Auto-omics for climate and sustainability
An automated explainable bioinformatics and AI workflow for multi-omic, climate and environmental data, applied to sustainability problems e.g., nature-based carbon capture.
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.
Helping researchers build and run virtual versions of real-world experiments in the cloud.
Developing AI and analytics to understand the drivers of study or clinical trial efficiency.
We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.
Performing pattern recognition and solving complex optimization problems with coupled oscillator networks.
An enterprise-scale initiative to measure, track, and reduce carbon emissions
Neuro-inspired AI to optimize learning and computing efficiency of next generation AI.
Enhance scientific discovery with multimmodal knowledge