In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...
The classical store-and-forward routing has and will continue to be the most important routing architecture in many modern packet-switched communication networks. In a packet-switched network, data is sent in the form of discrete packets that traverse hop-by-hop from a source to a destination. At each intermediate hop, the router stores...
TAC is the most commonly used medication in post transplantation maintenance immunosuppression therapy. TAC lipophilicity and its erratic oral absorption especially in the presence of food intake result in great intra- and interpatient pharmacokinetic variations. Complicated dosing and frequent required therapeutic monitoring is thought to be the main cause of...
This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
Delta-sigma analog-to-digital converters traditionally have been used for low speed, high resolution applications such as measurements, sensors, voice and audio systems. Through continued device scaling in CMOS technology and architectural and circuit level design innovations, they have even become popular for wideband, high dynamic range applications such as wired and...
The Machine Learning (ML) algorithms are increasingly explored in varies of fields including designing and optimizing computer systems. Recent research, such as optimizing memory/cache prefetching by ML training or predicting traffic pattern in throughput processors, also exhibits a promising future of introducing ML into computer system design and optimization. Throughput...
Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...
The global demand for food is expected to double by 2050, presenting a need that is complicated by the many interrelated of pressures on the world’s natural resources from climate change, growing urban populations, and increasing development. As one of the fastest growing food production sectors, aquaculture is poised to...
Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using learned models, i.e., Software-2.0, has gained substantial adoption. However, we do not know what challenges developers encounter when they use ML libraries. With this knowledge gap, researchers miss opportunities to contribute to new research directions, tool builders do...
River basins provide essential services for both humans and ecosystems. Understanding the connections between ecosystems and society and their function has been at the heart of resilience studies and has become an increasing important endeavor in research and practice. In this dissertation, I define basin resilience as a river basin...