2 citations · 2 across the 8 of their papers we have counts for
10 papers
CANTO: CAD-Native Transformer Operators for AI-Aided Engineering
Daniel Leibovici, Nikola Borislavov Kovachki, Dawon Ahn +8
Modern engineering systems, from automobiles to aircraft, are designed by using precise, continuous parametric computer-aided design (CAD) models. Evaluating design changes through…
AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models
Dawon Ahn, Auder Der, Evangelos E. Papalexakis
Accurately explaining hidden patterns in multi-aspect data has typically been done by leveraging labels and/or accompanying auxiliary metadata. However, labels and auxiliary data m…
Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods
Dawon Ahn, Het Patel, Aemal Khattak +2
Railway crossings present complex safety challenges where driver behavior varies by location, time, and conditions. Traditional approaches analyze crossings individually, limiting…
Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods
Paimon Goulart, Jordan Steinhauser, Dawon Ahn +4
Scientific discovery pipelines typically involve complex, rigid, and time-consuming processes, from data preparation to analyzing and interpreting findings. Recent advances in AI h…
Multi-view Graph Condensation via Tensor Decomposition
Nícolas Roque dos Santos, Dawon Ahn, Diego Minatel +2
Graph Neural Networks (GNNs) have demonstrated remarkable results in various real-world applications, including drug discovery, object detection, social media analysis, recommender…
Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation
Dawon Ahn, Jun-Gi Jang, Evangelos E. Papalexakis
Group fairness is important to consider in tensor decomposition to prevent discrimination based on social grounds such as gender or age. Although few works have studied group fairn…
Global and Local Structure Learning for Sparse Tensor Completion
Dawon Ahn, Evangelos E. Papalexakis
How can we accurately complete tensors by learning relationships of dimensions along each mode? Tensor completion, a widely studied problem, is to predict missing entries in incomp…
Tensor Completion for Surrogate Modeling of Material Property Prediction
Shaan Pakala, Dawon Ahn, Evangelos Papalexakis
When designing materials to optimize certain properties, there are often many possible configurations of designs that need to be explored. For example, the materials' composition o…
Automating Data Science Pipelines with Tensor Completion
Shaan Pakala, Bryce Graw, Dawon Ahn +5
Hyperparameter optimization is an essential component in many data science pipelines and typically entails exhaustive time and resource-consuming computations in order to explore t…
TRAWL: Tensor Reduced and Approximated Weights for Large Language Models
Yiran Luo, Het Patel, Yu Fu +4
Recent research has shown that pruning large-scale language models for inference is an effective approach to improving model efficiency, significantly reducing model weights with m…