Where are you from, and what is your background?
I’m from Buenos Aires, Argentina: the true city that never sleeps! I earned my Licenciatura, a six-year degree equivalent to a combined Bachelor’s and Master’s, in Computer Science from the University of Buenos Aires (UBA). At UBA, the program has a strong emphasis on theoretical computer science, mathematics, and logic. My thesis focused on Belief Revision, an area within Knowledge Representation and Reasoning, a branch of Symbolic AI, that deals with updating knowledge bases while maintaining consistency and preserving as much information as possible. After graduating, I worked at Safe Intelligence, an Imperial spin-off, carrying out a project on the formal verification of object detection models for safety-critical applications.
What do you do in your spare time?
Having a good balance between work and social life is key for me. When I’m not at my desk, you can find me hanging out with friends, trying out new food, going out dancing, listening to live music and visiting art galleries. I really enjoy meeting new people, as well as just wandering around the city: even after 24 years living there, Buenos Aires had always made me feel like a tourist.
What influenced you to do a PhD?
As I was completing my Licenciatura, I realized there was still so much more for me to learn in the field. My interests grew particularly around the limitations of modern machine learning methods, especially in terms of safety, explainability, and performance in complex tasks requiring reasoning; and how these weaknesses can be overcome by integrating deep learning with symbolic AI. Plus, I’ve long wanted to have the experience of living abroad, and a PhD seemed like a great opportunity that met both my objectives.
What are your research interests?
My research focuses on developing frameworks for new AI technologies that ensure compliance with safety requirements and business rules. I am currently working on establishing a control layer for agentic systems. This involves determining how to provide an agent with sufficient information about an evolving operational environment, as well as deciding which tools should be made available to it. In this way, LLM-driven systems can remain reliable, compliant, and effective over long time horizons.
More broadly, my research aims to combine the scalability of neural models with the reliability and formal guarantees of symbolic reasoning, with the goal of building robust and trustworthy decision-making systems.