VictorJuiz/thesis huggingface dataset:
Integrated optics as a toolbox for communication, quantum science, and sensing
Keywords: Engineering.
Electrical Engineering.

Integrated optics promises to bring the rich world of optical technology to the nanoscale; the fabrication of bulk optics on chips shows great promise in applications across communications, sensing, augmented and virtual reality, and even quantum computing. However, multitudinous challenges must be overcome to make these applications a reality. In this thesis, I will present new experimental and theoretical techniques to tackle four such challenges. First, I will describe how nonlinear optics can be used to generate passive optical isolators - filling a critical gap in integrated photonic infrastructure. Second, I will show how photonic inverse design can be applied to mode multiplexing to enable higher bandwidth optical communications. Third, I will describe new techniques for the scalable integration of 2D materials into photonic devices, allowing us to take advantage of their unique material process in a way that is compatible with mass manufacturing. Finally, I will show how inverse design techniques can be used to optimize modulatory signals in solid-state quantum systems to engineer their spectra and interactions.
Low energy inference for always-on tinyML applications
Keywords: Engineering.
Electrical Engineering.

Internet of Things (IoT) devices and sensors are generating data at an unprecedented scale. Processing all of this data in the cloud is infeasible due to the energy and latency cost of sending it over long distances. tinyML seeks to deploy deep neural networks (DNNs) on low-cost IoT devices in order to alleviate these challenges by distilling the data into just the necessary and useful information. This is particularly crucial in always-on scenarios where tinyML systems continuously analyze sensor data, necessitating low-energy processing to extend battery life and minimize overall energy consumption. This thesis studies the optimization of inference energy for always-on tinyML processors, covering insights across the stack from the network architecture, to the hardware architecture, to the circuit design of memories and compute. Our contributions include: (1) A study on tinyML network architectures and precision, assessing model size, accuracy, energy, and latency trade-offs, validated by silicon measurements. (2) 6T-latch-based Inner Loop Memories (ILMs) optimized for tinyML with an access cost of only 15 fJ/Byte. (3) A Pipelined Pixel Streaming (PPS) system architecture and dataflow that leverages ILMs to reduce memory access overhead for memory-intensive bottleneck layers. (4) Full-custom bit-serial multipliers and adder trees integrated with ILMs to reduce compute area and energy. These contributions culminate in Medusa, a programmable 8b digital processor fabricated in 28 nm CMOS for always-on tinyML applications that achieves an image-to-class inference energy of 0.83 (4.6) µJ at an accuracy of 86.2% (91.6%) on CIFAR-10 with all parameters stored on chip. Medusa achieves a 3.0x lower energy per inference at iso-latency when compared with the current state of the art. Medusa achieves the goal of sub-nJ-per-pixel always-on tinyML inference across the Image Recognition, Visual Wake Words, and Audio Keyword Spotting workloads from the MLPerf Tiny Inference Benchmark deployed in this work.
Microfluidic methods to probe basophils for food allergy diagnostic applications
Keywords: Engineering.
Mechanical Engineering.

Basophils are rare (~1% of peripheral blood leukocytes), yet potent effector cells that initiate and perpetuate a wide range of immunological responses. In particular, the role of basophils in food allergy is widely studied and has led to the emergence of a highly accurate ex vivo diagnostic test for food allergy: the basophil activation test (BAT). In contrast to commonly used food allergy diagnostic tests, i.e., skin prick test (SPT), allergen specific IgE (sIgE) measurement, and oral food challenge (OFC), the BAT provides highly sensitive and specific results (more than 90% across a range of allergens) while not subjecting individuals to potentially unsafe outcomes (e.g., anaphylaxis). Furthermore, basophil reactivity measured with BAT correlates well with severity and can be used to predict allergen thresholds that elicit a reaction in OFC. Despite the advantages of BAT over other methods, translating it to common clinical practice has been hampered. Motivated by the potential that BAT presents for changing the paradigm of food allergy diagnostics, my thesis dissertation focuses on various microfluidic approaches and aspects of measuring basophil activation that aim to increase clinical accessibility to BAT and to other future assays that probe basophil function. First, I investigated the utility of avidin as a basophil activation marker to evaluate whether avidin could enhance the predictive capability of BAT. Next, I developed various methods for rapid immunomagnetic isolation of basophils from whole blood to enable BAT-based diagnostic approaches and research that require purified basophils. Finally, I developed and characterized a novel device and analysis pipeline for increasing the clinical accessibility to BAT. Where applicable, I used computational methods to inform the design of the devices and analyzed data with machine learning.
Mill of this world : studies in the ecology of circular motion in the age of Cervantes
Keywords: Humanities and Sciences.
Comparative Literature.

