Dissertation Defense: Jordi Abante
May 10 @ 3:00 pm
Dissertation Defense: Jordi Abante

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Statistical Signal Processing Methods for Epigenetic Landscape Analysis

Abstract: Since the DNA structure was discovered in 1953, a great deal of effort has been put into studying this molecule in detail. We now know DNA comprises an organism’s genetic makeup and constitutes a blueprint for life. The study of DNA has dramatically increased our knowledge about cell function and evolution and has led to remarkable discoveries in biology and medicine.

Just as DNA is replicated during cell division, several chemical marks are also passed onto progeny during this process. Epigenetics studies these marks and represents a fascinating research area given their crucial role. Among all known epigenetic marks, 5mc DNA methylation is probably one of the most important ones given its well-established association with various biological processes, such as development and aging, and disease, such as cancer. The work in this dissertation focuses primarily on this epigenetic mark, although it has the potential to be applied to other heritable marks.

In the 1940s, Waddington introduced the term epigenetic landscape to conceptually describe cell pluripotency and differentiation. This concept lived in the abstract plane until Jenkinson et al. 2017 & 2018 estimated actual epigenetic landscapes from WGBS data, and the work led to startling results with biological implications in development and disease. Here, we introduce an array of novel computational methods that draw from that work. First, we present CPEL, a method that uses a variant of the original landscape proposed by Jenkinson et al., which, together with a new hypothesis testing framework, allows for the detection of DNA methylation imbalances between homologous chromosomes. Then, we present CpelTdm, a method that builds upon CPEL to perform differential methylation analysis between groups of samples using targeted bisulfite sequencing data. Finally, we extend the original probabilistic model proposed by Jenkinson et al. to estimate methylation landscapes and perform differential analysis from nanopore data.

Overall, this work addresses immediate needs in the study of DNA methylation. The methods presented here can lead to a better characterization of this critical epigenetic mark and enable biological discoveries with implications for diagnosing and treating complex human diseases.

Committee Members

  • John Goutsias, Department of Electrical and Computer Engineering
  • Archana Venkataraman, Department of Electrical and Computer Engineering
  • Sanjeev Khudanpur, Department of Electrical and Computer Engineering
Dissertation Defense: Arun Nair
May 25 @ 12:30 pm
Dissertation Defense: Arun Nair

Note: This is a virtual presentation. Check this page at a later date for the Zoom link to where the presentation will be taking place.

Title: Machine Learning for Beamforming in Audio, Ultrasound, and Radar

Abstract: Multi-sensor signal processing plays a crucial role in the working of several everyday technologies, from correctly understanding speech on smart home devices to ensuring aircraft fly safely. A specific type of multi-sensor signal processing called beamforming forms a central part of this thesis. Beamforming works by combining the information from several spatially distributed sensors to directionally filter information, boosting the signal from a certain direction but suppressing others. The idea of beamforming is key to the domains of audio, ultrasound, and radar.

Machine learning is the other central part of this thesis. Machine learning, and especially its sub-field of deep learning, has enabled breakneck progress in tackling several problems that were previously thought intractable. Today, machine learning powers much of the cutting edge systems we see on the internet for image classification, speech recognition, language translation, and more.

In this dissertation, we look at beamforming pipelines in audio, ultrasound, and radar from a machine learning lens and endeavor to improve different parts of the pipelines using ideas from machine learning. We start off in the audio domain and derive a machine learning inspired beamformer to tackle the problem of ensuring the audio captured by a camera matches its visual content, a problem we term audiovisual zooming. Staying in the audio domain, we then demonstrate how deep learning can be used to improve the perceptual qualities of speech by denoising speech clipping, codec distortions, and gaps in speech.

Transitioning to the ultrasound domain, we improve the performance of short-lag spatial coherence ultrasound imaging by exploiting the differences in tissue texture at each short lag value by applying robust principal component analysis. Next, we use deep learning as an alternative to beamforming in ultrasound and improve the information extraction pipeline by simultaneously generating both a segmentation map and B-mode image of high quality directly from raw received ultrasound data.

Finally, we move to the radar domain and study how deep learning can be used to improve signal quality in ultra-wideband synthetic aperture radar by suppressing radio frequency interference, random spectral gaps, and contiguous block spectral gaps. By training and applying the networks on raw single-aperture data prior to beamforming, it can work with myriad sensor geometries and different beamforming equations, a crucial requirement in synthetic aperture radar.

Committee Members

  • Trac Tran, Department of Electrical and Computer Engineering
  • Muyinatu Bell, Department of Electrical and Computer Engineering
  • Vishal Patel, Department of Electrical and Computer Engineering
Dissertation Defense: Takeshi Uejima
May 28 @ 9:00 am
Dissertation Defense: Takeshi Uejima

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: A Unified Visual Saliency Model for Neuromorphic Implementation

Abstract: Although computer capabilities have expanded tremendously, a significant wall remains between the computer and the human brain. The brain can process massive amounts of information obtained from a complex environment and control the entire body in real time with low energy consumption. This thesis tackles this mystery by modeling and emulating how the brain processes information based on the available knowledge of biological and artificial intelligence as studied in neuroscience, cognitive science, computer science, and computer engineering.

Saliency modeling relates to visual sense and biological intelligence. The retina captures and sends much data about the environment to the brain. However, as the visual cortex cannot process all the information in detail at once, the early stages of visual processing discard unimportant information. Because only the fovea has high-resolution imaging, individuals move their eyeballs in the direction of the important part of the scene. Therefore, eye movements can be thought of as an observable output of the early visual process in the brain. Saliency modeling aims to understand this mechanism and predict eye fixations.

Researchers have built biologically plausible saliency models that emulate the biological process from the retina through the visual cortex. Although many saliency models have been proposed, most are not bio-realistic. This thesis models the biological mechanisms for the perception of texture, depth, and motion. While texture plays a vital role in the perception process, defining texture in a mathematical way is not easy. Thus, it is necessary to build an architecture of texture processing based on the biological perception mechanism. Binocular stereopsis is another intriguing function of the brain. While scholars have evaluated many computational algorithms for stereovision, pursuing biological plausibility means implementing a neuromorphic method into a saliency model. Motion is another critical clue that helps animals survive. In this thesis, the motion feature is implemented in a bio-realistic way based on neurophysiological observation.

Moreover, the thesis will integrate these processes and propose a unified saliency model that can handle 3D dynamic scenes in a similar way to how the brain deals with the real environment. Thus, this investigation will use saliency modeling to examine intriguing properties of human visual processing and discuss how the brain achieves this remarkable capability.

Committee Members

  • Ralph Etienne-Cummings, Department of Electrical and Computer Engineering
  • Andreas Andreou, Department of Electrical and Computer Engineering
  • Philippe Pouliquen, Department of Electrical and Computer Engineering
  • Ernst Neibur, Department of Neuroscience
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