VictorJuiz/thesis huggingface dataset:
On principled local optimization methods for federated learning

Federated Learning (FL), a distributed learning paradigm that scales on-device learning collaboratively, has emerged as a promising approach for decentralized AI applications. Local optimization methods such as Federated Averaging (FedAvg) are the most prominent methods for FL applications. Despite their simplicity and popularity, the theoretical understanding of local optimization methods is far from clear. This dissertation aims to advance the theoretical foundation of local methods in the following three directions. First, we establish sharp bounds for FedAvg, the most popular algorithm in Federated Learning. We demonstrate how FedAvg may suﬀer from a notion we call iterate bias, and how an additional third-order smoothness assumption may mitigate this eﬀect and lead to better convergence rates. We explain this phenomenon from a Stochastic Diﬀerential Equation (SDE) perspective. Second, we propose Federated Accelerated Stochastic Gradient Descent (FedAc), the ﬁrst principled acceleration of FedAvg, which provably improves the convergence rate and communication eﬃciency. Our technique uses on a potential-based perturbed iterate analysis, a novel stability analysis of generalized accelerated SGD, and a strategic tradeoﬀ between acceleration and stability. Third, we study the Federated Composite Optimization problem, which extends the classic smooth setting by incorporating a shared non-smooth regularizer. We show that direct extensions of FedAvg may suﬀer from the "curse of primal averaging, " resulting in slow convergence. As a solution, we propose a new primal-dual algorithm, Federated Dual Averaging, which overcomes the curse of primal averaging by employing a novel inter-client dual averaging procedure

Advisor: Ma Tengyu
Sidford Aaron
Valiant Gregory
On printing biopolymer-bound soil composite

This dissertation evaluates a novel bio-composite material, biopolymer-bound soil composite (BSC), for additive construction (3D printing) applications. BSC rheological and strength properties are measured and used to predict shape stability and structuration. These material attributes allow designers to determine the required construction time and maximum size of projects, and the rheological properties dictate the required capabilities of the automated construction systems used to build an autonomously constructed element. The goal of this dissertation is to provide the foundational knowledge needed to advance the use of BSC for 3D printing in terrestrial and extraterrestrial environments

Keywords: Civil & Environmental Engineering.
Advisor: Lepech Michael
Billington Sarah L. (Sarah Longstreth)
Loftus David (David John)
On the flow of the supercharge in a TTbar-deformed supersymmetric system

The $T\bar{T}$ deformation of 2-dimensional quantum field theories has been presented as a novel UV-completion mechanism with many interesting properties. Recently, it was shown that the $T\bar{T}$-deformed classical action of a flat space system with supersymmetry can be made off-shell supersymmetric to second order in the deformation parameter, by adding in new transformation rules at each order. The particular form of the perturbative extension developed in that work was complicated, and relied on a number of lucky cancellations to work out. In this thesis, we attempt to use the Hamiltonian picture description of $T\bar{T}$ of Kruthoff and Parrikar to re-derive the first-order contributions to the deformed SUSY transformations more directly. Though that picture is more natural, we show that a naive approach gives the correct answer as a subset of a more complicated expression. We discuss why this is the case, and suggest modifications which may get rid of the extra terms.

Keywords:  Department of Physics
SUSY
TTbar
supersymmetry
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Thesis
Advisor: Silverstein Eva
Feldman Benjamin
Coleman Evan
On the interaction of buoyant plumes and gaps with canopy mixing layers

Canopy mixing layers, as found in many environmental systems, quite significantly alter the local flow and turbulence fields. They also give rise to coherent structures that have a strong influence on the mixing and transport of momentum, turbulence, mass and heat in the system. Through experimental work with model vegetative canopies, this dissertation investigated systems in which the canopy mixing layers compete with other non-negligible processes. The first two topics are on canopies that are interrupted by gaps and trailing edges. The competing processes that alter the canopy mixing layer in gaps and edges are those related to flow expansion, reattachment, wake development, and boundary layer dynamics. The third topic looks at the interaction between canopy mixing layers and plume dynamics. The goal was to assess how canopy induced turbulence can impact the trajectory and behavior of hot buoyant plumes. All of these competing processes were studied to better understand the role of canopy dynamics in various environmental settings, most notably in wildfire spread. We connected our experimental work to looking into how mixing layer dynamics may alter the dynamics that is observed within fuel breaks, as well as wildfire plumes

Advisor: Koseff Jeffrey Russell
Fringer Oliver B. (Oliver Bartlett)
Ouellette Nicholas (Nicholas Testroet)
On the predictive power of graph neural networks

Graph Neural Networks (GNNs) are a class of deep learning models for making predictions on graph-structured data. Many different GNN models have been proposed to achieve promising empirical performance. However, their architectural designs were ad-hoc, and their theoretical understanding remained limited. Moreover, these models were developed on small graph benchmark datasets, which altogether limit the development of powerful GNNs for real-world prediction tasks over graphs. In this thesis, we aim to build powerful predictive GNNs by understanding, improving, and benchmarking the predictive power of GNNs---the ability of GNNs to make accurate predictions over graphs. This thesis consists of three parts. In Part I, we develop a theoretical framework for understanding the predictive power of GNNs. We specifically focus on the expressive power, asking whether GNNs can express desired functions over graphs. We use our theoretical framework to provide insight into whether a given GNN is powerful enough to model the ground-truth target function that underlies the data. We also propose a maximally-expressive GNN model that can provably model most functions over graphs. Equipped with the framework to design expressive GNN models, in Part II, we move on to improve their predictive power on unseen/unlabeled data, i.e., improve the generalization power of GNNs. Motivated by real-world applications, we develop methods for improving the generalization power of GNNs under two common limited data scenarios: limited labeled data and limited edge connectivity. Finally, in Part III, we create new graph benchmark datasets to resolve the issues with the existing benchmarks and to engage the community toward improving the predictive power of GNNs. We present the Open Graph Benchmark (OGB) and OGB-LSC, a collection of challenging, realistic, and large-scale benchmark datasets for machine learning on graphs. We discuss the impact our benchmarks have had in advancing the predictive power of GNNs and conclude with future challenges of applying GNNs to real-world prediction tasks

