Georgia Tech’s Austin P. Wright unveiled Nested Fusion, an innovative algorithm initially used by NASA’s Perseverance rover to search for signs of past life on Mars. Now, it’s poised to revolutionize the way we forecast hurricanes, wildfires and study climate patterns on Earth.
A groundbreaking algorithm, Nested Fusion, first tested on NASA’s Perseverance Rover on Mars, has the potential to revolutionize our understanding of extreme weather events and climate patterns here on Earth. Developed by Georgia Tech doctoral student Austin P. Wright, Nested Fusion’s innovative approach was a runner-up for the best paper award at the prestigious 2024 International Conference on Knowledge Discovery and Data Mining (KDD 2024).
“Nested Fusion is really useful for researchers in many different domains, not just NASA scientists,” Wright said in a news release. “The method visualizes complex datasets that can be difficult to get an overall view of during the initial exploratory stages of analysis.”
Nested Fusion’s primary function is to combine datasets with different resolutions into a single, high-resolution visual distribution. This approach permits NASA scientists to analyze multiple datasets from various sources concurrently, significantly enhancing the efficiency of studying Mars’ surface composition for signs of previous life.
The algorithm’s utility isn’t confined to space exploration. Scientists across various fields can apply Nested Fusion’s methods to manage large, overlapping datasets, fueling advances in earth sciences such as climate modeling, plant and animal studies, and more.
Wright shared that users have begun integrating Nested Fusion into earth science contexts, merging satellite imagery, biomarkers and climate data. This synthesis is expected to accelerate the predictive capabilities for hurricanes, wildfires and other weather phenomena.
“Cross-correlational analysis takes a long time to do and is not done in the initial stages of research when patterns appear and form new hypotheses,” Wright added. “Nested Fusion enables people to discover these patterns much earlier.”
Wright’s leadership in data science and machine learning extends further within NASA’s Mars 2020 mission. As the data science and ML lead for PIXLISE software, Wright works closely with NASA Jet Propulsion Laboratory scientists to study data from the Perseverance Rover. The rover’s Planetary Instrument for X-ray Lithochemistry (PIXL) comprises an X-ray Fluorescence (XRF) Spectrometer and a Multi-Context Camera (MCC), which generate co-aligned datasets critical for analyzing Martian targets.
This innovation simplifies a previously labor-intensive process. NASA scientists, using Nested Fusion, now estimate a sample’s mineral composition in hours — a task that once demanded days of collaborative effort among specialized teams.
“I was extremely happy that this work was recognized with the best paper runner-up award,” added Wright. “This kind of applied work can sometimes be hard to find the right academic home, so finding communities that appreciate this work is very encouraging.”
Presented at KDD 2024 in Barcelona, Spain, the Nested Fusion algorithm was honored among over 150 papers in the applied data science track, underscoring its significant contributions to the field.
Wright’s innovative work demonstrates the profound impact of data science on traditional scientific disciplines, setting a new standard for future research and applications on Earth and beyond.