Jaques’s research focuses on social reinforcement learning in multi-agent and human-AI interactions. Her interest in AI learning and collaboration extends into developing multi-training algorithms that create automatic curriculum to help AI learn from each other, and improving mechanisms that allow AI to learn from human partners. Jaques has received numerous awards including Best Demo at NeurIPS, Best of Collection in the IEEE Transactions on Affective Computing, and Best Paper at the NeurIPS workshops on ML for Healthcare and Cooperate AI. Her work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine and on CBC Radio.