MSc
Biosketch
Rafael Zimmer is a computer scientist with a strong background in high-performance computing and quantitative finance. He earned his Bachelor’s degree from USP, Brazil, and is currently pursuing a Master’s at the Technical University of Munich. His previous work includes developing deep reinforcement learning architectures for high-frequency trading, building limit order book simulators, and working with on-policy algorithms for decision making in market microstructure. Professionally, he has designed quantitative models and trading frameworks at Clave Capital, and RoboBanker, and worked as a researcher for CNPq and FAPESP, in Brazil.
relAI Research
Rafael is interested in the intersection of decision making processes and Quantitative Finance, with a focus on designing high-performance algorithms for algorithmic decision-making in high-frequency markets. He is particularly interested in distributed and parallelized reinforcement learning systems, enabling efficient exploration and learning in complex, stochastic financial environments, as well as the theory of stochastic analysis to improve the performance of data-based algorithms using techniques of scientific computing.