47 citations · 111 across the 9 of their papers we have counts for
9 papers
Predicting Compressive Strength of Consolidated Molecular Solids Using Computer Vision and Deep Learning
Brian Gallagher, Matthew Rever, Donald Loveland +6
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g. compressive strength) based on SEM images. We show that it's…
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
Bhavya Kailkhura, Brian Gallagher, Sookyung Kim +2
Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis…
Network Structure Inference, A Survey: Motivations, Methods, and Applications
Ivan Brugere, Brian Gallagher, Tanya Y. Berger-Wolf
Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many c…
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks
Timothy La Fond, Jennifer Neville, Brian Gallagher
An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they…
MaxOutProbe: An Algorithm for Increasing the Size of Partially Observed Networks
Sucheta Soundarajan, Tina Eliassi-Rad, Brian Gallagher +1
Networked representations of real-world phenomena are often partially observed, which lead to incomplete networks. Analysis of such incomplete networks can lead to skewed results.…
Anomaly Detection in Dynamic Networks of Varying Size
Timothy La Fond, Jennifer Neville, Brian Gallagher
Dynamic networks, also called network streams, are an important data representation that applies to many real-world domains. Many sets of network data such as e-mail networks, soci…
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
Neil Shah, Alex Beutel, Brian Gallagher +1
How can we detect suspicious users in large online networks? Online popularity of a user or product (via follows, page-likes, etc.) can be monetized on the premise of higher ad cli…
Dynamic Behavioral Mixed-Membership Model for Large Evolving Networks
Ryan Rossi, Brian Gallagher, Jennifer Neville +1
The majority of real-world networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook, ...). To understand the structural behavior of nodes in these large…
Role-Dynamics: Fast Mining of Large Dynamic Networks
Ryan Rossi, Brian Gallagher, Jennifer Neville +1
To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity…