Alexander Ku

I'm a PhD student at Princeton University, where I'm advised by Tom Griffiths and Jon Cohen. I'm also a research scientist at Google DeepMind.

My research explores how human intelligence reflects the structure and statistics of natural and artificial curricula, and how this insight can inform the development of artificial intelligence. While neuroscience and machine learning often focus on identifying architectural inductive biases that influence learning, my goal is to identify algorithmic inductive biases that are shaped by data. I'm particularly interested in understanding the curricular factors that enable flexible generalization, reasoning, and abstraction in neural networks.

Previously, I've worked on grounded language learning, text-to-image generation, and genomics.

Email / Google Scholar / GitHub / Twitter / CV