We develop Artificial Intelligence (AI) approaches that use knowledge to accelerate and innovate scientific discovery processes that are unnecessarily carried out manually and inefficiently today.
We work on a variety of AI research areas, such as semantic workflows, human-guided machine learning, interdisciplinary model integration, knowledge networks, controlled crowdsourcing of metadata, and automated hypothesis-driven discovery. A key theme in our projects is the use of AI technologies for different aspects of data science processes in order to make them more efficient.
We collaborate with scientists in diverse areas including Earth sciences, neuroscience, genomics and proteomics, agriculture, and economics.
Model Integration through Knowledge-Rich Data and Process Composition
A novel approach to data publishing that requires minimal effort from scientists
Tracking the provenance of scientific experiments and their executions
Creating automatic descriptions of experiment results
Organizing and sharing Earth Science data, with a focus on paleoclimate data.
Finding common fragments in scientific workflows
Guidelines and courses to build the Geoscience Papers of the Future
Automating hypothesis-driven discovery in different scientific domains
A software metadata registry to describe scientific software in a user-friendly manner
Resolving science processes through an open framework that facilitates participation
Encouraging scientists to publish papers with the associated products of their research
A Phased Performance-Based Pipeline Planner for Automated Machine Learning
A semantic workflow system that assists scientists with the design of computational experiments
A new sketching project to accelerate scientific workflows using EarthCube technologies
Automatic Time Series Analysis
We are always looking for collaborators including exceptional students who are passionate about AI and scientific research.
