Data Analytics

Every night, the Vera C. Rubin Observatory will produce 20 terabytes of raw image data. And every day, the data will be processed in near-real-time, tracking unexpected events unfolding in the universe and creating a 500-petabyte archive for research.

And that’s just one example of the advanced data analytics that have put us at the forefront of data-intensive research. From resource allocation services to database administration for large installations, we provide a number of essential services that enable discovery and expand scientific knowledge. Using traditional machine learning, deep learning, and software engineering expertise, we assist Industry Partner Program clients in making data-driven decisions. And, as part of the C3.ai Digital Transformation Institute, we help scientists navigate the intersection of AI, machine learning, cloud computing, the internet of things, big data analytics, organizational behavior, public policy and ethics.

NCSA’s Data Analytics team works with researchers and scientists to help analyze their large datasets to make the most effective use of the resources we provide. Here’s a small sampling of the types of projects we can support:

We specialize in working with biological and biomedical images, applying deep learning and software engineering techniques into problem-solving.

NCSA has a long history of applying machine-learning and deep-learning techniques into remote sensing and satellite image data for classification, mapping and change detection purposes of the natural environment.

We design and implement software libraries and apply them to simplify frequently encountered machine-learning problems.

We are involved in the operation, support and training of NCSA’s high-performance computing systems, such as HOLL-I and HAL, and leveraging them into artificial intelligence applications. We won second place in a prestigious AI competition on critical mineral assessment, co-sponsored by DARPA and the U.S. Geological Survey.

NCSA works with faculty, students and industrial partners on research and industrial consulting projects providing data-related solutions such as data processing, analysis, visualization, HPC performance tuning and cloud computing.

NCSA Spotlight

Matthew Krafczyk
Data Analyst

Matt leverages experience in computational physics and machine learning to bring research closer to real-world application.

Deep learning and computational physics have driven some of the most important technological advances of the past decade. Working in these domains provides interesting challenges on a daily basis!

Project Highlights

Keras Spatial

This python package for pre-processing and augmenting geospatial data for Tensorflow deep learning models is just one example of how we help make managing data easier.

Phillips 66

Using data mining, data engineering, and machine learning, the team has helped Phillips 66 explore time series and geospatial data and more.

All Project Highlights

News

Science Features

Illinois Computes Hitting Its Stride

One year removed from its public announcement, Illinois Computes is establishing itself as a critical computing and support resource for University of Illinois researchers.

April 9, 2024