Publications
Showing 28 results for Author: Derek C. Rose
Mar, 2026
Conference Paper
167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation
An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National Laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, as…
Feb, 2026
Journal
Denoising Seismograms in the Time Domain Using a Deep Learning Model
Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spect…
Jun, 2025
Journal
PickerXL, a Large Deep Learning Model to Measure Arrival Times from Noisy Seismic Signals
Precisely measuring seismic arrival times is a labor‐intensive task but is critical for both earthquake monitoring and subsurface imaging. Recently published deep learning models have demonstrated superior performance compared to traditional automatic approaches for picking arrival times. Although existing deep learning models have shown promising results, further advancem…
Apr, 2024
Journal
YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy
Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overla…
Dec, 2023
Conference Paper
Exascale Deep Learning to Accelerate Cancer Research
Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the…
Nov, 2023
Journal
Schizophrenia-related microdeletion causes defective ciliary motility and brain ventricle enlargement via microRNA-dependent mechanisms in mice
Progressive ventricular enlargement, a key feature of several neurologic and psychiatric diseases, is mediated by unknown mechanisms. Here, using murine models of 22q11-deletion syndrome (22q11DS), which is associated with schizophrenia in humans, we found progressive enlargement of lateral and third ventricles and deceleration of ciliary beating on ependymal cells lining…
Nov, 2023
ORNL Report
Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing
This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 3…
Nov, 2023
Conference Paper
ICDARTS: Improving the Stability of Cyclic DARTS
Cyclic DARTS (CDARTS) is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently. This training protocol aims to optimize the search process and evaluate the deep evaluation network comprised of discretized candidate operations. However, this a…