Hi, I'm Mufeng Tang (唐沐丰)
I am a Ph.D. student in Computational Neuroscience and Machine Learning at the University of Oxford, working with Rafal Bogacz and Helen Barron. My general research interest lies at the intersection of artificial and biological intelligence. I am investigating biologically plausible learning rules for neural networks learning temporal structures, and how they can be employed to study learning, memory and generalization in the brain.
Currently, I am working at Amazon Science as an Applied Scientist Intern, working on LLMs for heterogeneous tabular data classification.
Prior to my PhD, I obtained my master's degree in statistics at the University of Chicago. I worked with Prof. Yali Amit and Prof. Jason MacLean at UChicago on biologically plausible neural networks.