Dr. Chris Mattmann
IT Chief Technology and Innovation Officer at NASA JPL
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What I Do
Chris Mattmann is the IT Chief Technology and Innovation Officer (CTIO) at NASA JPL. He is the Division Manager of the AI, Analytics, and Innovative Development Organization in the Information Technology and Solutions Directorate at NASA JPL. Ensuring that cutting-edge technologies support NASA’s science missions.
Mattmann is the Division Manager of the AI, Analytics, and Innovative Development Organization in the Information Technology.
Recent Media
Featured in over 124 media articles including ABC, NBC, WIRED, The New York Times, Quanta Magazine including 2013 Feature with Dave Schimel on “Big Data Is Too Big for Scientists to Handle Alone”.
Career Highlights
and International Recognition
- 20 years at JPL. Principal scientist (first in the area of "Data Science") and internationally recognized expert in data science, cybersecurity, AI, informatics to multiple domains including Space Science, Earth and Planetary Science, and other industries.
- At JPL, conceived and architected and implemented the next generation of Earth Science ground data systems using open source and Apache software from 2005-2009. Delivered and operational on OCO/OCO2 and ACOS, NPP Sounder PEATE, SMAP and developed pipeline to infuse tech across NASA ROSES research ACCESS, AIST and other efforts.
- Inventor, Apache Tika. Wrote definitive guide book "Tika in Action" in 2011 and in 2017 Tika was the key technology to solving the Panama Papers which won the Pulitzer Prize in Journalism.
- Highlighted in 2015 NBC News Story on DARPA MEMEX helping to define a new Internet and stopping Human Trafficking (Lolita Lopez, NBC News) and credited with materially contributing to research in the Deep Web and Dark Web.
Featured Book
Machine Learning with TensorFlow, Second Edition
A fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10.
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