Theory Seminar

What can a bit tell us? Forays into information scalability.

Petros T. BoufounosPrincipal Member of Research StaffMitsubishi Electric Research Laboratories

Signal representation theory and practice has focused on how to best
represent a signal as efficiently as possible and minimize the
distortion on the signal incurred by the representation. However, in
many applications the processing stage only requires the extraction of
specific information from the signal and the signal itself is not
necessarily of interest. In such applications the representation
should be information scalable, i.e., adaptable to efficiently
represent only the information required by the processing.

In this talk, motivated by image-retrieval applications, we
demonstrate that such information scalability can be achieved using
appropriately designed signal embeddings. Combined with quantization,
these embeddings are a perfect fit for inference applications with
storage, processing or communication constraints, such as augmented
reality. These embeddings capture all or part of the geometry of the
signal space, as required for inference, at a very low bit-rate.
Thanks to this property we can reduce the storage or transmission rate
in image retrieval applications by more than 50%, when compared to
existing approaches.
Petros T. Boufounos is a Principal Member of Research Staff at
Mitsubishi Electric Research Laboratories (MERL) and a visiting
scholar at the Rice University Electrical and Computer Engineering
department. Dr. Boufounos completed his undergraduate and graduate
studies at MIT. He received the S.B. degree in Economics in 2000, the
S.B. and M.Eng. degrees in Electrical Engineering and Computer Science
(EECS) in 2002, and the Sc.D. degree in EECS in 2006. Between
September 2006 and December 2008, he was a postdoctoral associate with
the Digital Signal Processing Group at Rice University. Dr. Boufounos
joined MERL in January 2009.
Dr. Boufounos' immediate research focus includes signal acquisition
and processing, quantization and data representations, frame theory,
and machine learning applied to signal processing. He is also
interested into how signal acquisition interacts with other fields
that use sensing extensively, such as robotics and mechatronics. Dr.
Boufounos is an associate editor at IEEE Signal Processing Letters. He
has received the Ernst A. Guillemin Master Thesis Award for his work
on DNA sequencing, the Harold E. Hazen Award for Teaching Excellence,
both from the MIT EECS department, and has been an MIT Presidential
Fellow. He is also a senior member of the IEEE and a member of Sigma
Xi, Eta Kappa Nu, and Phi Beta Kappa.

Sponsored by

Anna Gilbert