Miguel de Cervantes famously wrote the scene in which Don Quixote charges against windmills as though they were giants, while Sancho Panza sees them plainly as windmills. On the one hand, the object is understood as something else; on the other, it is a simple identity that hides any further understanding of the object. My dissertation takes this scene as a point of departure to inquire how language in the time of Cervantes captured understandings of technology, addressing the polysemy of circular motion embodied in and performed by machines such as mills, cranes and wheels. The results suggest that tensions between identity and difference like that in Don Quixote existed across literary genres and were part of the material and symbolic environment in which machines existed. This theme is approached variously. In Chapter 1, Juan de Herrera's manuscript on the principle of the crane used at El Escorial suggests mental automation of a geometrical idealism even in the empirical context of machine-building. Chapter 2 considers machine-building in the relatively new context of patent-granting, juxtaposing understudied descriptions of mills by inventors Ayanz, Garay and Lobato del Canto and those of nontechnical writers such as Polidoro and Pineda. I argue that these inventors approach machines from the point of view of how motions relate to output; linguistically, a mill is more than simply a mill, it is that entire relation. In contrast, mythographic and encyclopedic historiography consider machines primarily in terms of their functions, indexing them to various extrinsic elements. Finally, the materials assembled in Chapter 3 constitute a first survey of how, in Spanish (and some Italian) emblem books, machines with rotary elements, mills among them, are charged with moral elements and are made to stand for unresolvable aspects of existence itself.
Multi-pole NEM relays and multiple-bits-per-cell RRAM for efficient 3-D ICs
Keywords: Engineering.
Electrical Engineering.

In this dissertation, I present techniques for improving the power, performance, and area of integrated circuits (ICs) through 3-D integration of two emerging nanotechnologies: (1) resistive random-access memory (RRAM), a non-volatile memory with multiple-bits-per-cell storage capability, and (2) nanoelectromechanical (NEM) relays, nano-scale mechanical relays that can be actuated electrostatically. In modern ICs for edge computing, data movement between on and off-chip memories typically consumes a large fraction of the total power. Dense, non-volatile embedded memory can reduce/eliminate off-chip data movement by keeping frequently-read application data always on chip. RRAM is a good candidate for such a memory, especially because it can store multiple bits per cell, achieving high density on-chip storage. However, efficient and reliable operation with multiple-bits-per-cell RRAM has been a challenge due to (1) stochastic device behavior during programming that results in large pulse counts with traditional write-verify methods, and (2) reliability issues arising from resistance relaxation. Towards the goal of achieving efficient and reliable multiple-bits-per-cell RRAM, I present three contributions: (1) range-dependent adaptive resistance (RADAR) tuning, a fast and energy-efficient programming method for multiple-bits-per-cell RRAM that uses an adaptive combination of coarse- and fine-grained cell resistance tuning, yielding a 2.4x reduction in pulse count over prior methods, (2) characterization of resistance relaxation behavior in three RRAM technologies and analysis of its implications for multiple-bits-per-cell storage, and (3) efficient multiple-bits-per-cell embedded RRAM (EMBER), the first demonstration of a fully-integrated multiple-bits-per-cell RRAM macro. EMBER contains a multiple-bits-per-cell read and write controller with a high degree of flexibility that enables good level allocation (mitigating reliability issues from resistance relaxation) and programming scheme optimization (yielding low-energy, low-latency multiple-bits-per-cell writes). Finally, in reconfigurable ICs, in addition to the memories, the routing fabric consumes a large fraction of the overall area and power. I demonstrate that replacing CMOS routing switches with 3-D integrated multi-pole nanoelectromechanical (NEM) relays in a coarse-grained reconfigurable array (CGRA) can achieve 19% lower area and 10% lower power at iso-performance.
Navigating the spinal skeletal stem cell microenvironment : insights for targeted tissue regeneration
Keywords: Medicine.

The intervertebral disc (IVD), also known as the spinal disc, undergoes accelerated degeneration compared to other connective tissues, leading to intervertebral disc degeneration. Addressing this challenge requires effective treatments capable of reversing fibrocartilage damage caused by factors such as age and injury. Tissue-resident stem cells hold promise for IVD treatment due to their self-renewal and differentiation properties. In this dissertation, I will discuss our efforts to identify novel regulators of spinal skeletal stem cell (SSC) differentiation and their role in regenerating IVD tissue in response to injury. Additionally, I will outline a protocol we developed for isolating and functionally assessing human SSCs from various cell and tissue sources. Our basic and preclinical findings lay the foundation for innovative stem cell-based therapies targeting IVD degeneration and other related disorders.
On data, modeling, and clinically aligned predictive models in healthcare
Keywords: Engineering.
Management Science and Engineering.

The application of data-driven models to healthcare settings has grown exponentially. The general pathway for creating these models involves leveraging data to construct a model that helps healthcare stakeholders in making "data-driven" decisions. To build trusted models for high-stakes decision-making problems using this pathway, it is crucial to proactively incorporate healthcare stakeholders. This dissertation studies the different elements of this pathway, particularly the importance of alignment with healthcare stakeholders. In the first two parts, this dissertation identifies gaps between modern machine learning (ML) pipelines and healthcare stakeholders, focusing on both the data and model phase, and studies the proactive use of clinical knowledge to create more robust, clinically usable models. For the data phase, we use clinical expertise to generate features from unstructured physician progress notes and analyze their impact on clinical risk prediction models in a prostate cancer case study. The results provide a proof of concept for the value of clinical expertise-guided feature generation. For the model phase, we provide a reproducible framework to examine the misalignment, or inconsistencies, of modern ML model behavior with clinical experiential learning, focusing on the impact of underspecification of ML pipelines. In a prostate cancer outcome prediction case study, we identify and address these inconsistencies via a constraint-guided alignment approach and additionally test the feasibility of a key requirement for a feedback-driven alignment approach. The constraint-guided alignment approach shows that a model can be better aligned with clinical experiential learning without compromising performance. This practical framework is complemented by a mathematical analysis, rooted in recent theory of overparameterized neural networks, to illustrate how model complexity, combined with a large training dataset, can lead to inconsistencies. In the final part, this dissertation transitions to illustrating the capabilities of a non-ML model to inform healthcare decision making by analyzing the effectiveness of face masks during the COVID-19 epidemic using a dynamic disease model. The findings illustrate the model's ability to provide timely insights for policymaking, effectively capturing how characteristics of face masks influence the epidemic's course during both the initial outbreak and subsequent resurgence. Overall, this dissertation, through interdisciplinary collaborations and new methodologies, advances our understanding of how to design data-driven models more effectively for greater trust and adoption in healthcare practices, and how such models can be used to inform healthcare decision making promptly and successfully.
Open-air manufacturing of perovskite solar modules
Keywords: Engineering.
Materials Science and Engineering.

Metal halide perovskites have emerged over the last 10 years as a strong candidate for next generation solar energy production with record device efficiencies of >26% competing with state-of-the-art incumbent crystalline silicon devices. The rapid rise in perovskite performance, combined with their solution processability and low-cost fabrication compared to conventional physical and chemical vapor-based deposition methods, demonstrates a pathway towards meeting the global energy demand. However, high performance solution-processed perovskite devices are typically fabricated on areas as small as 0.1 cm2, demonstrating a significant scaling barrier for commercialization and deployment. Additionally, while perovskites enable solution processing of low-cost earth-abundant materials, complementary device layers often rely on low-throughput vacuum-based processes, negating many of the cost benefits promised by perovskite solar energy. Here, an open-air spray deposition platform is proposed as a high-throughput, scalable route for the production of low-cost perovskite solar modules. Rapid Spray Plasma Processing (RSPP) is first presented as a platform for producing robust, high-performing large-area perovskite films in open air at linear processing speeds of 12 m/min. RSPP is paired with indirect liftoff laser scribing to produce perovskite modules with >25 cm2 active areas, optimizing for low-cost laser systems and consistent module performance across a broad range of active areas. The open-air spray deposition platform built around the RSPP technique is extended to the entire perovskite device, demonstrating the commercial benefit of open-air manufacturing with complete technoeconomic analysis translating lab-scale optimizations directly to MW-scale module manufacturing costs and demonstrating a pathway towards the levelized cost of energy (LCOE) target of $0.02/kWh.
Photonic spin and chirality in subwavelength optical cavities
Keywords: Engineering.
Mechanical Engineering.

Spin symmetry is fundamental to matter: from the handedness of DNA and amino acids to the angular momentum of electrons. Since the mid-nineteenth century, light has been our probe of spin typically via the weak preferential absorption of circularly polarized light. However, as we approach smaller lengths scales, especially smaller than the wavelength of light, interactions become several orders of magnitude weaker, and we struggle to resolve our observations. Magnifying these otherwise weak interactions while controlling the spin symmetry of the interaction will enable us to better direct light, create quantum bits of information for exponentially faster computing, and communicate information at light-speeds. In this dissertation, we present: 1) symmetry-protected optical cavities that magnify the local electromagnetic energy density, 2) the spin dissymmetry factor as a parameter to describe the interaction of light with quantum particles in optical cavities, and 3) the design of a highly asymmetric optical cavity that imparts one-way spin for complete control (isolation) over lasing. These optical cavities are designed with complete control over three-dimensional symmetry using dielectric metasurfaces.
PMC : a classification of production metrics found useful by AEC professionals
Keywords: Engineering.

