Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, causing nearly 25% of deaths in the United States. Despite the efforts to create in vitro models for the study and treatment of CVDs, these are still limited in their recapitulation of the heart tissue. Thus, the engineering of accurate cardiac models is imperative to gain more understanding and improve the outcome of CVDs. This Ph.D. dissertation focuses on the development and characterization of isogenic cardiac organoids derived from human induced pluripotent stem cells (hiPSCs). Additionally, the integration of chemical and biological cues for enriching their microenvironment and promoting their maturation state and function were studied. First, hiPSC-derived cardiac cells were utilized for the fabrication of multicellular spherical microtissues, namely isogenic cardiac organoids. The cellular composition and culture time of the engineered tissues were optimized to induce cellular aggregation and the formation of cell-cell interactions. Also, ribbon-like gold nanoparticles, namely gold nanoribbons (AuNRs), were synthesized, characterized, and biofunctionalized for their integration into the isogenic cardiac organoids. In-depth biological evaluation of the organoids showed enhanced cardiac maturation markers. Furthermore, a supplement-free cell culture regime was designed and evaluated for fabricating isogenic cardiac organoids. Mechanistic, cellular, and molecular-level studies demonstrated that the presence of hiPSC-derived cardiac fibroblasts (hiPSC-CFs) significantly improves the morphology and gene expression profile of the organoids. Electrophysiological-relevant features of the organoids, such as conduction velocity (CV), were further investigated utilizing a microelectrode array (MEA) platform. It was shown that MEA offers a simple, yet powerful approach to assessing electrophysiological responses of the tissues, where a trend in decreased CV was found due to the presence of hiPSC-CFs. Overall, this dissertation has a broad impact casting light on the development strategy and biological mechanisms that govern the formation and function of isogenic cardiac organoids. Moreover, this study presents two unique approaches to promote maturation of stem cell-derived cardiac organoids: 1) through the integration of novel biofunctionalized nanomaterials, and 2) through a cell culture regime, leading to enhanced function of the organoids. The proposed micro-engineered organoids have broad applications as physiologically relevant tissues for drug discovery, CVDs modeling, and regenerative medicine.

In many real-world machine learning classification applications, well labeled training data can be difficult, expensive, or even impossible to obtain. In such situations, it is sometimes possible to label a small subset of data as belonging to the class of interest though it is impractical to manually label all data not of interest. The result is a small set of positive labeled data and a large set of unknown and unlabeled data. This is known as the Positive and Unlabeled learning (PU learning) problem, a type of semi-supervised learning. In this dissertation, the PU learning problem is rigorously defined, several common assumptions described, and a literature review of the field provided. A new family of effective PU learning algorithms, the MLR (Modified Logistic Regression) family of algorithms, is described. Theoretical and experimental justification for these algorithms is provided demonstrating their success and flexibility. Extensive experimentation and empirical evidence are provided comparing several new and existing PU learning evaluation estimation metrics in a wide variety of scenarios. The surprisingly clear advantage of a simple recall estimate as the best estimate for overall PU classifier performance is described. Finally, an application of PU learning to the field of solar fault detection, an area not previously explored in the field, demonstrates the advantage and potential of PU learning in new application domains.

This dissertation proposes and presents two different passive sigma-delta

modulator zoom Analog to Digital Converter (ADC) architectures. The first ADC is fullydifferential, synthesizable zoom-ADC architecture with a passive loop filter for lowfrequency Built in Self-Test (BIST) applications. The detailed ADC architecture and a step

by step process designing the zoom-ADC along with a synthesis tool that can target various

design specifications are presented. The design flow does not rely on extensive knowledge

of an experienced ADC designer. Two example set of BIST ADCs have been synthesized

with different performance requirements in 65nm CMOS process. The first ADC achieves

90.4dB Signal to Noise Ratio (SNR) in 512µs measurement time and consumes 17µW

power. Another example achieves 78.2dB SNR in 31.25µs measurement time and

consumes 63µW power. The second ADC architecture is a multi-mode, dynamically

zooming passive sigma-delta modulator. The architecture is based on a 5b interpolating

flash ADC as the zooming unit, and a passive discrete time sigma delta modulator as the

fine conversion unit. The proposed ADC provides an Oversampling Ratio (OSR)-

independent, dynamic zooming technique, employing an interpolating zooming front-end.

The modulator covers between 0.1 MHz and 10 MHz signal bandwidth which makes it

suitable for cellular applications including 4G radio systems. By reconfiguring the OSR,

bias current, and component parameters, optimal power consumption can be achieved for

every mode. The ADC is implemented in 0.13 µm CMOS technology and it achieves an

SNDR of 82.2/77.1/74.2/68 dB for 0.1/1.92/5/10MHz bandwidth with 1.3/5.7/9.6/11.9mW

power consumption from a 1.2 V supply.

Machine learning for analytics has exponentially increased in the past few years due to its ability to identify hidden insights in data. It also has a plethora of applications in healthcare ranging from improving image recognition in CT scans to extracting semantic meaning from thousands of medical form PDFs. Currently in the BioElectrical Systems and Technology Lab, there is a biosensor in development that retrieves and analyzes data manually. In a proof of concept, this project uses the neural network architecture to automatically parse and classify a cardiac disease data set as well as explore health related factors impacting cardiac disease in patients of all ages.