activity
20192022
most citedSnowboot: Bootstrap Methods for Network Inference

12 citations · 14 across the 7 of their papers we have counts for

collaborators

8 papers

cs.HC2022

Learning on Health Fairness and Environmental Justice via Interactive Visualization

Abdullah-Al-Raihan Nayeem, Ignacio Segovia-Dominguez, Huikyo Lee +5

This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioe…

cs.LG20221 cited

Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets

Yuzhou Chen, Tian Jiang, Miguel Heleno +2

Nowadays, it is broadly recognized in the power system community that to meet the ever expanding energy sector's needs, it is no longer possible to rely solely on physics-based mod…

cs.LG2022

ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery

Andac Demir, Baris Coskunuzer, Ignacio Segovia-Dominguez +3

In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of…

cs.LG20211 cited

Topological Relational Learning on Graphs

Yuzhou Chen, Baris Coskunuzer, Yulia R. Gel

Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are…

cs.LG2021

Using NASA Satellite Data Sources and Geometric Deep Learning to Uncover Hidden Patterns in COVID-19 Clinical Severity

Ignacio Segovia-Dominguez, Huikyo Lee, Zhiwei Zhen +5

As multiple adverse events in 2021 illustrated, virtually all aspects of our societal functioning -- from water and food security to energy supply to healthcare -- more than ever d…

cs.LG2021

Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting

Yuzhou Chen, Ignacio Segovia-Dominguez, Yulia R. Gel

There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block…