The Machine Intelligence Lab (MIL) is dedicated to the theory and practice of broadly applicable intelligent systems. In particular, we focus on biologically-inspired cognitive models, including deep learning architectures for scalable perception engines and reinforcement-learning models for decision making under uncertainty.

Our studies emphasize qualitative and quantitative evaluation of system performance, robustness and scalability. To achieve the latter, we develop solutions that effectively exploit off-the-shelf as well as customized massively-parallel computing platforms.