Leveraging AI foundation models and multi-cloud computing to usher in a new era of reproducible and collaborative experimentation for scientific discovery.
- Machine Learning
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
An automated explainable bioinformatics and AI workflow for multi-omic, climate and environmental data, applied to sustainability problems e.g., nature-based carbon capture.
AutoML for incremental machine learning algorithms for big time-series data.
Developing AI and analytics to understand the drivers of study or clinical trial efficiency.
Understanding spatiotemporal heterogeneity across different scales of biological organization.
Modeling the 4D genome with deep learning and stochastic simulations.
Analyzing and visualizing differences in blood flow patterns, made visible with fluorescent dyes and multispectral imaging.
Using Logical Neural Networks to demonstrate the benefit of incorporating knowledge and reasoning into neural network learning
Combining spatial single-cell omics with AI to model the complexity of the tumor micorenvironment and enable novel spatial biomarker discovery.