Bridging the gap between AI research and real-world applications


My name is Juan José (Juanjo) and I'm a Research Scientist at InstaDeep. I am passionate about the intersection between system design in Machine Learning and the challenges of real-world problems. My current research threads focus on Automated Reinforcement Learning (AutoRL) and Large Language Models for biology applications. In 2023, I completed my PhD at MIT. I worked on improving the robustness of RL-based autonomous systems in real-world environments. My contributions range from automating the design of RL algorithms for multiple real-world-oriented goals to proposing specific models to tackle key practical applications in resource management for satellite communications, molecular design, and traffic signal control.

In 2017, under the CFIS program, I received two BSc degrees in Telecommunications Engineering and Industrial Engineering from UPC, in Spain. I carried out my undergraduate Thesis as a visiting student in the System Architecture Lab at MIT; I designed a large-scale Internet of Things network by optimizing its architecture leveraging systems engineering. I previously worked at Arcvi and at Barcelona Supercomputing Center. In 2019, I was awarded a La Caixa fellowship with full funding for my PhD studies. In 2020, I received my Master's degree in Aerospace Engineering from MIT. My Master's Thesis focused on the study of autonomous resource management systems for communication satellites and the use of various AI algorithms to support their operation. Later that year, I interned at Novartis AI Innovation Lab. In 2021, I interned at Google Brain, where I remained as a Student Researcher throughout the rest of the year and part of 2022.

Research interests