Alexander Ku

I'm a PhD student at Princeton University (Psychology & Neuroscience), advised by Tom Griffiths and Jon Cohen, and a part-time Research Scientist at Google DeepMind.

I study learning and abstraction in neural networks, with a focus on the tradeoff between representational flexibility and efficiency. My recent work investigates how neural systems optimize this tradeoff across different time scales. I approach these topics from the perspective of rational (Bayesian) analysis, meta-learning, and information theory.

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