When Elence Xinzhu Chen was young, she wanted to be an entrepreneur.

While applying for her undergraduate degree, Chen hoped to go to business school. Her family advised her to pursue engineering or science. She applied to both. After a mishap with her passport and a missed interview, Chen found herself at the National University of Singapore, where she received a Bachelor of Science in Project and Facilities Management.

“Despite my fascination with the achievements of prosperous entrepreneurs, I have discovered that my true ardor lies in the fields of engineering, science, and technology applications – how technology can be applied to solve real problems in the world, or change the way people live,” said Chen.

Chen then received a master’s in design studies with a concentration in Energy and Environment from the Harvard University Graduate School of Design (GSD). Now, as a doctoral candidate at the GSD specializing in building technology, data science, and engineering, and a core researcher at the Harvard Center for Green Buildings and Cities (CGBC), Chen focuses most of her work on machine learning-based model predictive building control.

“Buildings are connected through sensors, and effectively managing them is the most complex automation challenge, as big buildings might reach a million sensors, control points, and IoT devices,” said Chen. “I focus on developing and optimizing control algorithms through the machine learning approach – I create predictive models that simulate building dynamics and responses, which allow the building systems to learn from data using their own autonomous decision-making capabilities.”

Chen’s motivation for working with smart buildings was not only her passion for engineering, but also the important impacts this work could have on climate change and issues of sustainability.

“Buildings consume about 40% of energy in terms of global energy consumption. They need to be controlled in better ways, both individually and collectively, in order to support carbon reduction,” said Chen. “When I’m conducting research, I think of three things: feasibility, scalability, and applicability of research methods. I might be answering a specific, niche problem, but I want the result to have some impact on the real-world applications.”

Chen divides her time between her research and her role as a teaching fellow at the GSD. In her free time, Chen enjoys painting, online shopping, and watching pitch stories from successful entrepreneurs. She also loves to spend time outside, either kayaking or hiking in the White Mountains.

Above all, Chen is not afraid of a challenge. As she confronts her research, teaching, and passions, she does not simply endure obstacles. Instead, she looks forward to them. For Chen, the most rewarding results of her research have often come from challenging methods. She encourages others to look at research from a similar perspective.

“I love the challenging parts of my work – the obstacles that I dig into and solve independently, and the process of addressing difficulties and learning from them,” Chen said. “Do what you are passionate about. No matter what you do, you will always face challenges. If you don’t enjoy your work, you might not take the initiative in dealing with these difficulties.”

 

Elence Xinzhu Chen has published several research papers, such as, “Ensembled Deep Learning-based Model Predictive Control for Automatic Window Operations in Winter” and “Data-Informed Building Energy Management (DiBEM) Towards Ultra-Low Energy Buildings,” which can be viewed under our Publications page.