Ariana Mann

Ariana is a Ph.D. student in the Department of Electrical Engineering, advised by Nicholas Bambos.  The focus of her work is on the design and analysis of computational systems under data location and power constraints. Examples include: decentralized, power-limited on-device learning, power-controlled processor rate for carbon footprint minimization, and energy-optimized fault tolerance for large-scale machine learning. Ariana previously spent four years working at startups as a data engineer and before that received her S.B. in Physics from MIT. She is broadly interested in tools and frameworks to lower the barrier to entry for the use of (efficient) data science and machine learning.