Machine Learning and Instrument Autonomy Group

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Science Serving Data Science

MLIA is leading JPL’s efforts to support scientists in Astronomy, Planetary Science, and Earth Science to gain insight and process-level understanding from large and complex data. Our approach on Data Science for Science Understanding (SUDS) is changing how research is performed across JPL and beyond.

Overview of data science for physical scientists by Dr. Lukas Mandrake at the JPL SUDS Seminar Series

Areas of Expertise

Science Insight from ML	Interpretable Modeling
Large & complex data discovery	Publication-supporting product quality & delivery
Catalog purification & exploration	Explainable, justifiable model selection
Physical & ML model fusion	Interpretable model behavior
Identification of key drivers	Rigorous performance statistics
Bias discovery & correction	Physical model acceleration
Uncertainty Awareness	Global scalability / applicability

Current Projects

Scientific Understanding from Data Science (SUDS) Council

We aim to measurably improve JPL’s scientific understanding and productivity within Astrophysics, Earth and Planetary sciences by infusing data science techniques, such as uncertainty quantification, model-based inference, machine learning, and artificial intelligence, that can directly support science and its applications. The council shall develop a strategy that advances JPL leadership, in collaboration with Campus, in scientific understanding based on state-of-the-art data science methodologies. This council will first evaluate the current state of practice at JPL for scientific understanding from data science methods.

Past Projects

Adaptive Data Processing for Next Generation Radio Arrays

We utilize machine learning methods to enable large-scale radio astronomy data analyses in real-time. Specifically, we focus on the detection and characterization of time-varying sources, one of the major scientific goals of radio arrays.

Agricultural Research Via Interactive, Scalable Technology

We developed and demonstrated a machine learning analysis toolkit that uses Support Vector Machines, clustering, and regression models to identify the connections between weather and agriculture.

MISR Automated Cloud Classification

By comparing images of the same area of the earth from different angles, scientists are able to identify thin clouds and determine approximate cloud heights with unprecedented accuracy.

Mars Target Encyclopedia (MTE)

The Mars Target Encyclopedia (MTE) aims to collect in one place information about observational targets (e.g., rocks, soils) identified and named during Mars surface exploration. The MTE links targets to publications that provide information (e.g., composition, provenance, interpretation) about each target. Mission science team members, planetary science researchers, educators, students, and the general public can find relevant information about targets of interest or compare targets found by different missions. The MTE database will be released through the PDS and searchable through the Analyst's Notebook.