I am an Assistant Professor of Electrical Engineering at Harvard's John A. Paulson School of Engineering and Applied Sciences. Before joining Harvard I was a social good post-doctoral fellow at IBM Research in Yorktown Heights, New York. I received my Ph.D. in Electrical Engineering and Computer Science at MIT. My main research interests are information theory, inference, and statistics, with applications to privacy, fairness, machine learning, and communications engineering.
My research has three intertwined goals: (i) develop theory and models that capture the fundamental limits of estimation and learning from data, (ii) construct fair and private learning algorithms with performance guarantees based on these limits, and (iii) use this methodology as a design driver for future information processing and content distribution systems. In order to achieve these goals I use theoretical tools from information theory, statistics, cryptography and machine learning.
I consider myself a scientist who is an engineer at heart, so I enjoy doing fundamental research that serves as a design driver for practical applications. I have a broad set of interests which include information theory, statistics, communications and optimization. You can find more details in the publications below.
Ok Google, thank you so very much for your generous gift (see under "Machine Learning and Data Mining").
I am very grateful for the support of the National Science Foundation for our research on information-theoretic foundations of fair machine learning (check out this GSAS feature). You can learn more details about the award at the Harvard SEAS website.
Thank you IBM!
Thank you NVidia for the two Titan XP GPUs awarded to our group.
We are excited to organize a course on ML at FEEC/Unicamp in August 2019! You can find more details here.
Fall 2019: Information Theory (ES 250)
Spring 2019: Signals and Systems (ES 156)
Spring 2018: Signals and Systems (ES 156)
Fall 2017: Information Theory (ES 250)
Email: flavio (at) seas (dot) harvard (dot) edu
33 Oxford St.
Office MD 347