Usman Anwar
Also an ‘Open Philanthropy AI Fellow’
Advisor: David Krueger
Research on inverse constrained reinforcement learning, impact regularizers, and distribution generalization
Usman is a PhD student at the University of Cambridge. His research interests span Reinforcement Learning, Deep Learning and Cooperative AI. Usman’s goal in AI research is to develop useful, versatile and human-aligned AI systems that can learn from humans and each other. His research focuses on identifying the factors which make it difficult to develop human-aligned AI systems and developing techniques to work around these factors. In particular, he is interested in exploring ways through which rich human preferences and desires may be adaptively communicated to the AI agents, especially in complex scenarios such as multi-agent planning and time-varying preferences with the ultimate goal of both broadening the scope of tasks that AI agents can undertake as well as making the AI agents more aligned and trustworthy. For publications and other details, please visit https://uzman-anwar.github.io