Victoria Da Poian

Machine Learning (ML) tools for MOMA data

Developing machine learning and data analysis tools for mass spectrometry data from Exomars MOMA (Mars Organic Mass Analyzer) mass spectrometer instrument.

MOMA Instrument Software Operator

Technology & Missions

Mass Spectrometry Science Autonomy for Ocean Worlds

This group (led by Dr. Theiling) is a new cooperative network spanning mass spectrometer instrument developers, ocean worlds laboratory analog experimentalists, and machine learning experts in an effort toward developing science autonomy for ocean worlds missions. We are working to: 1) develop flight instruments that can function autonomously in order to achieve established science goals and 2) develop consistent and streamlined methods for interpreting data from flight mass spectrometers. 

Use Transfer Learning on Limited Datasets (NASA@Work Award)

This NASA@Work award aims at developing transfer learning applications in planetary science missions. Indeed, flight instruments have limited data volumes, making it challenging to apply machine learning techniques.

https://nasa-at-work.nasa.gov/a/dtd/Using-NASA-Science-Data-and-Citizen-Science-to-Build-Open-Access-databases-through-Transfer-Learning/23232-51

DraMS Ground Software

Technology & Missions

NASA Visiting Scientist

NASA Goddard Space Flight Center - NASA Goddard Space Flight Center, Greenbelt

June 2019 - Present

• Dragonfly mission: Laboratory work on Titan analog samples (preparation and measurements using laser desorption mass spectrometers: Thermo Scientific LTQ XL, Bruker Autoflex Max).
• ExoMars mission: Development of machine learning and data analysis tools on MOMA (Mars Organics Molecular Analyzer) mass spectrometer data to assist the MOMA science team in their decision-making process during ExoMars mission surface operations.
• Goddard Instrument Field Team: In charge of hXRF (handled XRay Fluorescence) spectrometer experiments in the San Francisco Volcanic Field during field campaign with Drs. Kelsey Young and Jacob Richardson.
• Laboratory for Agnostic Biosignatures (LAB): Conduct research with Drs. Heather Graham and Paul Mahaffey focusing on MSMS data to categorize samples using different spectra analysis tools and machine learning process.
• MOMA ETU analysis: Conduct research in collaboration with Drs. Xiang Li, Andrej Grubisic and other NASA and external collaborators on mass spectrometry analysis both on commercial instrument and in-flight spare ones.
• Titan atmosphere chamber: Carry out GCMS analysis on gaseous samples to elucidate the aerosol formation in the carbon fractionation of Titan atmosphere.

CNES Aerospace Engineering Intern

CNES CSG (Centre Spatial Guyanais) - Kourou, French Guiana

March 2018 - August 2018

Five-month internship within Flight Safety department working on the development of a decision-making tool using Machine Learning and Data Science for the flight control department in charge of the security of people and environment during Ariane5, Vega or Soyouz launches

Exchange semester at Bauman Moscow State Technical University (BMSTU)

Bauman Moscow State Technical University (BMSTU) - Moscow

2019 - Present

• Aerospace (propulsion, flight dynamics) and Russian language courses.
• Scientific work on worldwide space agencies project: the Deep Space Gateway, research focusing on the proof of concept of a astronaut collaborative rover for Lunar missions

ESA Aerospace Engineering intern

ESA/EAC (European Space Agency / European Astronaut Center) - Koln, Germany

March 2017 - September 2017

Five-month internship, working on analog human-telerobotics simulations in the SpaceShip EAC Project:

• development and teleoperation of a lunar analogue rover aiming at being astronaut’s buddy for Lunar exploration
• modelling a Soyuz replica for modern simulation (architecture)