Serverless, also known as function-as-a-service (FaaS), is an increasingly important paradigm in cloud computing. Developers register functions to a managed FaaS platform to serve user requests without the need to maintain their own servers. FaaS abstracts away the complexity of managing infrastructure, offers high availability, and automatically scales. However, today's FaaS platforms are often inefficient and unreliable, leaving developers with several complex application management challenges. Specifically, there are three key challenges: (1) minimizing cost while maintaining performance under varying load, (2) providing strong fault-tolerance guarantees in the presence of failures, and (3) improving debuggability and observability for distributed ephemeral functions. In this dissertation, we describe three new abstractions and build three systems to enhance the cost-efficiency, reliability, and debuggability of FaaS applications. We focus on two important categories of FaaS applications: compute-intensive, such as image recognition services, and data-centric, such as e-commerce web services. First, we address the challenge of cost efficiency for ML inference serving, a growing category of compute-intensive tasks. In particular, we tackle the key question of how to automatically configure and manage resources and models to minimize cost while maintaining high performance under unpredictable loads. Existing platforms usually require developers to manually search through thousands of model-variants, incurring significant costs. Therefore, we propose INFaaS, an automated model-less system where developers can easily specify performance and accuracy requirements without the need to specify a specific model-variant for each query. INFaaS generates model-variants from already trained models and efficiently navigates the large trade-off space of model-variants on behalf of developers to achieve application-specific objectives. By leveraging heterogeneous compute resources and efficient resource sharing, INFaaS guarantees application requirements while minimizing costs. Second, we address the challenge of providing fault tolerance while achieving high performance for data-centric applications. Existing FaaS platforms support these applications poorly because they physically and logically separate application logic, executed in cloud functions, from data management, done in interactive transactions accessing remote databases. Physical separation harms performance, and logical separation complicates efficiently providing fault tolerance. To solve this issue, we propose Apiary, a high-performance database-integrated FaaS platform for deploying and composing fault-tolerant transactional functions. Apiary wraps a distributed database engine and uses it as a unified runtime for function execution, data management, and operational logging. By physically co-locating and logically integrating function execution and data management, Apiary delivers similar or stronger transactional guarantees as comparable systems while significantly improving performance, cost, and observability. Finally, we delve into the challenge of debugging distributed data-centric applications. These applications are hard to debug because they share data across many concurrent requests. Currently, developers need to unravel the complex interactions of thousands of concurrent events to reproduce and fix bugs. To make debugging easier, we extend the tight integration between compute and data in Apiary and explore the synergy between the way people develop and debug their database-backed applications. We propose R^3, a "time travel" tool for data-centric FaaS applications that access shared data through transactions. R^3 allows for faithful replay of past executions in a controlled environment and retroactively execution of modified code on past events, making applications easier to maintain and debug. By recording concurrency information at transaction-level granularity, R^3 enables practical time travel with minimal overhead and supports most production DBMSs. We demonstrate how R^3 simplifies debugging for real, hard-to-reproduce concurrency bugs from popular open-source web applications.

The objective of this research is to achieve accurate and scalable bridge health monitoring (BHM) by learning, integrating, and generalizing the monitoring models derived from drive-by vehicle vibrations. Early diagnosis of bridge damage through BHM is crucial for preventing more severe damage and collapses that could lead to significant economic and human losses. Conventional BHM approaches require installing sensors directly on bridges, which are expensive, inefficient, and difficult to scale up. To address these limitations, this research uses vehicle vibration data when the vehicle passes over the bridge to infer bridge conditions. This drive-by BHM approach builds on the intuition that the recorded vehicle vibrations carry information about the vehicle-bridge interaction (VBI) and thus can indirectly inform us of the dynamic characteristics of the bridge. Advantages of this approach include the ability for each vehicle to monitor multiple bridges economically and eliminating the need for on-site maintenance of sensors and equipment on bridges. Though the drive-by BHM approach has the above benefits, implementing it in practice presents challenges due to its indirect measurement nature. In particular, this research tackles three key challenges: 1) Complex vehicle-bridge interaction. The VBI system is a complex interaction system, making mathematical modeling difficult. The analysis of vehicle vibration data to extract the desired bridge information is challenging because the data have complex noise conditions as well as many uncertainties involved. 2) Limited temporal information. The drive-by vehicle vibration data contains limited temporal information at each coordinate on the bridge, which consequently restricts the drive-by BHM's capacity to deliver fine-grained spatiotemporal assessments of the bridge's condition. 3) Heterogeneous bridge properties. The damage diagnostic model learned from vehicle vibration data collected from one bridge is hard to generalize to other bridges because bridge properties are heterogeneous. Moreover, the multi-task nature of damage diagnosis, such as detection, localization, and quantification, exacerbates the system heterogeneity issue. To address the complex vehicle-bridge interaction challenge, this research learns the BHM model through non-linear dimensionality reduction based on the insights we gained by formulating the VBI system. Many existing physics-based formulations make assumptions (e.g., ignoring non-linear dynamic terms) to simplify the drive-by BHM problem, which is inaccurate for damage diagnosis in practice. Data-driven approaches are recently introduced, but they use black-box models, which lack physical interpretation and require lots of labeled data for model training. To this end, I first characterize the non-linear relationship between bridge damage and vehicle vibrations through a new VBI formulation. This new formulation provides us with key insights to model the vehicle vibration features in a non-linear way and consider the high-frequency interactions between the bridge and vehicle dynamics. Moreover, analyzing the high-dimensional vehicle vibration response is difficult and computationally expensive because of the curse of dimensionality. Hence, I develop an algorithm to learn the low-dimensional feature embedding, also referred to as manifold, of vehicle vibration data through a non-linear and non-convex dimensionality reduction technique called stacked autoencoders. This approach provides informative features for achieving damage estimation with limited labeled data. To address the limited temporal information challenge, this research integrates multiple sensing modalities to provide complementary information about bridge health. The approach utilizes vibrations collected from both drive-by vehicles and pre-existing telecommunication (telecom) fiber-optic cables running through the bridge. In particular, my approach uses telecom fiber-optic cables as distributed acoustic sensors to continuously collect bridge dynamic strain responses at fixed locations. In addition, drive-by vehicle vibrations capture the input loading information to the bridge with a high spatial resolution. Due to extensively installed telecom fiber cables on bridges, the telecom cable-based approach also does not require on-site sensor installation and maintenance. A physics-informed system identification method is developed to estimate the bridge's natural frequencies, strain and displacement mode shapes using telecom cable responses. This method models strain mode shapes based on parametric mode shape functions derived from theoretical bridge dynamics. Moreover, I am developing a sensor fusion approach that reconstructs the dynamic responses of the bridge by modeling the vehicle-bridge-fiber interaction system that considers the drive-by vehicle and telecommunication fiber vibrations as the system input and output, respectively. To address the heterogeneous bridge properties challenge, this research generalizes the monitoring model for one bridge to monitor other bridges through a hierarchical model transfer approach. This approach learns a new manifold (or feature space) of vehicle vibration data collected from multiple bridges so that the features transferred to such manifold are sensitive to damage and invariant across multiple bridges. Specifically, the feature is modeled through domain adversarial learning that simultaneously maximizes the damage diagnosis performance for the bridge with available labeled data while minimizing the performance of classifying which bridge (including those with and without labeled data) the data came from. Moreover, to learn multiple diagnostic tasks (including damage detection, localization, and quantification) that have distinct learning difficulties, the framework formulates a feature hierarchy that allocates more learning resources to learn tasks that are hard to learn, in order to improve learning performance with limited data. A new generalization risk bound is derived to provide the theoretical foundation and insights for developing the learning algorithm and efficient optimization strategy. This approach allows a multi-task damage diagnosis model developed using labeled data from one bridge to be used for other bridges without requiring training data labels from those bridges. Overall, this research offers a new approach that can achieve accurate and scalable BHM by learning, integrating, and generalizing monitoring models learned from drive-by vehicle vibrations. The approach enables low-cost and efficient diagnosis of bridge damage before it poses a threat to the public, which could avoid the enormous loss of human lives and property

Proper treatment of domestic wastewater is crucial for protecting human health and the environment. However, conventional wastewater treatment processes are often high-cost, energy-intensive, and insufficient for recovering resources. Furthermore, water infrastructure in the United States is nearing the end of its intended design lifespan, posing a key opportunity for reinvention. Anaerobic secondary treatment is a promising example of next-generation water infrastructure that prioritizes resource recovery through the production of methane energy. However, anaerobic secondary effluent requires further attention due to the presence of dissolved methane, sulfide, nitrogen, and phosphorus. This dissertation explores post-treatment of anaerobic secondary effluent to maximize resource recovery from domestic wastewater. Specifically, a life cycle assessment was performed to evaluate tradeoffs between physical/chemical processes and biological processes for dissolved methane, sulfide, nitrogen, and phosphorus removal. Additionally, a membrane-aerated biofilm reactor was tested to treat anaerobic secondary effluent with high concentrations of ammonium-nitrogen and sulfide. Lastly, the use of wastewater-derived struvite as a novel fire retardant was explored to improve the profitability of phosphorus-recovery technologies. These studies serve to direct future efforts in developing complete water treatment trains with anaerobic secondary treatment

Complex engineering design requires solving large optimization problems involving several disciplines. Collaborative Optimization is a two-level design optimization architecture that solves multidisciplinary problems as a series of single-disciplinary problems with little infrastructure overhead. It can be critical when practitioners do not have a fully coupled multidisciplinary optimizer but have access to multiple single-disciplinary optimizers. However, it comes at a steep computational cost because the limited information sharing between disciplines slows downs the progress of the optimization. Our work shows that this issue can be mitigated by exploiting the full extent of the available information. In the Collaborative Optimization framework, disciplines are repeatedly given a design point and tasked with finding the closest feasible design. These iterations are computationally expensive as they require each discipline to solve an optimization problem and report the result. To combine the information collected by all iterations, we propose to learn a predictive model of the distance to the feasible set of each discipline using datasets of feasible and infeasible design points. This requires modeling the signed distance function of each set, which is a challenging machine-learning task. Therefore, we introduce Householder networks: a new, lightweight neural network architecture that can learn distance functions more efficiently than conventional architectures. We then introduce our new method, called Bayesian Collaborative Optimization, which uses ensembles of Householder networks to represent probabilistic models of the disciplinary feasible sets. Following the Bayesian Optimization framework, these models are iteratively refined and used to find values of the design variables that improve the objective function while remaining feasible for every discipline. This method is shown to outperform previous Collaborative Optimization approaches on simple test problems. Finally, we introduce a new multi-disciplinary aircraft design problem. We optimize the airframe, propulsion system, and trajectory of an unmanned fixed-wing vehicle tasked with completing a half-marathon with a fixed battery. This problem tries to balance out realism, by including a diverse set of modeling and optimization approaches, with simplicity and low computational cost. The importance of trajectory optimization, which is efficiently solved by itself but hard to solve coupled with other disciplines, makes this problem different from those previously available. We hope that this problem will be useful to the research community as a test for multidisciplinary design optimization architectures. We have open-sourced the code that generated the results presented in this document. It can be accessed at the following links: https://github.com/jdebecdelievre/HouseholderNets.jl , https://github.com/jdebecdelievre/BayesianCollaborativeOptimizat ion.jl , and https://github.com/jdebecdelievre/ModelAirRaces.jl.

Protein function is dependent upon adoption of a native 3-D configuration whilst avoiding toxic conformations. Some proteins can fold spontaneously; however, the complexity of the proteome requires cellular cofactors called chaperones to ensure problematic proteins are natively folded. Chaperonins are a class of molecular chaperones that are universally conserved in all domains of life and contribute to the cellular proteostasis toolkit. They are 1-MDa complexes composed of two rings that undergo a dramatic conformational change with ATP hydrolysis to form an inner folding cavity. Chaperonins have significantly diverged in architecture, topology, and client repertoire so they are divided into two classes: group I and group II chaperonins. Group I chaperonins exist in prokarya whilst group II chaperonins are found in eukarya and archaea. All chaperonins help to orchestrate ATP-dependent client protein sequestration and refolding through encapsulation in the central folding cavity. This thesis is centered on enumerating the mechanism of a group II chaperonin found in M. Maripaludis (MmCpn), an archaeal methanogen. Biochemical and biophysical interrogation of structural moieties, chiefly the c-termini, were found to contribute to ATP hydrolysis, substrate re-folding, and complex integrity. New structural elements such as interfacial methionine fingers and electrostatic contacts were observed to undergo novel conformations, yielding new candidates for studying group II chaperonins in general. Electrostatic disruption of the c-termini allowed for dissection of multimeric states, including the long elusive single-ring species which could serve as a useful tool for understanding chaperonin biogenesis and function

This paper contains three chapters which each develops a new economic model to investigate a question related to market structure. The first chapter develops a search-theoretic model of competing platforms that profit from targeted advertising to understand the impacts of policies, such as those on consumer data and platform interoperability, on this market's function. The second chapter, based on joint work with Darrell Duffie, investigates the welfare implications of the growing fragmentation of trade across lit exchanges. The third chapter investigates the optimal design of a market for trade of a financial asset.

Accurate and efficient models for the relative motion of two satellites are needed to achieve greater autonomy in an increasingly crowded orbit environment with limited computing power. Analytical models provide a computationally cheap alternative to numerical integration for relative motion propagation, typically at the expense of accuracy. Two avenues to improving relative dynamics modeling accuracy are examined herein. First, higher-order terms in the Keplerian dynamics can be included. This dissertation introduces new, second-order models that are valid for both circular and elliptical orbits in three of the most popular relative state representations: the Cartesian relative position coordinates in the rotating Hill frame, the spherical coordinates in the inertial frame, and the relative orbital elements (ROE). The performance of these models is compared with one another and with several of the most popular models from the relative dynamics literature. The second direction to improve modeling accuracy is the inclusion of non-Keplerian perturbations. A general framework for modeling arbitrary perturbations in the Hill frame is developed and used to derive the equations governing the leading-order corrections for Earth oblateness perturbation, as well as a closed-form solution for the effect of oblateness on the relative motion in near-equatorial orbits. This is compared with Keplerian models and an ROE-based perturbation model in a full-force propagation. In addition to the advancement of relative dynamics models, this dissertation examines two applications of such models. A fast and efficient method for initial relative orbit determination (IROD) from bearing-angle measurements is introduced. The second-order models developed in the course of this research provide a means resolving the range ambiguity problem that arises from linear relative motion models. The IROD problem thereby becomes one of solving a system of polynomial constraint equations linking the line-of-sight measurements to the relative state parameters. An efficient method for solving this system is developed around the insight that these parameters scale with the ratio of the inter-spacecraft separation to the orbit radius and are therefore small for most applications of interest. The method uses a truncated expansion of the quadratic formula to recursively eliminate unknowns, reduce the dimension of the system, and ultimately acquire an approximate solution. Strategies for improving robustness, efficiency, and accuracy are developed and the method is applied to general second-order systems as well as to a broad range of IROD scenarios. Modifications to the constraint equations and solution algorithm are introduced to address the challenge of bias in the bearing-angle measurements. The second application considered is that of low-thrust maneuver planning for formation reconfiguration. The adoption of fuel-efficient electric propulsion systems poses a challenge for relative orbit control schemes, which are typically based on the assumption of impulsive maneuvers. That challenge is met herein with a geometrically intuitive, semi-analytical solution to the low-thrust problem. Beginning with the equations of relative motion of two spacecraft, an unperturbed chief and a continuously-thrusting deputy, a thrust profile is constructed which transforms the equations into a form that is solved analytically. The resulting relative trajectories are the family of sinusoidal spirals, which provide diversity for design and optimization based upon a single thrust parameter. Closed-form expressions are derived for the trajectory shape and time-of-flight for two prescribed relative velocity behaviors, and used to develop a novel patched-spirals trajectory design and optimization method. The example problem of a servicer spacecraft establishing and reconfiguring a formation around a target in geostationary earth orbit is used to demonstrate the application of the patched spirals technique as well as the advantages of the relative spiral trajectories over impulsive maneuvers. The sensitivity of the trajectory solutions to deviations from the underlying assumptions, uncertainties in the state, and errors in thrust are studied through high-fidelity simulation

As artificial intelligence changes nearly every facet of modern society, we should not be surprised that it is changing how we do social science. By leveraging the power of machine learning and automated text analysis, researchers can analyze complex patterns from data and extract meaning from natural language at an unprecedented scale. However, the application of these tools to social scientific inquiry raises important issues concerning construct validity and the very nature of deductive social science. Throughout this dissertation, I examine the promises and pitfalls of applying these cutting-edge technologies specifically to the sociological study of meaning. In the first chapter, I provide a comprehensive review of popular automated text analysis methods and classify them according to the pre-analytic constructs they extract from text. In the following chapters, I present two original studies that use machine learning and automated text analysis to answer fundamental questions about culture and meaning. The first study asks: does everyday symbolic exchange contain sufficient information to effectively enculturate a tabula rasa learner? The second asks: does the way an individual understands their nation shape their immigration policy preferences? Via novel and rigorous applications of computational methods, I provide compelling evidence that supports the affirmative answers to both questions. Ultimately, this dissertation highlights the potential of machine learning and automated text analysis to produce sound social science research. However, it also underscores analytical concerns of which researchers should be mindful

Artificial Intelligence (AI) has been transforming digital and analog markets in the past few years. Will the current economic analysis apply to new decision-making technologies? In my dissertation, Artificial Intelligence and Dynamic Markets, I study this question from multiple perspectives. I model automated decision-makers, the markets they operate in, and the information structures of dynamic digital economies. Through this research, my aim is to enhance our understanding of the functioning of dynamic digital markets and identify the market structures that facilitate efficient AI adoption.

"Before the Red Pill" traces the American men's rights movement (MRM) from its roots in the early 1960s to its growing influence in mainstream national politics by the early 2000s. Examining both MRM leadership efforts and grassroots organizing across the United States, this dissertation utilizes organizational papers, activist correspondence, oral histories, movement newsletters, advice literature and memoirs, and mainstream press coverage. The dissertation reveals the complex dynamics of gender, race, and politics in the growth of the MRM. The experience of divorce radicalized men's rights activists, who began organizing in the 1960s to reform family law. Rather than a mere backlash against feminism, men's rights thinkers adapted some of their most important insights and strategies from second-wave feminists throughout the 1970s, before becoming militantly misogynistic by the 1990s. Both conservative women intellectuals and second wives of divorced men's rights activists played critical roles during this era, softening the movement's public image and aiding in the development of a fathers' rights sub-movement devoted to child custody and support reforms. Overwhelmingly white themselves, men's rights thinkers made selective allusions to race to compare their politics to the Black freedom struggle, yet they distanced themselves from potential Black members amid the racialized politics of the 1980s and 1990s. By the turn of the twenty-first century, men's rights activists devoted themselves to undermining feminist organizing against rape, domestic violence, and sexual harassment while claiming that men, rather than women, were the true victims of gendered violence. The simultaneous intensification of antifeminist and anti-state sentiments among activists pushed the movement further rightward into conservative partisan politics. Understanding the men's rights movement helps explain the emotive roles of masculinity, grievance, and entitlement in mobilizing the far Right base and maintaining persistent inequalities in the contemporary United States.

This dissertation, comprised of three papers, explores mechanisms operating within engineering and technology work environments at the organizational and interactional levels that help perpetuate societal-level gender and race inequalities. The first paper provides support for the hypothesis that in the absence of intentional action to counteract stereotypes, organizations promote employees into jobs aligned with racialized gendered stereotypes, net of performance and experience. This paper highlights the importance of taking an intersectional approach to the study of gender inequality by demonstrating how analyzing data by gender alone can mask important trends by race, gender, and job and their intersection. The second paper demonstrates that undesirable and inequitable working conditions for women in occupations filled mostly with men contribute to lower retention of women, especially racial minority women, in traditionally masculine, higher paying jobs, contributing to occupational gender segregation, the biggest contributor to the gender pay gap. The third paper describes one way to create more equitable conditions for minority groups, including women, at work. This paper documents the results of a field experiment showing that improving interpersonal interactions in a tech company has an especially positive effect among workers who feel least like they belong at work, a group of workers who are disproportionately women and racial minorities. This paper provides an experimentally validated template for improving interactions, pointing a way toward creating more inclusive organizations that support a sense of belonging for all workers, an important step toward reducing societal gender and racial inequality. Together, these papers contribute new knowledge to our understanding of how gender and race inequalities persist within technical work organizations and suggest ways of mitigating those inequalities. More broadly, this research sheds light on the continual creation and maintenance of inequalities that occur in many settings throughout society through interpersonal interactions and organizational structures and policies.

Scattering amplitudes are a bridge between theory and measurable quantities in high energy particle experiments, such as cross sections of scattering processes. Traditionally, scattering amplitudes are calculated using Feynman diagrams. However, their computational complexity grows dramatically as one tries to calculate higher-order corrections. Recently, a new method, the amplitude bootstrap, has arisen, that aims to bypass Feynman diagrams and compute scattering amplitudes directly by using knowledge of the mathematical structures describing amplitudes. Parts of this thesis constitute a continuation in this development of bootstrap techniques, focusing on their application in a toy model, N=4 Super-Yang-Mills Theory. The rest of the thesis deals with hidden structures of amplitudes in the same theory. In particular, it includes a newly conjectured symmetry and duality of amplitudes.

Decades of advances in semiconductor technology have led to the scaling of silicon transistors towards nanometer scale dimensions, culminating in physical limitations to transistor size, and the concurrent issue of thermal degradation and excessive energy consumption. As the semiconductor industry moves towards continuously increasing computational density, for example from planar to 3-dimensional (3D) vertical stacking, the ability to effectively regulate temperature and manage heat has become a pressing bottleneck to the realization of these dense architectures. Thermal challenges in high-density memory and computation require new thermal and material considerations at the nanoscale. Accurate knowledge of temperature and heat transport within a complex system is the essential first step to successful thermal management. To this end, experimental validation of material thermal conductivity is fundamental both to understanding thermal transport and to implementing solid state thermal management approaches. The complexity of thermal transport within a dense system also necessitates novel thermal management tactics including active methods of routing heat, such as thermal switching. This dissertation evaluates the intersection of nanoscale thermal transport and nanomaterials, assessing materials that show promise for low-temperature integration in semiconductor-based systems. The studies presented in this thesis range from fundamental thermal, electrical, and material characterization thin films to the design, demonstration, and characterization of an active thermal switch. We first present thermal characterization of nanoscale boron nitride thin films transferred and deposited at low temperature (< 500 °C) for back-end of the line (BEOL) temperature compatible processes. We report the in-plane thermal conductivity measurement of large area hexagonal boron nitride thin films grown using chemical vapor deposition (CVD) and transferred at room temperature to be 55-78 W/m/K from 300-400 K. We also evaluate the complementary thermal and electrical properties of low thermal conductivity boron nitride films deposited at room temperature using electron enhanced atomic layer deposition (EE-ALD). These electrically insulating films have very low cross-plane thermal conductivities (< 0.6 W/m/K) with 10 nm thick films approaching the amorphous thermal limit of BN (~0.13 W/m/k). Finally, we present the fabrication and thermal characterization of graphene-based thermal switches for active thermal management, reporting the first thermal switch based on flexible, collapsible graphene membranes with low operating voltage (~2V)