Research
My research centers on the development and application of statistical methodology for complex business data. I creates tools for discrete data analysis and the integration of large language models to analyze mixed-type and unstructured survey data. In parallel, I advance machine learning and deep learning methods (penalized regression, neural networks, etc.) for high-dimensional, multimodal data, with applications in Asset Pricing, Organizational Behavior, and Gene–Environment Interaction.
Methodology: Discrete Data Analysis, Nonparametric Statistics, Penalized Regression, Machine/Deep Learning, Large Language Models (LLMs), High-Dimensional and Multimodal Analysis, Qualitative Comparative Analysis
Application: Survey Analysis, Social Mobility, Financial Asset Pricing, Organizational Behavior, Information Systems, Gene–Environment Interaction
Selected Research
Working Papers
Jiawei Huang, Dungang Liu, Yuan Jiang, & Yu Xie. An “i-mobility” Framework for Studying Social Mobility: Individualized Inference via Generative Analysis of Discrete Data. Job Market Paper.
Hui Guo, Jiawei Huang, Runze Li, & Yan Yu. Simplicity versus Complexity: The Role of Historical Average in Kelly, Malamud, and Zhou’s (2024) RFF Model. Link
Peer-Reviewed Publications
Jiawei Huang, Jie Sheng, & Daifeng Wang. Manifold Learning Analysis Suggests Strategies to Align Single-Cell Multimodal Data of Neuronal Electrophysiology and Transcriptomics. Communications Biology 4.1 (2021): 1308. Link
Nam D. Nguyen, Jiawei Huang, & Daifeng Wang. A Deep Manifold-Regularized Learning Model for Improving Phenotype Prediction from Multimodal Data. Nature Computational Science 2.1 (2022): 38-46. Link
Ting Jin, Peter Rehani, Mufang Ying, Jiawei Huang, Shuang Liu, Panagiotis Roussos, & Daifeng Wang. scGRNom: A Computational Pipeline for Integrative Multi-Omics Analysis to Predict Cell-Type-Specific Disease Genes and Regulatory Networks. Genome Medicine 13.1 (2021): 95. Link