A modern paradigm considers AEC projects to be temporary production systems. Through rapid feedback, production metrics (PM) provide a chance to project teams to control these production systems by adjusting their actions and decisions in a timely manner, making it more likely for projects to stay on track. Observation of PM reported (PMR) through the Virtual Design and Construction (VDC) certificate program conducted by the Stanford Center for Professional Development (SCPD) revealed that AEC professionals are missing a common PM vocabulary, which hinders establishing and reporting PM consistently. While a limited number of PM, such as PPC (percent plan complete), number of RFIs (requests for information), number of changer orders, and number of clashes detected, are gaining widespread adoption in the AEC industry, it isn't clear whether there are other PM which AEC professionals are finding useful and whether they are generalizable across a variety of projects. This thesis contributes a classification of production metrics found useful by ~600 AEC professionals managing a variety of project production systems. The professionals represent ~100 AEC companies engaged as owners, designers, and builders on ~300 projects of different types (building, infrastructure, industrial) in 10 countries on 2 continents. The contribution, presented in this dissertation as the production metrics classification (PMC), was generated through several rounds of content analysis and expert reviews of 1,971 PMR by professionals enrolled in the 1st large-scale VDC certificate program, and was validated through a survey administered at the 2nd program. It consists of 96 PM grouped into 3 categories and 17 sub-categories. 36 of the 96 PM relate to collaboration, 17 relate to information generation and use, and 43 relate to workflows for managing project production systems. 37 of the 96 PM relate to conformance while 59 relate to performance. 7 PM are likely to be measured daily and 70 are likely to be measured weekly. 33 PM can be measured immediately after an activity is performed and are therefore, likely to improve in the short term, i.e., within a week of adjusting actions and decisions, while others are likely to take longer. The PMC addresses a conceptual gap in the literature by providing clarity on specific PM found useful in project production systems. It provides a foundation for future research to establish control mechanisms in AEC production systems based on feedback provided by specific PM. In addition, it opens pathways for establishing correlations between specific PM and project outcomes.
Probabilistic assessment of pore pressure prediction with Bayesian Geophysical Basin Modeling
Keywords: Sustainability.
Energy Resources Engineering.

Effective decision-making in exploring and developing subsurface resources hinges on accurately estimating geologically plausible and realistic properties. Nevertheless, conventional inverse modeling formulations for property estimation encounter computational challenges arising from high-dimensional problem settings and the substantial geological uncertainty inherent in subsurface estimation problems. Especially for pore pressure forecast, this challenge yields to disregarding the geological processes occurring in the subsurface in the predictions by standardizing the use of empirical models calibrated only on offset wells. This dissertation introduces and investigates methodologies rooted in the Bayesian Geophysical Basin Modeling (BGBM) workflow to assess pore pressure. The aim is to incorporate geological processes into estimations and utilize probabilistic models, facilitating a comprehensive evaluation of pore pressure predictions across exploration and exploitation stages in subsurface resource activities.
Probabilistic causal logics : axiomatization, expressivity, complexity
Keywords: Engineering.

This dissertation applies techniques and perspectives from logic to the study of probabilistic models of causality. It aims to shed light on recent questions and controversies by gaining a theoretical grasp on the potential and limitations of the latter. As a foundation, we first study logical languages capturing probabilities over a set of propositional atoms. We find that a division between those languages containing at most additive arithmetic and those containing any sort of multiplicative arithmetic robustly tracks a gap in computational complexity from NP-completeness to ETR-completeness respectively. We address expressivity and give several new axiomatization arguments. We next move to the analysis of causality, adopting the very popular structural framework. We axiomatize the logic of interventionist conditionals on deterministic structural models and show it to be NP-complete, and also obtain similar results for a few other related logics. The languages of the aforementioned sections express either probability or causality, but not both. The final part of the thesis combines the two notions. We define rich probabilistic logical languages delineating the three levels of the Causal Hierarchy: associational, interventional, and counterfactual. Inference then emerges as a special case of logical entailment. Interpreting our languages over probabilistic structural causal models, we give axiomatizations at each level and show that, in terms of expressivity, each level strictly exceeds those below it in almost-every causal model, thus revealing that additional model assumptions are necessary in a very strong sense to perform causal inference. However, in terms of computational complexity, the hierarchy 'collapses': we find that the satisfiability problem for all three languages is ETR-complete. Lastly, we turn to those additional assumptions that permit causal inference. Within the structural framework, these are frequently presented in the form of a mixed acyclic graph, on which methods like the do-calculus rely. We obtain a complete axiomatization of our counterfactual language over the model class of any graph that meets certain restrictions. Finally we formally define and axiomatize another popular causal framework, that of potential outcomes and Rubin causal models. Comparing the structural and potential outcomes approaches, we find that every Rubin model can be expressed as the abstraction of a structural model and that structural assumptions may appear implicitly in practice with potential outcomes, even though these two causal frameworks have sometimes been presented as though in conflict.