Published in International Conference on Learning Representations (ICLR), Oral Presentation, 2026
Analyzes the polyhedral structure induced by ReLU networks to better understand their discrete geometry.
Published in 2023 IEEE International Conference on Big Data, 2023
Methods for extracting chemical reaction fingerprints from large-scale knowledge graphs for downstream modeling.
Published in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022
Sparsification techniques for heterogeneous graphs to improve the scalability of representation learning.
Published in Neurocomputing, 2025
A mixed-integer programming framework for generating faithful explanations of graph neural network predictions.
Published in Molecular Informatics, 2023
A fragment-based deep generative model for molecular design using a hierarchical chemical graph representation.
Published in Journal of Molecular Graphics and Modelling, 2021
A novel 3D deep learning approach for protein-ligand binding affinity prediction using octree-based volumetric representation to efficiently characterize molecular surfaces.