66 citations · 137 across the 16 of their papers we have counts for
16 papers
Machine Learning-based Correlation of Charpy Impact Properties Between Sub-sized and Standard-sized Specimens for Nuclear Structural Materials
Yugandhar Kasala Sreenivasulu, Isshu Lee, John W. Merickel +5
Reliable correlations of Charpy impact test results between sub-sized and full-sized specimens are essential for structural integrity assessments, particularly in nuclear applicati…
Post-Hoc Understanding of Metaphor Processing in Decoder-Only Language Models via Conditional Scale Entropy
Lawhori Chakrabarti, Jennifer Johnson-Leung, Bert Baumgaertner +3
Metaphor requires a language model to resolve a token whose contextual meaning diverges from its basic literal sense. Understanding how transformer models organize this reinterpret…
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems
Matthew Sgambati, Aleksandar Vakanski, Matthew Anderson
Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of…
GCSAM: Gradient Centralized Sharpness Aware Minimization
Mohamed Hassan, Aleksandar Vakanski, Boyu Zhang +1
The generalization performance of deep neural networks (DNNs) is a critical factor in achieving robust model behavior on unseen data. Recent studies have highlighted the importance…
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning
Jiang Chang, Deekshith Basvoju, Aleksandar Vakanski +2
Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the co…
Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification
Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang +2
Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due t…
LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification
Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir +1
The application of machine learning methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understandi…
Do Sharpness-based Optimizers Improve Generalization in Medical Image Analysis?
Mohamed Hassan, Aleksandar Vakanski, Min Xian
Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, si…
A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation
Kyle Lucke, Aleksandar Vakanski, Min Xian
In recent years, convolutional neural networks for semantic segmentation of breast ultrasound (BUS) images have shown great success; however, two major challenges still exist. 1) M…
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
Longze Li, Jiang Chang, Aleksandar Vakanski +3
With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted…
Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials
Mohammad Karimzadeh, Deekshith Basvoju, Aleksandar Vakanski +3
Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-be-layer from 3D modeling data. Among AM applications, t…
Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation
Mohammad Karimzadeh, Aleksandar Vakanski, Min Xian +1
Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women. Although machine learning-based Computer-Aided Diagnosis (CAD)…
Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network
Bryar Shareef, Min Xian, Aleksandar Vakanski +1
Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Although convolutional neural networks (CNNs) have demonstrated relia…
Enhanced Sharp-GAN For Histopathology Image Synthesis
Sujata Butte, Haotian Wang, Aleksandar Vakanski +1
Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. However, existing methods struggle to pro…
SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
Haotian Wang, Min Xian, Aleksandar Vakanski +1
Existing deep neural networks for histopathology image synthesis cannot generate image styles that align with different organs, and cannot produce accurate boundaries of clustered…
Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images
Haotian Wang, Aleksandar Vakanski, Changfa Shi +1
Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation…