VictorJuiz/thesis huggingface dataset:
Navigating the spinal skeletal stem cell microenvironment : insights for targeted tissue regeneration

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.

Keywords: Medicine.
Advisor: Longaker Michael T.
Bhutani Nidhi
Palmer Theo
Weissman Irving L.
On data, modeling, and clinically aligned predictive models in healthcare

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.

Keywords: Engineering.
Management Science and Engineering.
Advisor: Bayati Mohsen
Brandeau Margaret L.
Johari Ramesh
Open-air manufacturing of perovskite solar modules

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.

Keywords: Engineering.
Materials Science and Engineering.
Advisor: Dauskardt R. H. (Reinhold H.)
Brongersma Mark L.
Congreve Dan
Photonic spin and chirality in subwavelength optical cavities

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.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Dionne Jennifer Anne
Tang Sindy (Sindy K.Y.)
Brongersma Mark L.
PMC : a classification of production metrics found useful by AEC professionals

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.

Keywords: Engineering.
Advisor: Fischer Martin
Jain Rishee
Rajagopal Ram
Probabilistic assessment of pore pressure prediction with Bayesian Geophysical Basin Modeling

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.

Keywords: Sustainability.
Energy Resources Engineering.
Advisor: Mukerji Tapan
Caers Jef
Shachter Ross D.
Probabilistic causal logics : axiomatization, expressivity, complexity

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.

Keywords: Engineering.
Advisor: Barrett Clark
Icard Thomas
Pratt Vaughan R.
Probabilistic inference in the era of large models

Recent progression in generative artificial intelligence has witnessed a ballooning in model size and data dimensionality. These large models, however, come with increased computational demands which prohibit the use of many traditional probabilistic inference algorithms. There is a pressing need for new inference algorithms that are efficient enough to run on large models and modern architectures, and powerful enough to work with high dimensionalities and large datasets. In this dissertation, we address this challenge by designing algorithms using ingredients that are compatible with model scale, such as parallelization, amortized inference, and neural function approximations. We present a variety of techniques to improve sampling and inference, leading to faster sample speed, better flexibility of sample queries, and more accurate estimation of inference targets. Our methods are applicable to large models across a range of architectures such as diffusion, autoregressive, and masked-autoencoder models. We demonstrate these findings on applications spanning image and text generation, game-playing, and robotics domains. These insights lead to practical improvements and novel perspectives for efficiently deploying large-scale generative models.

Keywords: Engineering.
Advisor: Ermon Stefano
Sadigh Dorsa
Ahmadipouranari Nima
Progress towards cavity-based X-ray free-electron lasers : demonstration of cavity ringdown and microbunch rotation out-coupling

Current X-ray Free-Electron Lasers (XFELs), including the Linac Coherent Light Source (LCLS) at SLAC, are single-pass SASE (self-amplified spontaneous emission) machines, where electrons produce X-rays as they pass through an undulator line just once. The exponential gain of X-ray power in an XFEL occurs as X-rays act on the electron beam, causing it to form microbunches at the X-ray wavelength. In a SASE XFEL, the X-rays which initiate this process come from noise in the beam, and thus X-rays produced by current XFELs are transversely coherent, but longitudinally noisy. In proposed cavity-based XFELs (CBXFELs), by contrast, X-rays are spectrally filtered and returned by mirrors from the end of the undulator line to the beginning, so a strong X-ray pulse is used to seed the FEL process on subsequent passes. Future X-ray light sources based on CBXFELs can thus improve longitudinal coherence and spectral brightness, and promise to be more stable than current XFELs. In this thesis I discuss the requirements to build a CBXFEL cavity and several aspects of CBXFEL development. I cover in detail the characterization of CBXFEL-quality diamond mirrors, two cavity out-coupling methods including active cavity Q-switching and microbunch rotation, and a demonstration of a 14 m Bragg-reflecting cavity.

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Huang Zhirong
Marinelli Agostino
Reis David A.
Proximal proteomics identifies novel subcellular localization and protein-protein interactions in Raf1-dependent cancers and SARS-CoV-2 infection

Protein-protein interactions are essential to communication of information within cells. These interactions are altered in response to extracellular stimuli, intracellular differences in the presence of metabolites, and numerous other cell-state differences. Dysregulation of proteoforms and the complexes they form occurs in any disease state and restoring appropriate function to cells requires understanding how these signaling pathways and quaternary protein structures are disrupted. To understand the dependence of various tumors on the Raf-1 protein, proximity proteomics strategies were used. These experiments identified a novel mitochondrial localization of Raf-1 and an interaction with glutaminase. To better understand this localization and novel interaction, experiments that force Raf-1 localization to various compartments are used to decouple its canonical MAPK pathway capability and the novel glutaminase-altering function and further demonstrate its importance for cancer. Beyond tumorigenesis, protein-protein interactions are also critical for viral infection and pathogenesis. To understand these processes, BioID proteomics was performed on 17 SARS-CoV-2 proteins. This work identified several pathways by which SARS-CoV-2 suppressed cellular immunity, novel subcellular localization for some of the SARS-CoV-2 proteins, and points of difference between CoV-2 and other coronaviruses.

Keywords: Medicine.
Advisor: Khavari Paul A.
Ferrell James Ellsworth
Jackson Peter K. (Peter Kent)
Oro Anthony
Reading and writing neural computation : from silent speech to holographic optogenetics

This thesis presents a collaborative and interdisciplinary approach to understanding and manipulating neural computation across species. The work is divided into three main projects, each contributing to the overarching goal of improving human-computer interaction and deepening our understanding of neural dynamics. The first project explores Silent Speech Interfaces (SSIs) as a noninvasive alternative to brain-computer interfaces for soundless verbal communication. This work seeks to improve the performance of SSIs by using novel loss functions and large language models (LLMs) to significantly reduce word error rates in silent speech recognition on an open vocabulary. The development introduces Multimodal Orofacial Neural Audio (MONA) and LLM Integrated Scoring Adjustment (LISA), improving the state- of-the-art word error rate on silent speech from 28.8% to 12.2%. This demonstrates that SSIs are a viable alternative to automatic speech recognition and offers potential communication solutions for people with speech impairments. The second project focuses on translational research in SSIs at the intersection of chemical engineering and machine learning. We introduce a novel surface elec- tromyography (sEMG) sensor system that employs stretchable, sticky conductive polymers for high-fidelity adherence to the skin. In tandem with sensor development, this project also advances the application of high-speed state-space machine learning models for decoding sEMG on a 50-word vocabulary. This dual focus on advanced sensor technology and new machine learning methods opens new avenues for more reliable and human-centered wearable devices for biomedical applications. The third project shifts focus to the bioengineering of transgenic zebrafish to facilitate whole-brain optogenetics and holographic control of neural circuits. Through the creation of stable transgenic lines that express novel optogenetic actuators, this work enables the manipulation and observation of neural activity throughout the entire brain of a larval zebrafish. We also develop statistical and machine learning models to predict and denoise calcium dynamics in neural tissue. This project provides insight into a neural circuit involved in anxiety and depression, offering a new tool to dissect the functional organization of vertebrate brains. Together, these projects make advances in the fields of machine learning, bio- engineering, chemical engineering, and neuroscience in the pursuit of developing new modalities for human-computer interaction. From translational research in silent speech recognition for humans and basic research in neural circuit manipulation in zebrafish, this thesis contributes to the development of novel technologies for interfacing with the nervous system while drawing inspiration from the principles underlying neural computation.

Keywords: Medicine.
Advisor: Druckmann Shaul
Baccus Stephen A.
Bao Zhenan
Henderson Jaimie (Jaimie M.)
Linderman Scott
Rewriting the mammalian genome : a user's guide

In this dissertation, I embark on an explorative journey to unravel the complexities and unlock the potential of "rewriting the mammalian genome." This odyssey commences with a philosophical reflection on the questions that first inspired me towards this pursuit and an exploration of the current landscape of site-specific engineering technologies, tracing both their origins and evolution (Chapter 1). This is followed by an examination of the successes and challenges I have encountered in trying to apply these tools to "rewrite the mammalian genome" both ex vivo (Chapter 2) and in vivo (Chapter 3). Leveraging insights I have gained from my experience with both approaches, I amalgamate them into something new (Chapter 4). Along the way I also outline hurdles posed by both innate cellular (Chapter 2) and adaptive immune systems (Chapter 5) to "rewriting the mammalian genome" as well as potential avenues to overcome them. Finally, the narrative culminates in a distillation of the work I have performed and the lessons I have learned along the way to outline a comprehensive framework by which I believe "rewriting of the mammalian genome" can be achieved (Chapter 6). The dissertation encapsulates work conducted across various fields, emphasizing the lessons I have learned in my endeavor to "rewrite the mammalian genome." It offers reflections and insights into the complexities, challenges, and triumphs encountered in my quest to push the frontiers of genetic engineering in mammals, charting a course to fully realize the immense potential that "rewriting of the mammalian genome" holds.

Keywords: Medicine.
Advisor: Nakauchi Hiromitsu
Porteus Matthew H.
Qi Lei (Professor of Bioengineering)
Sebastiano Vittorio