15 citations · 61 across the 20 of their papers we have counts for
24 papers
2024 NSF CSSI-Cybertraining-SCIPE PI Meeting August 12 to 13, 2024, Charlotte, NC
Abani Patra, Mary Thomas, Elias Bou-Harb +31
The second annual NSF, OAC CSSI, CyberTraining and related programs PI meeting was held August 12 to 13 in Charlotte, NC, with participation from PIs or representatives of all majo…
Large Deviations for Accelerating Neural Networks Training
Sreelekha Guggilam, Varun Chandola, Abani Patra
Artificial neural networks (ANNs) require tremendous amount of data to train on. However, in classification models, most data features are often similar which can lead to increase…
Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility
Syed Mohammed Arshad Zaidi, Varun Chandola, EunHye Yoo
Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling…
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds
Amol Salunkhe, Georgios Georgalis, Abani Patra +1
Complete computation of turbulent combustion flow involves two separate steps: mapping reaction kinetics to low-dimensional manifolds and looking-up this approximate manifold durin…
Physics Informed Machine Learning for Chemistry Tabulation
Amol Salunkhe, Dwyer Deighan, Paul Desjardin +1
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Inste…
Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data
Pranav Sankhe, Seventy F. Hall, Melanie Sage +3
Youth in the American foster care system are significantly more likely than their peers to face a number of negative life outcomes, from homelessness to incarceration. Administrati…
ChemTab: A Physics Guided Chemistry Modeling Framework
Amol Salunkhe, Dwyer Deighan, Paul DesJardin +1
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Inste…
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle
Sreelekha Guggilam, Varun Chandola, Abani Patra
Most current anomaly detection methods suffer from the curse of dimensionality when dealing with high-dimensional data. We propose an anomaly detection algorithm that can scale to…
From images in the wild to video-informed image classification
Marc Böhlen, Varun Chandola, Wawan Sujarwo +1
Image classifiers work effectively when applied on structured images, yet they often fail when applied on images with very high visual complexity. This paper describes experiments…
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)
Sreelekha Guggilam, Syed M. A. Zaidi, Varun Chandola +1
Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with…
Bayesian Anomaly Detection Using Extreme Value Theory
Sreelekha Guggilam, S. M. Arshad Zaidi, Varun Chandola +1
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is…
Learning Deep Representations from Clinical Data for Chronic Kidney Disease
Duc Thanh Anh Luong, Varun Chandola
We study the behavior of a Time-Aware Long Short-Term Memory Autoencoder, a state-of-the-art method, in the context of learning latent representations from irregularly sampled pati…
Query Log Compression for Workload Analytics
Ting Xie, Oliver Kennedy, Varun Chandola
Analyzing database access logs is a key part of performance tuning, intrusion detection, benchmark development, and many other database administration tasks. Unfortunately, it is c…
dynamicMF: A Matrix Factorization Approach to Monitor Resource Usage in High Performance Computing Systems
Niyazi Sorkunlu, Duc Thanh Anh Luong, Varun Chandola
High performance computing (HPC) facilities consist of a large number of interconnected computing units (or nodes) that execute highly complex scientific simulations to support sci…
Detecting Data Leakage from Databases on Android Apps with Concept Drift
Gokhan Kul, Shambhu Upadhyaya, Varun Chandola
Mobile databases are the statutory backbones of many applications on smartphones, and they store a lot of sensitive information. However, vulnerabilities in the operating system or…
Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models
Jialiang Jiang, Sharon Hewner, Varun Chandola
Hospital readmissions have become one of the key measures of healthcare quality. Preventable readmissions have been identified as one of the primary targets for reducing costs and…
Learning Manifolds from Non-stationary Streaming Data
Suchismit Mahapatra, Varun Chandola
Streaming adaptations of manifold learning based dimensionality reduction methods, such as Isomap, are based on the assumption that a small initial batch of observations is enough…
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
Frank Schoeneman, Varun Chandola, Nils Napp +2
Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Map…
Server, server in the cloud. Who is the fairest in the crowd?
Marc Böhlen, Varun Chandola, Amol Salunkhe
This paper follows the recent history of automated beauty competitions to discuss how machine learning techniques, in particular neural networks, alter the way attractiveness is ha…
Modeling Graphs Using a Mixture of Kronecker Models
Suchismit Mahapatra, Varun Chandola
Generative models for graphs are increasingly becoming a popular tool for researchers to generate realistic approximations of graphs. While in the past, focus was on generating gra…
S-Isomap++: Multi Manifold Learning from Streaming Data
Suchismit Mahapatra, Varun Chandola
Manifold learning based methods have been widely used for non-linear dimensionality reduction (NLDR). However, in many practical settings, the need to process streaming data is a c…
Tracking System Behaviour from Resource Usage Data
Niyazi Sorkunlu, Varun Chandola, Abani Patra
Resource usage data, collected using tools such as TACC Stats, capture the resource utilization by nodes within a high performance computing system. We present methods to analyze t…
Error Metrics for Learning Reliable Manifolds from Streaming Data
Frank Schoeneman, Suchismit Mahapatra, Varun Chandola +2
Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data…
Summarizing Large Query Logs in Ettu
Gokhan Kul, Duc Luong, Ting Xie +4
Database access logs are large, unwieldy, and hard for humans to inspect and summarize. In spite of this, they remain the canonical go-to resource for tasks ranging from performanc…