AUGUST 14, 2024 Journal Article Informing Solar Blind Radioluminescence Imaging through a Calibrated Spectrum ( Read More )
AUGUST 7, 2024 Conference Paper Red-QAOA: Efficient Variational Optimization through Circuit Reduction ( Read More )
AUGUST 6, 2024 Conference Paper Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing ( Read More )
AUGUST 6, 2024 Conference Paper FuseIM: Fusing Probabilistic Traversals for Influence Maximization on Exascale Systems ( Read More )
AUGUST 6, 2024 Conference Paper Evaluating Emerging AI/ML Accelerators: IPU, RDU, and and NVIDIA/AMD GPUs ( Read More )
AUGUST 6, 2024 Conference Paper DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems ( Read More )
JULY 31, 2024 Conference Paper Meshfree Process Modeling and Experimental Validation of Friction Riveting of Aluminum 5052 to Aluminum 6061 ( Read More )
JULY 26, 2024 Journal Article ABC-Auxetics: An Implicit Design Approach for Negative Poisson’s Ratio Materials ( Read More )
JULY 26, 2024 Conference Paper Investigating Marine Environmental Degradation Of Additive Manufacturing Materials For Renewable Energy Applications ( Read More )
JULY 26, 2024 Journal Article Modeling the Effects of Artificial Drainage on Agriculture-dominated Watersheds using a Fully Distributed Integrated Hydrology Model ( Read More )
MAY 14, 2024 Report Oxide Dispersion Strengthened Ferritic Steel Wire Feedstock Development for Large Format Additive Manufacturing - CRADA 620 (Abstract) ( Read More )
APRIL 26, 2024 Conference Paper Malicious Cyber Activity Detection using Zigzag Persistence ( Read More )
APRIL 20, 2024 Conference Paper cuAlign: Scalable Network Alignment on GPU Accelerators ( Read More )
APRIL 20, 2024 Conference Paper QASMTrans: A QASM Quantum Transpiler Framework for NISQ Devices ( Read More )
APRIL 17, 2024 Report UQ4QM: Uncertainty Quantification for Quantum Materials - Final Report to Chemical Dynamics Initiative ( Read More )
MARCH 15, 2024 Journal Article Chemical composition based machine learning model to predict defect formation in additive manufacturing ( Read More )