrical Engineering and Computer Science (UT Austin)
2022 Rising Star in Cyber-Physical Systems (U Virginia) as well as its societal impact2026 Johns Hopkins AGI Governance Fellow
2025 Emerson Consequential Scholar
2025 Stanford Impact Labs Fellow.
Official biography for talks here
Research
My research sits at the intersection of control theory?The mathematical study of how to make complex systems behave safely and reliably. and artificial intelligence. With a background in aerospace engineering, I have seen firsthand the power of control systems to ensure safety and enhance performance across complex engineering systems. For example, once unimaginable, flight has become safe and routine today. My vision is that, similarly, control theory holds great power to ensure safety and enhance performance in the new class of intelligent systems we are witnessing with AI.
In the work I direct at the Control and Intelligent Systems Lab at UC Berkeley, we design Controllable AI: intelligent systems that we can principled design and reliably control, so they do what we want them to do, and never do what we don't want them to do. We pursue this by adapting mathematical principles from control theory to understand, control, and ultimately improve the behavior of AI systems, ranging from language models to robotic systems. If you want to learn more about this work, visit our website and connect with us here!
Teaching
I believe in using education to uplift individuals who can, in turn, serve their communities. To pursue this goal, I focus on building a learning community that goes beyond individual achievement, grounded in growth and shared purpose. I always ground my teaching and mentoring in the principles below.
Core Teaching Principles
- Creating a Collaborative Learning Environment. I strive to build a sense of community and belonging, where students feel valued and engaged in a shared mission.
- Establishing Clear Learning Goals. I articulate expectations and learning objectives to guide learning and ensure all students receive the same information and opportunities.
- Grounding Foundations in Applications. I create an environment where students do not see themselves as knowledge consumers but as future contributors to their fields.
- Assessing Learning Comprehensively. I evaluate both technical mastery and conceptual understanding while attending to students' individual needs. I put special emphasis on evaluating students' capacity to apply their knowledge to solve problems and communicate it to others.
- Championing Knowledge-Sharing. In both classroom teaching and individual mentoring, I emphasize knowledge-sharing as a core educational value.
I have participated in teaching across different institutions. At UC Berkeley, I will be teaching courses across control theory, machine learning, and artificial intelligence. Stay tuned for details soon!
Courses I have taught
- Distributed Model Predictive Control - Co-Instructor,
- AI Center Projects in Machine Learning Research - Co-Instructor,
- Robust Control Theory (CDS 231) - Teaching Assistant,
- Optimal Control and Estimation (CDS 112) - Teaching Assistant,
- Network Control Systems (CDS 141) - Teaching Assistant,
- Introductory Methods of Applied Mathematics (ACM 95/100) - Head of Teaching Assistants,
- Introduction to Probability Models (ACM 116) - Head of Teaching Assistants,
Service
Outreach Activities
I am very fortunate to have received an education, and I am committed to passing this gift on to others. I am very invested in teaching and outreach activities, especially in underserved communities.
Clubes de Ciencia
I volunteer for Clubes de Ciencia, a non-profit that organizes summer intensive courses for high school and college students in Latin America. I have had the privilege to travel to Mexico (Merida, 2023) and Peru (Lima, 2026), where I designed and taught a course about the capabilities and intrinsic risks of AI. During our time together, students grasped the importance of knowledge-sharing and created online resources (Spanish only: Mexico, Peru) to share their learnings!
I am also regularly invited to visit high schools, where I share my personal experience and help motivate the students to pursue a scientific career.
Science Policy
I believe that knowledge belongs to all of us, and it should be used to serve society as a whole. For this reason, I engage in science policy in various ways. I am an AGI Governance Fellow at the Johns Hopkins School of Government and Policy, and a member of the Future of Life Institute, a global community promoting responsible AI. I also served as the Engagement and Education Officer for the Stanford Science Policy Group.