Meng Jiang

Assistant Professor

University of Notre Dame

United States

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About

Dr. Meng Jiang is an Assistant Professor at the University of Notre Dame in the United States. His research areas include machine learning applications in resource-constrained environments, polymeric membrane materials for gas separation, and graph neural networks. He has contributed to numerous publications that explore topics such as transfer learning, multimodal learning, and knowledge graph reasoning.

Articles (15)

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2025

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2024

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2024

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2024

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2024

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2022

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2022

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2022

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2022

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2022

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2022

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2021

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2020

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2020

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2020

Collaborators (6)

Tengfei Luo

Dorini Family Professor

University of Notre Dame

UNITED STATES

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Tomasz Kajdanowicz

Head of the Department

Wroclaw University of Science and Technology

POLAND

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Nitesh Chawla

University of Notre Dame

UNITED STATES

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Ahmed Abbasi

University of Notre Dame

UNITED STATES

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Ruiyang Qin

Assistant Professor

Villanova University

UNITED STATES

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Agboola Suleiman

University of Notre Dame

UNITED STATES

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Meng Jiang

Research Interests

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About

Articles (15)

Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices

Rationalizing Graph Neural Networks with Data Augmentation

Transcend the boundaries: Machine learning for designing polymeric membrane materials for gas separation

Transfer Learning across Graph Convolutional Networks: Methods, Theory, and Applications

Large Language Models on Graphs: A Comprehensive Survey

Leveraging low-fidelity data to improve machine learning of sparse high-fidelity thermal conductivity data via transfer learning

Heterogeneous relational reasoning in knowledge graphs with reinforcement learning

Deep Multimodal Complementarity Learning

Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning

Biomedical Knowledge Graphs Construction From Conditional Statements

Binarizing Weights Wisely for Edge Intelligence: Guide for Partial Binarization of Deconvolution-Based Generators

Leverage electron properties to predict phonon properties via transfer learning for semiconductors

Precise temporal slot filling via truth finding with data-driven commonsense

Collaborators (6)

Tengfei Luo

Tomasz Kajdanowicz

Nitesh Chawla

Ahmed Abbasi

Ruiyang Qin

Agboola Suleiman

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