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New Publications, Defenses, and New Members

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Publication February 5, 2025

New paper on "Brain Age Prediction with MEG Brain Networks" accepted for Publication
Paper titled "Fully Hyperbolic Neural Networks -- A Novel Approach to Studying Aging Trajectories" got accepted by IEEE JHBI.
project2 Hugo Ramirez

Publication December 15, 2024

New Preprint on "Transformers vs Mamba for Temporal Graph Embedding"
Paper titled "A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers" can be downloaded from arXiv.
project1 Ashish Pandey Sarang Patil

Defense December 2, 2024

Master's Thesis Defense by Hamid Razavi
Hamid successfully defended his Master's Thesis titled "Advancing Prediction of Stimulant Medication Misuse Through Graph Representation Learning."
Hamid Razavi

Publication May 11, 2024

New Paper on "Subjective Cognitive Decline Detection with MEG Brain Networks"
Paper titled "Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline" has been accepted by [IEEE Journal...
project3 Cole Baker

Publication December 22, 2023

New Paper on "Temporal graph embedding with transformers"
Paper titled "TransformerG2G - Adaptive Time-Stepping for Learning Temporal Graph Embeddings Using Transformers" has been accepted by Neural Networks.

Publication September 21, 2023

New Paper on "Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks"
Paper titled "Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks" has been accepted by NeurIPS.

Media Coverage

MIT News

New insights into training dynamics of deep classifiers (Download Paper)

MIT News (Homepage)

March 8, 2023

SIAMREVIEW

(Invited Paper) Understanding Graph Embedding Methods and Their Applications (Download paper)

An overview of graph embedding methods, their theoretical foundations, and practical applications. It highlights strengths, limitations, and real-world uses in social network analysis, brain networks, and protein networks.

November 4, 2021

PLos paper

New machine learning system can automatically identify shapes of red blood cells from blood samples of sickle cell anemia patients. (Download paper)

[Science Daily News] [Eurekalert | AAAS News]

October 19, 2017