Haowei (Jack) Hua华 浩 炜

I am an undergraduate at Princeton University studying Neuroscience, with minors in Cognitive Science and French. My research sits at the intersection of neuroscience, machine learning, data science, and computational biology — with a particular interest in how computational models can help us understand complex biological and cognitive systems, from human reasoning and educational measurement to genomic regulation and scientific discovery.

Across my work, I use machine learning and deep learning as a common language for interdisciplinary research. My experience spans natural-language processing, automated assessment, multimodal data analysis, computational genomics, sequence modeling, and large-scale model evaluation. I am particularly interested in generalization beyond familiar training distributions, interpretable modeling, and reproducible scientific experimentation.

My research interests developed around a common question: how can computational models capture meaningful structure in complex human and biological systems? Early work in educational measurement examined AI-generated writing, automated scoring, and differences between human and machine judgment. These projects led to broader questions about representation, robustness, interpretability, and generalization — questions I now pursue across computational genomics, neuroscience, and machine learning.