Scholar
Meng Jiang
Google Scholar ID: LZIPfCkAAAAJ
University of Notre Dame
Citations & Impact
Citations
9,412
H-index
50
i10-index
138
Publications
20
Co-authors
15
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Developed the tool for molecular discovery: torch-molecule; authored 'Modeling Polymers with Neural Networks'; published 'Deep Learning for Polymer Discovery: Foundation and Advances'; presented research on graph data augmentation at KDD'22, KDD'23, NeurIPS'23, LoG'24; published papers on generative models at NeurIPS'24, ICLR'25; discovered two superior polymeric gas separation membrane materials and published related reviews and research in Cell Reports Physical Science, Chemical Physics Reviews, Materials Today Physics, and Science Advances.
Research Experience
- I direct the Foundation Models and Applications Lab (FAML) at the Lucy Institute. I also direct the Data Mining towards Decision Making (DM2) Lab, supported by the National Science Foundation (NSF), National Institutes of Health (NIH), and Office of Naval Research (ONR).
Background
- I am an Associate Professor and Frank M. Freimann Collegiate Professor of Computer Science and Engineering at the University of Notre Dame. I'm appointed as a Lucy Family Institute Fellow and the Program Chair of ND-IBM Tech Ethics Lab. I am also an Amazon Scholar. My research fields are AI and Data Science. I'm interested in text and graph data for applications such as material discovery, recommender system, question answering, education, and mental health. My recent projects focus on knowledge-augmented NLP, instructed LLM, self-correct LLM, personalized LLM, unlearned LLM, graph data augmentation, and graph diffusion model.
Miscellany
- Information on personal interests and hobbies is not provided.
