X
This website uses cookies to ensure you get the best experience on our website. By continuing to use this website, you consent to our use of these cookies. Learn more

A MAGICal Mentorship

As part of the Independent Science Research elective, Sam D. ‘22 and Mackenzie M. ‘22 secured mentorships with female scientists and engineers through MAGIC (More Active Girls in Computing). With the goal of closing the gender gap in STEM fields, MAGIC offers tools, resources, and guidance for young women who are interested in these areas. Sam and Mackenzie have been working with their mentors since mid-November and will present their original research at MAGIC's annual symposium.

Sam and her mentor Jess Weinberg, a software engineer at Google, are working on a rover to better understand the geology of Mars. The rover uses two Arduino circuit boards, but both Sam and her mentor were unfamiliar with the boards and the Arduino programming language at the start of the project. “It has been extremely rewarding to work through the new material together,” Sam explains. Once the rover is up and running, they plan to transfer data from the rover to a phone or computer via Bluetooth Low Energy (BLE) and then analyze their findings.  “Overall, my MAGIC mentorship has been an amazing experience,” says Sam. My most valued takeaway is learning how to overcome challenges. Debugging (the process of identifying, troubleshooting, and fixing bugs–or problems–in your code) is a large part of programming and the STEM process in general. I hope to major in computer science and/or engineering in college and pursue STEM as a career in the future.”

Mackenzie and her mentor Dr. Jaelle Scheuerman, a postdoctoral fellow at the Naval Research Lab at Stennis Space Center, are focusing on understanding machine learning, specifically as it applies to natural language processing. “I learned how to put together a machine learning model that would learn patterns in text, such as what words tend to follow each other and how words earlier in a sentence would impact the words that come later,” Mackenzie explains. “The result was a model that could generate 'sarcastic' sentences, although most of the sentences generated tend to be more confusing than sarcastic!” She credits her mentor with not only teaching her a lot about machine learning, but also bolstering her self-confidence. “My main goal was to get to a place where I could understand and create a model that could generate text, which felt like an impossibly daunting task at first,” Mackenzie says. “Actually being able to achieve my goal was an incredible experience! I am planning to study computer science in college with the hope of a career in STEM. I feel like this mentorship has really helped cement my interest in the field of machine learning.” 
 
To learn more about their projects, visit the ISR Symposium website for recordings of their presentations and many others. 
Back