Michela Muñoz Fernández can speak four languages, including Italian. That came in handy for NASA’s Juno mission, which has two instruments developed by Italian scientists. Communicating effectively meant more than speaking in their native tongue—she also had to calm troubled waters roiled by cultural differences.
For seven years she strove to keep the teams talking, shuttling back and forth between JPL and Italy and holding teleconferences at the crack of dawn. She gave up all her free time to keep the flow of information steady and free of static, because she knew what was at stake. “Mixed communications can lead to errors, and as we’ve learned with other space missions, minor errors can be catastrophic.”
Due to the international nature of the collaboration she had to become well-versed in export restrictions, learning what she could share openly and what had to remain unsaid. She’s justifiably proud that her efforts resulted in three successful instruments that are now gathering data as Juno orbits Jupiter.
“Mixed communications can lead to errors, and as we’ve learned with other space missions, minor errors can be catastrophic.”
Raised in Madrid, her childhood dream was to work for NASA. Visits to Cape Canaveral when she was a high school exchange student cemented that goal. The Deep Space Network, which keeps in contact with all spacecraft beyond the Moon, fortuitously has a complex in Madrid. She worked for the company that manages the complex, and became entranced by space telecommunications and electrical engineering.
“I wanted to know, how does a machine transmit a message that comes from my mouth? I saw it as a fascinating puzzle, how different signals are propagated, how they become coded, depending on the shape of the antenna.” She used that curiosity during her PhD studies at Caltech to design receivers that could capture weak signals emanating from other planets.
Her quest for coherent communication is comprehensive—from solving relay problems in the Deep Space 1 mission, to easing interactions between different science teams. As the principle investigator on a machine-learning task for Mars Science Laboratory, she’s even using artificial intelligence to catch tiny, imperceptible errors before they can escalate into a complete loss of signal.