About

Grace Connors is a Software Engineer at Collimator and a Lecturer at MIT. With over 5 years of relevant experience, she specializes in building modern cloud-native engineering modeling and simulation software. Grace has a Master of Science degree in Mechanical Engineering from Massachusetts Institute of Technology (MIT) and a Bachelor of Science degree in Mechanical Engineering from the same institution. She also attended Bronxville High School. Prior to her current role, Grace worked as a Toy Design Instructor at MIT where she led a team of 6 students in ideating, designing, and manufacturing a new toy design over the course of 3 months. She provided assistance in brainstorming and building to her students, and troubleshooted alongside them as problems came up during prototyping. Grace also taught 6 students about Mechanical Engineering concepts including machining, finishing parts, and the iterative design process through play-testing. She was also a Graduate Researcher in GEAR Lab at MIT where she built a novel controller for a photovoltaic electrodialysis water desalination system to optimally use solar irradiance available every day, splitting between direct energy use and battery storage, to maximize system reliability and minimize operational cost by using machine learning and optimal control theory. She developed system level optimization tools for determining the optimal system design for water desalination systems depending on geography, water consumption, and feed water conditions. Grace implemented neural network machine learning algorithms to predict the available solar irradiance based on the system’s geography to inform the optimal control strategy. She elucidated design requirements for photovoltaic electrodialysis water desalination systems based on customer and user interviews and market research. Grace also performed detailed system design and built a prototype of the optimal system to perform experimental testing of control system to validate the system model, optimization theory, and controller. She managed a team of 8 research scientists and other graduate students through test planning and execution in Alamogordo, NM. Grace won the Judge’s Choice for an Elegant Solution for the JWAFS World Water Day Video Challenge 2021. Grace's tech stack includes Software Engineer, test, Research Scientist, ML, and Neural Networks. She is a highly motivated and skilled engineer who is passionate about using her knowledge and skills to build innovative solutions that solve complex problems.