Making all research data accessible, discoverable, and usable

Globus Labs is a research group led by Prof. Ian Foster and Dr. Kyle Chard that spans the Department of Computer Science at the University of Chicago and the Data Science and Learning Division at Argonne National Laboratory. Our modest goal is to realize a world in which all research data are reliably, rapidly, and securely accessible, discoverable, and usable. To this end, we work on a broad range of research problems in data-intensive computing and research data management. Our work is made possible by much-appreciated support from the National Science Foundation, National Institutes of Health, Department of Energy, National Institute of Standards and Technology, and other sources, and in addition to computer science, engages fields as diverse as materials science, biology, archaeology, climate policy, and social sciences. We work closely with the team developing the Globus research data management platform, who often challenge us to think bigger—and sometimes implement our less crazy ideas.


We are developing methods to automate the scientific data lifecycle.

An open source machine learning platform for scientists

funcX is a Function as a Service platform for scientific computing.


Ian’s paper “Autonomous experimentation systems for materials development: a community perspective” is published in Matter.

Matt (Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources), Tak’s (Cloud Clustering Over January 2003 via Scalable Rotationally Invariant Autoencoder), Hong’s (Gold Panning: Automatic Extraction of Scientific Information from Publications), Max’s (Ultrafast Focus Detection for Automated Microscopy), and the funcX teams’ (Federated Function as a Service for eScience) posters were accepted to eScience 2021.

Tak’s paper “Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder” (DOI: 10.1109/TGRS.2021.3098008) is accepted for publication at IEEE Transactions on Geoscience and Remote Sensing!

Logan Ward’s paper “Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models” is accepted for publication at The Journal of Physical Chemistry A!