Lisa Rivalin, Ph.D., has extensive experience in research and data science, specializing in building energy efficiency and sustainability. As a Research Engineer and Ph.D. student at ENGIE Axima and Mines ParisTech from April 2012 to May 2016, Lisa developed expertise in applied statistics and building energy. Lisa's career includes significant roles at ENGIE Lab, where comprehensive studies on energy recovery technologies were conducted, and as a Research Scholar at Berkeley Lab, contributing to projects integrating machine learning and physical modeling. Serving as Data Science Technical Lead at ENGIE Axima while also being an Affiliate Research Scholar at Lawrence Berkeley National Laboratory, Lisa architected Smart Building Data Platforms and developed predictive control algorithms. Currently, as a Production System Engineer at Meta, Lisa focuses on sustainability initiatives to minimize the company's carbon footprint through advanced data science and AI methods. Prior experiences as a private tutor and a community worker highlight a commitment to education and community development.
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