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Insight Edge (Sumitomo Corp.) / Planning Team Member
For my PhD I used statistics, machine learning, and massive astronomical image data to explore what our galaxy is made of.
Curiosity led me to double-major in biology and physics. Finally, I ended up diving into astronomical data science during grad school, studying the molecules floating around in space that can eventually become planets, and even living things.
Working in an extremely diverse team to leverage AI, the Google Cloud Platform, and scientific guidance to probe the edge astronomy and biology. My role in the team, as an astronomy PhD with experience machine learning, is as a conduit between the scientists and Ai engineers. My technical input during the program was valuable in developin
This project is in collaboration with GRID, Inc. We are applying a variety of machine learning techniques to massive, all-sky astrophysical data sets. Our goal is to facilitate breakthroughs in big-astrophysical-data. This field suffers from two major limitations that we think machine learning can help with: 1) Heavy reliance on conventional models (which make very strong assumptions) 2) Too much data! We have more information coming in from satellites and telescopes than we are able to efficiently and thoroughly analyze.
I double-majored in biology and physics to build a broad platform of knowledge about natural sciences. For example, I took elective classes in ecology and neuroscience due to their interdisciplinary nature. My major interest was in studying connections between biology, chemistry, physics, and astronomy. In my last year and a half, I start
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株式会社Ridge-i / エンジニア 博士(天文学) ベルアーロン
Homepage for he "Challenges in Machine Learning" Workshop held in Montreal at NeurIPS 2018. Presented on behalf of my NASA FDL Astrobiology Team 1 teammates and mentors. CMiL selected our work for a poster presentation, as a "New Impactful Challenge". Learn more about the workshop here: http://ciml.chalearn.org/ciml2018/schedule
pos.sissa.it
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