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, signal processing, and machine learning.
My research group works on information theory, signal processing, and machine learning. We aim to develop foundational theory that guides the design of machine learning algorithms that do not cause harm. Recently, our work has centered around three critical challenges in machine learning: fairness, privacy, and reliability. Our vision is that information theory is key for the responsible design of machine learning systems and that a theory-guided approach can massively outperform heuristics. You can find more details about our research in the selected publications below.
I consider myself a scientist who is an engineer at heart, and I enjoy doing fundamental research that serves as a design driver for practical applications. My research adopts the information-theoretic blueprint: for a given performance metric (e.g., precision, fairness, privacy), I ask not "How can a given system be made better?" but instead "What is the absolute best a system can be?" I use information theory to characterize fundamental performance limits of machine learning systems, and then design algorithms that try to achieve these limits.
As a Brazilian-American engineer, I am passionate about broadening the participation of students from diverse backgrounds, including from Latin-America, in information theory and machine learning. I am also excited about addressing data-driven challenges specific to the developing world. If this is something that interests you as well, please get in touch!
My research group is supported by the NSF and by generous gifts and awards from Google, IBM, Amazon, and Oracle Research.
There is a post-doc position and Ph.D. positions avaiable in my group for the upcoming year. Please reach out for more information.
I organized a symposium at the 2022 Congresso Nacional de Matemática Aplicada e Computacional in Campinas, São Paulo, Brazil. The workshop was supported in part by a Harvard Brazil Lemann Research Award.
I organized a tutorial at ISIT 2022 on responsible ML together with Haewon Jeong, Shahab Asoodeh, and Mario Diaz. Slides can be found here!
And congratulations for joining the MIT-IBM research lab in Cambridge! You can read Hao's Thesis here.
Read more about Haewon Jeong's exciting work at the SEAS website.
We just kicked-off the research sponsored by a recent NSF and Amazon FAI grant! Very grateful to the National Science Foundation and Amazon for supporting our work.
The annoucnement can be found here.
Extremely honored for receiving the inaugural Título de Honra ao Mérito (Honor to the Merit award) from the University of Brasilia (UnB), my alma mater. This new award recognizes alumni who have achieved national and/or international recognition in their field of study at UnB. I was the first alumni chosen for the award from the area of "technology and exact sciences" (i.e., engineering, math, statistics, and CS).
Honored for receiving a commendation from the Harvard College's Dean of Undergraduate Education for "extraordinary teaching during extraordinary times." Signals & Communications (ES 156) is more important than ever!
Alexa, thank you for sponsoring our work with an Amazon Research Award!
I am once again grateful to National Science Foundation for supporting our work on privacy with an NSF Medium grant and in fair machine learning with an NSF Eager grant.
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!
We are excited to organize a course on ML at FEEC/Unicamp in August 2019! You can find more details here.
Fall 2022: Information Theory (ES 250)
Spring 2022: Covid teaching relief
Fall 2021: Information Theory (ES 250)
Spring 2021: Signals & Communications (ES 156)
Fall 2020: On parental leave from teaching
Spring 2020: Signals & Communications (ES 156)
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
Science & Engineering Complex
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