Annabel Reyna
Annabel Reyna Hello, my name is Annabel Reyna Gomez! I am a sophomore from the San Francisco Bay Area (home of the Silicon Valley) pursuing a major in Mechanical Engineering with a minor in Aerospace Engineering. On campus, I am a member of Ruddock House, Club Latino, Women Mentoring Women, a Caltech Y Rise Tutor, and an Admissions Ambassador. Off campus, I intern at NASA’s Jet Propulsion Laboratory (JPL) researching and predicting ionospheric irregularities using machine-learning. During my free time, I enjoy 3D printing, watching movies with my friends, and reading personal-development books.

An Interview with a JPL Mentor!

An Interview with a JPL Mentor!

Over the past several months, I have had the opportunity to intern at the NASA Jet Propulsion Laboratory (JPL) under the mentorship of senior research technologist Dr. Xiaoqing Pi. Dr. Pi’s guidance and mentorship has been instrumental to the development and success of my internship at JPL, where I use machine-learning to enhance the accuracy and integrity of navigation and communication signals. In addition to helping me develop an understanding of atmospheric and ionospheric remote sensing and machine-learning, Dr. Pi has often offered his insights on how to improve my researching skills. Dr. Pi was generous enough to take the time to answer a few questions regarding his research and advice for future student interns. I believe many students can benefit from some of the lessons that he has taught me:

Q: What does your research consist of?

Dr. Xiaoqing Pi: “I have been conducting research on space physics, and remote sensing and modeling of the Earth’s ionosphere. Space physics is a science discipline about physics and systematic interactions between solar wind, interplanetary magnetic field, Earth’s magnetic field, magnetosphere, ionosphere, and thermosphere. The same principles apply to the Sun and other planets, and to other stars and their planets. The ionosphere is the ionized portion of the upper atmosphere at altitudes between about 100 to 1000 kilometers (the layer above 1000 km is called the plasmasphere). It is created by photoionization of the upper atmosphere due to solar EUV radiation. The density, temperature, and velocity of ionospheric charged particles (ions and electrons, or we also call them plasma) vary with solar EUV radiation intensity, the radiation optical path in the atmosphere, atmospheric constituents, and ionospheric dynamics. The ionosphere affects radio signals in ways to delay signals, and sometimes causes random fluctuations in signals, which are called ionospheric scintillation. This occurs if the signals pass through regions of irregularly distributed electron densities or ionospheric irregularities. The signal delay and scintillation effects have an impact on technology systems that rely on radio signals at HF through L-band. Examples include short wave radios, Global Navigation Satellite System (GNSS, including GPS, GLONASS, BeiDou, and Galileo), satellite-based Earth remote sensing radars, etc. Research on space physics helps to advance our understanding of mechanisms of ionospheric variations and disturbances. Development of remote sensing techniques and models helps to augment our observation capability and to enhance the reliability and accuracy of the technology systems”.

Q: What do you enjoy the most about your field of study?

Dr. Xiaoqing Pi: “What I enjoy the most about my work is that I can pursue scientific discoveries and also conduct development to enhance technology systems. In my study field, I have had chances to conduct research and develop techniques to support satellite-based augmentation system (the Wide Area Augmentation System – WAAS as an example, which is the U.S. system to augment GNSS for aviation navigation), satellite-based interferometric synthetic aperture radars, ocean altimeters, deep space navigation, etc”.

Q: What are some projects you are currently working on

Dr. Xiaoqing Pi: “I am currently conducting several research projects. One of them is to develop techniques to detect, measure, and remove ionospheric effects globally in satellite-based interferometric synthetic aperture radar imagery. This research supports the upcoming NASA-ISRO SAR mission (NISAR). Another is to analyze scintillation effects on GNSS signals and to develop capability of predicting ionospheric irregularities and scintillation. This research supports the U.S. Wide Area Augmentation System (WAAS). I am also further extending a Global Assimilative Ionospheric Model (GAIM) to estimate ionospheric conditions under which ionospheric irregularities can be triggered. This is a research project under NASA’s Science Mission Directorate Living With a Star (LWS) program”.

Q: How has your experience been with interns? What do you enjoy about being a mentor?

Dr. Xiaoqing Pi: “I have had chances to work with several interns in the past, right now, and in the coming summer at either undergraduate or graduate level. These interns are very excited and enthusiastic about the research projects that they get into. Although the research fields are fresh to them, they are all eager to learn the phenomena, physical processes, and technology, and to contribute to the projects. As their mentor, I am happy to see that they are very motivated to face challenges and pursue solutions in order to reach the research goals”.

Q: What advice do you have for current undergraduates regarding research and internships?

Dr. Xiaoqing Pi: “Undergraduate students should not shy away from challenging projects and tasks. They should prepare themselves with required basic skills and by reading relevant references about the research fields. They should follow their mentor’s advice, and actively look for solutions when encountering problems. Take my current research that recruits undergraduate interns through SURF, as an example, the research involves machine learning techniques and Python software. The interested students should be familiar with Python programming skills and familiar with Python-based machine learning libraries as well as their various applications. In addition, students should be prepared to process a large amount of data using Python programs. Different projects require different skills. Interested students may follow mentors’ guidance and suggestions”.

Q: Why should students apply for and/or participate in NASA internships?

Dr. Xiaoqing Pi: “NASA’s research projects are of frontier research. Your mentors are often the front runners of the fields. Being an intern at a NASA center, you will surely learn exciting space science and cutting-edge technologies. Take my current research as an example, one of the major challenges in NASA’s Heliophysics Division research programs is to predict occurrence and intensity of ionospheric irregularities. The irregularities can cause scintillation in trans-ionospheric radio signals, which fades signal power and degrades signal quality. The occurrence and intensity of ionospheric irregularities involve complicated coupling processes between ionized and neutral gas in the upper atmosphere, including their dynamics controlled by the geomagnetic field and impact of solar wind perturbations on the ionosphere and thermosphere. Prediction of the irregularities is needed in certain technology applications, such as satellite-based aviation navigation and radio communications in military operations, as examples. This is an area where machine learning may play a role to solve the problem”.

Q: What are some qualities you look for when selecting an intern?

Dr. Xiaoqing Pi: “When selecting interns, I look for their enthusiasm about the project, preparedness of the required skills and knowledge about the field, eagerness of learning, good communications, active pursuit of solutions, and team work spirit. In terms of knowledge, one of my research projects requires knowledge of machine learning and its applications, as well as software tools that can be used for machine learning projects. There are some good machine learning libraries written in Python so that Python programming skills are very helpful. In addition, when conducting a specific machine learning task, a large amount of historical data sets is required. With Python programming skills, students can write programs to organize, process and analyze the data and outcome”.

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