Michelle finished her undergraduate degree in Computer Science and Engineering from MIT in 2018, and completed Master’s of Engineering in Computer Science from MIT in 2019. As an undergrad, she minored in Energy Studies, and a large part of her motivation to continue her education was to merge her interests of computer science and energy. For her thesis work, she worked with Professor Marija Ilic and Rupamathi Jaddivada on designing smarter grid systems. A majority of this work focused on improving household-level, short timescale energy prediction and exploring the value of machine learning approaches compared to statistical and mathematically computed approaches; this work showed the value of using these different types of approaches to complement each other. Additional work was done to demonstrate a possible system design for an implementation of the DyMonDS framework. This design was one possible architecture that was intended to address the broader issue of the increasing need for cybersecure systems, and to evaluate blockchain as a potential tool for smart grid systems. During her time working with EESG, we published two conference papers that parallel the two bodies of work described above: “Household Energy Predictions: Methods and Applications for Smarter Grid Design” and “Secure Blockchain-Enabled DyMonDS Design,”. She is currently a software engineer at Redfin in Seattle, but energy continues to be something that she is passionate about. Outside of work, you can find her running, reading fantasy novels, baking, and exploring coffeeshops.