Our Research Directions
At Hawk-Franklin Research, we are at the forefront of pioneering AI methodologies to tackle complex challenges, particularly in biosciences and healthcare. Explore our key research areas where we are pushing the boundaries of innovation through detailed reviews and novel developments.
In-Context Learning for GNNs
We are investigating novel approaches to enable Graph Neural Networks to perform in-context learning. This allows GNNs to adapt to new graph structures and tasks with minimal examples, drawing inspiration from Large Language Models, which is crucial for dynamic biological data scenarios.
Tabular Foundation Models
Our focus is on developing robust foundation models specifically engineered for tabular data. By pre-training on diverse and extensive datasets, these models aim to achieve superior zero-shot or few-shot performance in critical areas such as cancer genomics and biomarker discovery.
Survival Foundation Models
We are advancing specialized foundation models for survival analysis. These models are designed to integrate multi-modal patient data to deliver more accurate prognostic predictions, significantly contributing to personalized medicine strategies in oncology and other critical care areas.