Knowledge Capture and Discovery

About

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.

Research Interests

Projects

DANA

MINT

Model Integration through Knowledge-Rich Data and Process Composition

ODC

Organic Data Curation

A novel approach to data publishing that requires minimal effort from scientists

OPMW

OPMW-PROV

Tracking the provenance of scientific experiments and their executions

DANA

Data Narratives

Creating automatic descriptions of experiment results

LinkedEarth

LinkedEarth

Organizing and sharing Earth Science data, with a focus on paleoclimate data.

LinkedEarth

FragFlow

Finding common fragments in scientific workflows

GPF

GPF

Guidelines and courses to build the Geoscience Papers of the Future

DISK

DISK

Helping automate the discovery of scientific knowledge

OntoSoft

OntoSoft

A software metadata registry to describe scientific software in a user-friendly manner

ODS

Organic Data Science

Resolving science processes through an open framework that facilitates participation

SPF

Organic Data Science

Encouraging scientists to publish papers with the associated products of their research

P4ML

P4ML

A Phased Performance-Based Pipeline Planner for Automated Machine Learning

WINGS

WINGS

A semantic workflow system that assists scientists with the design of computational experiments

ASSET

ASSET

A new sketching project to accelerate scientific workflows using EarthCube technologies

LinkedEarth

AutoTS

Automatic Time Series Analysis

Learn more about our software:  

People

Yolanda Gil

Yolanda Gil

Director of the group

Personal page
Twitter

Varun Ratnakar

Varun Ratnakar

Research Programmer

Personal page
Twitter

Deborah Khider

Deborah Khider

Data Scientist

Personal page
Twitter

Maximiliano Osorio

Maximiliano Osorio

Research programmer

Personal page
Twitter

Hernan Vargas

Hernan Vargas

Research Programmer



Collaborators

Collaborators

Students, collaborators and alumni

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Publications

Join us

2020 Summer interns (performed online), together with Yolanda (center) and Daniel (center-up)
2019 Summer interns, together with Yolanda,Daniel And Varun.
2018 Summer interns, together with Yolanda (center-right) and Daniel (center-down)
2017 Summer interns
2016 Summer interns, together with Yolanda.

We are always looking for collaborators including exceptional students who are passionate about AI and scientific research.

If you are interested in a position, send your CV to: gil at isi.edu or dgarijo at isi.edu