Our computer science and neuroscience researchers work together from the beginning to design the experiments and the computational platforms and models used to analyze them. We also train the next generation of scientists at the intersection of computer science and neuroscience through fellowships for postdoctoral fellows and graduate students, and foster new collaborations and insights by hosting workshops and conferences that bring together experts from across neuroscience, computing, and related fields.

Research in machine learning and computer science more generally approached the topic of resilience under the name of robustness and error-tolerance. We strive to design machine learning algorithms which are robust when encountering out of distribution inputs which the algorithm has not been trained on and at the extreme even be robust to adversarial data. Similarly, in engineered communication and computation devices which are in charge of much of our infrastructure today we must ensure that our system encompass sufficient redundance so to be tolerant to errors. Error tolerance for quantum devices is likely the most important challenge ahead in order to truly utilize their power in computation and simulation. The research on robustness and error tolerance will be conducted in the Simons Institute for theory of computing organized in the form of a research pod on resilience.