Photo Email: lisalee [at] google [dot] com


Lisa Lee is a Senior Research Scientist at Google DeepMind. She completed her PhD in Machine Learning at Carnegie Mellon University, advised by Ruslan Salakhutdinov and Eric Xing. She graduated summa cum laude with an A.B. in Mathematics from Princeton University, where she was advised by Sanjeev Arora.

Lisa studied cello under Clara Kim at the Juilliard School. She is an alum of the New York Youth Symphony, the Princeton University Orchestra, and the NYYS Chamber Music Program, among others.

Research Interests

I am interested in building AI agents that can learn and adapt like humans and animals do, with the following desiderata: It perceives the physical world and acts according to its understanding of the world. It competes, cooperates, and communicates with other “living” entities. It is born with inductive biases accrued from billions of years of evolution. It keeps a memory of the past, and anticipates the future using learned biases. It performs abstract reasoning to plan ahead, learn higher-order skills, and learn concepts beyond the physical world. It continually adapts its skills and knowledge representation for better generalization. It is intrinsically motivated by curiosity, empowerment, and the need to be in control over itself and the environment.

I am most excited about bringing computer bits to “life”. When I was little, I felt an attachment to digital pets which are just binary bits in the computer; a snowman that I tried to save in the fridge one spring; and other non-living, not-necessarily-physical things. My childhood dream was to make Pokemon creatures real by building intelligent Pokemon robots. I still feel driven by the same childhood yearning.

Keywords: Deep reinforcement learning, intrinsic motivation, learning to explore, continual learning, hierarchical planning, multi-agent learning


I genuinely enjoy teaching and advising students. My teaching style is inspired by my past instructors Claudiu Raicu and Robert Dondero from Princeton University, who helped reinforce my self-confidence and resilience in tackling progressively more challenging problems.

I was twice awarded TA of the Year for Deep Reinforcement Learning (2018) & Probabilistic Graphical Models (2019).

Conference & Workshop Committees