Ruben has a background in Mechatronics and Mechanical Engineering and has turned to Artificial Intelligence (AI) where his main interest lies in Machine Learning (ML) research, particular in Reinforcement Learning (RL) with a focus on human-inspired learning approaches that rely on reuse of previously acquired knowledge.
His long-term research interest lies in successfully applying RL techniques to real-world challenges to accelerate and improve decision-making, autonomously or as a support tool for humans, preferably for applications in the energy and smart mobility domain.
Ruben is very pro-active and pushes himself to reach ambitious goals.
To this effect, he highly values teamwork and cooperation and welcomes constructive discussion and exchange.
His most valuable contributions to the workplace include his high energy, the drive to get results, and the ability to inspire others.
Ruben loves to support the research community and routinely takes on leadership roles to accept responsibilities and create forward momentum for a group or cause.
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He holds a master degree in Mechanical Engineering in the area of controlling mechanical systems from the São Paulo State University (UNESP), Brazil, and a Diplom-Ingenieur degree in Mechatronics in the area of sensors and robotics from the Karlsruhe Institute of Technology (KIT), Germany.
Eventually, Ruben received his Ph.D. in Computer Engineering in the area of ML at the Escola Politécnica of the University of São Paulo (USP), Brazil, one of the leading universities in Latin America.
During his Ph.D., Ruben’s efforts were recognized through various prestigious awards, like the Google Research Award for Latin-America, the Upsilon Pi Epsilon Honor Society Award for academic excellence, an invitation to the Heidelberg Laureate Forum as Outstanding Young Researcher, various travel grants to conferences and other events, as well as best paper, best student poster, and distinguished work awards for his contributions at international events.
Apart from his academic experiences, Ruben has acquired years of professional experiences before and during his studies while working in the technology and energy sector, as well as in the organization of international ML conferences.
Most notably, he was invited to for a highly competitive summer internship at Microsoft where he worked on the interface between ML and software development and build an RL agent that would learn to navigate Microsoft apps and perform simple tasks.
He was also able to build a great international network in the applied ML community as the Lead Organizer of the Predictive APIs (PAPIs) conference series which he organized in London (UK), Boston (USA), and São Paulo (Brazil) with increassing success.
More recently, Ruben was appointed to a Postdoctoral Researcher position at the Lawrence Livermore National Laboratory (LLNL) (USA) where he also acted as the Chair of the Lawrence Livermore Postdoc Association (LLPA) and represented the Lab’s postdocs in the Institutional Postdoc Program Board (IPPB).
After converting to a full Staff position at LLNL, he is now working as a Staff Researcher ML, continuing research on a variety of RL projects to develop methods for collaborative autonomy in multi-agent systems, interpretable RL, and real-world applications for ML approaches.
In the past, Ruben also has actively build the ML community in São Paulo by organizing meetups and workshops with a strong emphasis on diversity and inclusion.
He is now continuing his community engagement efforts as elected Vice-Chair of the IEEE Computer Society Oakland/Eastbay/San Francisco chapter and a voting member on the IEEE Computer Society Artificial Intelligence Standards Committee (C/AISC).
Research Interests
Autonomous Agents
Artificial Intelligence
Reinforcement Learning
Deep Learning
Transfer Learning
Multiagent Learning
