Oral Presentation of My Dissertation Proposal at CS PhD Retreat
Better AI in Scientific Applications: Interdisciplinary Approaches for Advancing Representational Learning
May 29, 2024
Asymmetric contrastive multimodal learning for advancing chemical understanding
We propose an asymmetric contrastive multimodal learning framework, an effective and training-efficient framework tailored for molecules, promoting cross-modality understanding between the molecular graph and other chemical modalities.
Nov 11, 2023
Poster Presentation at Boston Symmetry Day 2023
Motif-based Graph Representation Learning for Molecules
Apr 7, 2023
Motif-based graph representation learning with application to chemical molecules
We designed a novel convolution module for graph representational learning on molecules with an efficient pretraining strategy, enabling the capture of local structural and semantic information from graph motifs.
Jan 11, 2023
On dyadic fairness: Exploring and mitigating bias in graph connections.
We theoretically relate the graph connections to dyadic fairness on link predictive scores in learning graph neural networks and accordingly introduced an algorithm for fair link prediction by adjusting the adjacency weight matrix to address the fairness-utility trade-off.
Jan 12, 2021