The Machine Learning and Instrument Autonomy strives to research, develop, and infuse data driven solutions machine learning and other data science supporting technologies to advance robotic exploration and science.
Recent News
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▸ June 12th, 2024 by Mario Damiano
Mario Damiano, Aaron Bello-Arufe, Jeehyun Yang, and Renyu Hu published the paper entitled LHS 1140 b is a potentially habitable water world to the Astrophysical Journal Letters. The paper describe the data analysis of JWST observations, scientific interpretation, and scientific impact of the results.
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▸ June 4th, 2024 by Philip Brodrick
Phil Brodrick was the JPL lead on the publication of a paper led by Longlei Li (Cornell University) entitled “Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols”, in Nature Communications Earth and Environment. The work shows how new lab measurements of dust composition help constrain global radiative forcing, and demonstrates the need for improved remote measurements of surface minerals.
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▸ April 11th, 2024 by Mario Damiano
Dr. Mario Damiano was invited to be members of the Steering Committee (SC) of the Habitable Worlds Observatory (HWO) Characterizing Exoplanet Working Group (WG). In addition, Mario Damiano was invited to co-lead the development of science case(s) around the detection and characterization of water worlds. The Characterizing Exoplanet WG charter is to “Investigate HWO science cases related to remote sensing observations of exoplanets using high-contrast imaging and UV/optical/near-infrared spectroscopy, as well as transit spectroscopy.”
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▸ April 9th, 2024 by Ryan McGranaghan
Dr. Ryan McGranaghan gave the Early Career Keynote Presentation at the American Geophysical Union (AGU) Triennial Earth-Sun Summit entitled, “Complexity Heliophysics: A lived and living history and its grand opportunity.” The talk presented the lived and living history of complexity science as a paradigm of scientific discovery in Earth and space science, arriving at three pathways that this paradigm makes clear are a future for these fields: 1) Complexity – AI: understanding the connections between complexity science and machine learning/artificial intelligence; 2) Risk science and resilience: A ‘risk science’ framework can be a layer where fundamental understanding and predictive capability converge; and 3) Cultural challenges: interdisciplinary work requires new capacities for facilitation and team composition as well as new capabilities for integrating information, which together require new ways of ‘looking’ at a community (e.g., indicators and metrics).