kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes. - Chen Cao 002; Devin Kwok; Shannon Edie; Qing Li 076; Bowei Ding; Pathum Kossinna; Simone Campbell; Jingjing Wu 002; Matthew Greenberg; Quan Long (Briefings Bioinform., 2021)

Neural Networks as Paths through the Space of Representations. - Richard D. Lange; Jordan Matelsky; Xinyue Wang; Devin Kwok; David S. Rolnick; Konrad P. Kording (CoRR, 2022)

webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study. - Chen Cao 002; Jianhua Wang; Devin Kwok; Feifei Cui; Zilong Zhang; Da Zhao; Mulin Jun Li; Quan Zou 001 (Nucleic Acids Res., 2022)

Deep Networks as Paths on the Manifold of Neural Representations. - Richard D. Lange; Devin Kwok; Jordan Kyle Matelsky; Xinyue Wang; David Rolnick; Konrad P. Kording (TAG-ML, 2023)

Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. - Ekansh Sharma; Devin Kwok; Tom Denton; Daniel M. Roy 001; David Rolnick; Gintare Karolina Dziugaite (ECML/PKDD (7), 2024)

Dataset Difficulty and the Role of Inductive Bias. - Devin Kwok; Nikhil Anand; Jonathan Frankle; Gintare Karolina Dziugaite; David Rolnick (CoRR, 2024)

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records. - Guishen Wang; Xiaoxue Lou; Fang Guo; Devin Kwok; Chen Cao 002 (IEEE J. Biomed. Health Informatics, 2024)

The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions. - Devin Kwok; Gül Sena Altintas; Colin Raffel; David Rolnick (CoRR, 2025)