Event
Ph.D. Dissertation Defense: Abbas Kazemipour
Wednesday, October 25, 2017
1:00 p.m.
Room 1146, AVW Bldg.
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: Ph.D. Dissertation Defense
Name: Abbas Kazemipour
Committee members:
ProfessoMin Wu, Chair
Professor Behtash Babadi, Co-Chair
Professor Prakash Narayan
Professor Radu Balan
Professor Shaul Druckmann (Stanford University and Janelia Research Campus)
Professor Behtash Babadi, Co-Chair
Professor Prakash Narayan
Professor Radu Balan
Professor Shaul Druckmann (Stanford University and Janelia Research Campus)
Dr. Johnathan Fritz
Date/time: Wednesday, October 25, 2017 at 1 pm
Location: AV. Williams Building: ISR seminar room 1146
Title: Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications
Abstract:
Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i.d measurements are at disposal. However real world scenarios typically exhibit non i.i.d and dynamic structures and are confined by physical constraints, preventing applicability of the theoretical guarantees of compressed sensing and limiting its applications. In this thesis we develop new theory, algorithms and applications for non i.i.d and dynamic compressed sensing by considering such real world constraints.
In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes to model dependent temporal structures, namely the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data.
Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution.
Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second.
In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes to model dependent temporal structures, namely the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data.
Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution.
Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second.
