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ECE PhD student Padmaja Jonnalagedda wins Award in AI for Space Workshop

Top row - Bir Bhanu and Mary Droser, Second row – Padmaja Jonalagedda and Rachel Surprenant
Top row - Bir Bhanu and Mary Droser
Second row - Padmaja Jonalagedda and Rachel Surprenant

Padmaja Jonnalagedda (VISLab), PhD student of Distinguished Professor Bir Bhanu in Electrical and Computer Engineering (ECE), won the Best Presentation Award at the First Artificial Intelligence for Space (AI4Space) workshop held in conjunction with the Premier IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) in June 2021. The paper is based on a collaborative research between Bir Bhanu’s VISLab in ECE and Mary Droser’s laboratory in Earth and Planetary Sciences (EPSCI) at UCR. The paper is titled as “SPACESeg: Automated Detection of Bed Junction Morphologies Indicating Signs of Life in Ediacaran Period“ co-authored by Padmaja Jonnalagedda (ECE), Rachel Surprenant (EPSCI), Mary Droser (EPSCI) and Bir Bhanu (ECE). The work is geared towards translational astrobiological research to conduct remote and automated computational analysis on terrestrial and extraterrestrial images. The paper performs analysis to extract visual signs of the origin of life on Earth during the Ediacaran period. The terminal Ediacaran Period is most well-known for its exceptional preservation of the first multicellular, community-forming organisms on Earth, known colloquially as the Ediacara Biota (574 - 539 million years). To allow for translational research, they used cross-sectional imaging data from the Flinders region of Australia where the Ediacaran biota is preserved – similar to the kind of images that can be obtained from other planets such as Mars. Their current algorithms outperform most computational methods in detecting the definitive biotic signatures in the sedimentological fossil record.

Recently, NASA Perseverance rover landed on Mars with the purpose of studying the terrain for extraterrestrial life. Due to this increasing astrobiological research interest, the team hypothesizes that using SPACESeg algorithm for detecting and analyzing similar signs from Perseverance images can help to identify signs of potential origins of life on Mars. The team continues to collect data to enhance their suite of algorithms to robustly study the evolution of biosignatures of life on Earth.