Morris Huang
|Morris⟩ = |AI⟩ + |Science⟩
Hey, my name is Morris (Yu-Chao) Huang (黃禹超 in Chinese). I have recently graduated with a Master’s in Physics under the guidance of Prof. Hsi-Sheng Goan at National Taiwan University. I am currently working as research assistant at National Center for Theoretical Sciences.
The equation |Morris⟩ = |AI⟩ + |Science⟩ reflects my superposition of research passion at the intersection of artificial intelligence and science. My research interests lie in two main directions: (1) Theoretical understanding of the computational and statistical properties of deep learning models, and (2) Developing methodologies with theoretical guarantees to ensure both optimality and practical applicability. I am a prospective Ph.D. applicant for fall 2025.
Machine Learning Demo - Hopfield Networks
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). Check out our paper on utilizing modern Hopfield networks for tabular learning [ICML'24].
Physics Demo - Two Stream Instability
Beam Width ( \(1 / V_b \) )
The phenomenon of the two-stream instability occurs where plasma flows are moving in opposite directions. This instability arises from the transfer of energy between particles in the plasma and EM wave, leading to the exponential growth of certain wave modes, described by the dispersion relation. \[ 1 = \dfrac{\omega_p^2/2}{(\omega+\omega_D)^2} + \dfrac{\omega_p^2/2}{(\omega-\omega_D)^2}. \] (See blog post for more detail )