Morris Yu-Chao Huang

Incoming PhD Student @ University of North Carolina, Chapel Hill

I am an incoming PhD student at UNC CS, advised by Prof. Tianlong Chen and Prof. Guorong Wu. Previously, I had the opportunity to intern at NU, working under Prof. Han Liu in the MAGICS lab. Before that, I earned M.Sc. in Physics under the guidance of Prof. Hsi-Sheng Goan at National Taiwan University followed by research assistant position at National Center for Theoretical Sciences.

I push the frontier of AI through a scientific lens (Details).

Feel free to reach out if you think we should connect !

The closing lines of one of my recent favorite books by Albert Camus:

The struggle itself toward the heights is enough to fill a man’s heart. One must imagine Sisyphus happy.

~ News

Selected Publications

* These authors contributed equally to this work.


  1. Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
    Yu-Min Tseng*, Yu-Chao Huang*, Teng-Yun Hsiao*, Yu-Ching Hsu, Jia-Yin Foo, Chao-Wei Huang, and Yun-Nung Chen
    Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
  2. BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
    Chenwei Xu*, Yu-Chao Huang*, Jerry Yao-Chieh Hu*, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, and Han Liu
    International Conference on Machine Learning (ICML), 2024
  3. On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
    Jerry Yao-Chieh Hu*, Weimin Wu*, Yi-Chen Lee*, Yu-Chao Huang*, Minshuo Chen, and Han Liu
    International Conference on Learning Representations (ICLR), 2025

Machine Learning Demo - Hopfield Networks

The retrieve pattern is ... :drum:
(Steps: 0/3000)

The Nobel Prize in Physics 2024 is awarded to John Hopfield and Geoffrey Hinton! (see here) The Hopfield network is inspired by the Ising model. Hopfield network acts as a dynamic energy system where neurons interact to reach stable, low-energy states, similar to particles finding equilibrium in a physical system. The energy function is given by \[ E = -\frac{1}{2} \sum_{i \neq j} W_{ij} s_i s_j - \sum_{i} b_i s_i, \] where neuron interactions mimic energy exchanges, guiding the network to "remember" stored patterns (see a nice blog post). :brain: :sparkles: Check out our paper on utilizing modern Hopfield networks for tabular learning [ICML'24].