10 citations · 11 across the 5 of their papers we have counts for
5 papers
Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics
Ran Zhang, Mohannad Elhamod
The rapid advancement of LLMs has led to the creation of diverse agentic systems in data analysis, utilizing LLMs' capabilities to improve insight generation and visualization. In…
Neuro-Visualizer: An Auto-encoder-based Loss Landscape Visualization Method
Mohannad Elhamod, Anuj Karpatne
In recent years, there has been a growing interest in visualizing the loss landscape of neural networks. Linear landscape visualization methods, such as principal component analysi…
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
Mohannad Elhamod, Mridul Khurana, Harish Babu Manogaran +15
Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how…
Physics-Informed Machine Learning for Optical Modes in Composites
Abantika Ghosh, Mohannad Elhamod, Jie Bu +3
We demonstrate that embedding physics-driven constraints into machine learning process can dramatically improve accuracy and generalizability of the resulting model. Physics-inform…
CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems
Mohannad Elhamod, Jie Bu, Christopher Singh +5
Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network out…