Portrait of Qianggang Ding is unavailable

Qianggang Ding

PhD - Université de Montréal
Supervisor
Bang Liu
Research Topics
Deep Learning
Generative Models
Graph Neural Networks
Natural Language Processing
Representation Learning

Publications

DASFormer: self-supervised pretraining for earthquake monitoring
Qianggang Ding
Zhichao Shen
Weiqiang Zhu
Bang Liu
Earthquake monitoring is a fundamental task to unravel the underlying physics of earthquakes and mitigate associated hazards for public safe… (see more)ty. Distributed acoustic sensing, or DAS, which transforms pre-existing telecommunication cables into ultra-dense seismic networks, offers a cost-effective and scalable solution for next-generation earthquake monitoring. However, current approaches for earthquake monitoring like PhaseNet and PhaseNet-2 primarily rely on supervised learning, while manually labeled DAS data is quite limited and it is difficult to obtain more annotated datasets. In this paper, we present DASFormer, a novel self-supervised pretraining technique on DAS data with a coarse-to-fine framework that models spatial-temporal signal correlation. We treat earthquake monitoring as an anomaly detection task and demonstrate DASFormer can be directly utilized as a seismic phase detector. Experimental results demonstrate that DASFormer is effective in terms of several evaluation metrics and outperforms state-of-the-art time-series forecasting, anomaly detection, and foundation models on the unsupervised seismic detection task. We also demonstrate the potential of fine-tuning DASFormer to downstream tasks through case studies.
2025-03-05
ICLR.cc/2025/Workshop/FM-Wild (published)
doi.org
openreview.net
MatExpert: Decomposing Materials Discovery by Mimicking Human Experts
Qianggang Ding
Santiago Miret
Bang Liu
Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a no… (see more)vel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our approach integrates three key stages: retrieval, transition, and generation. First, in the retrieval stage, MatExpert identifies an existing material that closely matches the desired criteria. Second, in the transition stage, MatExpert outlines the necessary modifications to transform this material formulation to meet specific requirements outlined by the initial user query. Third, in the generation state, MatExpert performs detailed computations and structural generation to create new materials based on the provided information. Our experimental results demonstrate that MatExpert outperforms state-of-the-art methods in material generation tasks, achieving superior performance across various metrics including validity, distribution, and stability. As such, MatExpert represents a meaningful advancement in computational material discovery using langauge-based generative models.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net