Ziming Liu

Hi! I’m Ziming Liu (刘子鸣). I will join the College of AI at Tsinghua University as a tenure-track Assistant Professor, starting 2026 Fall. I’m looking to hire two PhD students who will start in Fall 2027 at Tsinghua University.

My lab is called LIU lab, which stands for Learning, Intelligence, and Universe.

• Learning is our mindset –the first principle of research is curiosity-driven learning, not publication
• Intelligence is our goal – understanding and building intelligent systems
• Universe is our methodology – we study AI as if we’re studying our Universe, i.e., breaking & building things from first principles, balancing between experiments and theory.

I did my postdoc in AI + Neuroscience at Stanford with Andreas Tolias. I did my PhD in AI + Physics at MIT with Max Tegmark. Before that, I obtained my B.S. in physics from Peking University in 2020. Get my CV here.

I’m currently devoted to two related things:

• “Physics of AI” – using first principles to understand the structure (“Google map”) of AI research/idea/design space.
• “AI for AI” – building an AI agent that can intelligently navigate through the map of ideas, like human AI researchers.

I believe both the AI agent and the “physics of AI” knowledge base will become key ingredients of a safe and efficient AGI, which naturally supports curiosity-driven continual learning.

Over a longer time scale, my research interests generally lie in the intersection of AI and Science:

• Science of AI: Understanding AI using science. I’m interested in understanding intriguing network phenomena (e.g., grokking, neural scaling laws).
• Science for AI: Advancing AI using science. When the scaling of the current paradigm plateaus, it is time to focus back on fundamental science (for AI).
• AI for Science: Advancing science using AI. I’m excited about inventing accurate/interpretable AI4Science models and curiosity-driven AI scientists.

Consider reading these Quanta articles if you want to get some taste of my research:

• Science of AI (Grokking): How Do Machines ‘Grok’ Data?
• Science for AI (Poisson Flow): The Physical Process That Powers a New Type of Generative AI
• AI for Science (KAN): Novel Architecture Makes Neural Networks More Understandable