Advisor: Leskovec Jurij
Liang Percy
Ré Christopher
Oncogene Induced Senescence Alters Genomic Susceptibility to UV-Induced DNA Lesions

Oncogene Induced Senescence (OIS) is a tumor suppressive pathway that halts cell cycle progression in response to oncogene activation. During the development of cutaneous melanomas, mutational escape from OIS is a key contributor to the onset of overt malignancy. However, the origins of the escape process itself remain poorly understood. It is unclear if escape mutations arise solely through the random acquisition of DNA lesions, or if lesion acquisition is predetermined by the extensive epigenetic changes associated with OIS entry. To address this knowledge gap, we applied TRansposase Assisted Damage Sequencing (TRAD-Seq) to characterize changes in genomic susceptibility to UV-induced Cyclobutane Pyrimidine Dimers (CPDs) in a melanoma OIS model. We found a global increase in CPD susceptibility that is particularly pronounced in Differentially Susceptible Regions (DSRs) characterized by depletion of lamin B1 and enrichment of H3K9me3. Top-ranked DSRs encompass a range of cancer-associated genes relevant to melanoma development, including the CDKN2A/p16 locus implicated in OIS-escape. Furthermore, DSRs overlap with structural mutation hotspots identified in an Australian melanoma sequencing cohort, suggesting that differential CPD susceptibility underlies large-scale genomic instabilities seen in clinical melanomas. Together, our results show that OIS-driven epigenomic reorganization alters CPD susceptibility in a manner conductive to melanoma development and senescence escape.

Keywords: Morrison, Ashby
Department of Biology
Melanoma
Epigenetics
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Thesis
One Man, One Vote: Representational Equality, Prison Gerrymandering, and Reform

The Census Bureau counts incarcerated Americans at the location of their prison cells, rather than their hometowns. Census data is a central part of redistricting, enabling state and local governments to create roughly equal constituencies. But because felons largely cannot vote, districts containing penitentiaries can be the same size as their neighbor but with a fraction of the voting population. This means that voters in that district have a disproportionately high amount of representation. Patterns of incarceration further mean that people in prison are primarily incarcerated out of urban, Democratic communities of color and held in prisons in rural, white, Republican areas. The result is a transfer of political power from urban hubs to rural areas, with consequences for policy. Thirteen states have taken some level of action to address prison gerrymandering and mitigate its effects. This thesis investigates the factors relating to legislative success, finding that Democratic trifecta governments, mobilizing narratives around racial equity, and a legal foundation based in notions of electoral equality and representational nexuses all correspond to the passage of comprehensive legislation.

Keywords: prison gerrymandering
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Thesis
Online resource allocation : new results on regret and fairness

We consider an online resource allocation problem where a decision maker accepts or rejects incoming customer requests irrevocably in order to maximize expected reward given limited resources. At each time, a new order/customer/bid is revealed with a request of some resource(s) and a reward. We consider a stochastic setting where all the orders are i.i.d. sampled from an unknown distribution. While the literature on the topic mainly focuses on regret minimization, our paper considers the fairness aspect of the problem. On a high level, we define the fairness in a way that a fair online algorithm should treat similar agents/customers similarly, and the decision made for similar agents/customers should be consistent over time. To achieve this goal, we define the fair offline solution as the analytic center of the offline optimal solution set, and introduce cumulative unfairness as the cumulative deviation from the online solutions to the fair offline solution over time. We propose a fair algorithm based on an interior-point LP solver and a mechanism that dynamically detects unfair resource spending. Our algorithm achieves cumulative unfairness on the scale of order $O(\log(T))$, while maintains the regret to be bounded without dependency on $T$. In addition, compared to the literature, our result is produced under less restrictive assumptions on the degeneracy of the underlying linear program

Advisor: Giesecke Kay
Ye Yinyu
Pelger Markus
Online social network risk management

As online social networks become more influential in society, a new form of cyber threat continues to impact its users: platform manipulation. This occurs when social network users attempt to exploit other users through their online behavior on a social network platform. Current methods for detecting and eliminating platform manipulation rely on the use of machine learning (ML) and artificial intelligence (AI) models. While these methods are able to parse through very large data sets efficiently, they are often trained to only recognize specific traits within user behavior (e.g., spam, malicious links, hate speech), and may not be able to incorporate other forms of evidence (e.g., human observations) in real time without being re-trained and validated, which can be costly and time-consuming in practice. To make an accurate risk assessment of a user's long-term behavior which can be updated over time, decision-makers need a method for integrating multiple forms of evidence (AI and human) across space (by malicious trait) and time into a probability distribution over possible scenarios, as well as knowledge of the potential consequences to the platform from each scenario. In this research, we use probabilistic risk analysis to combine both AI-generated and human-generated evidence in order to determine the risk of individual users to the platform. This allows decision-makers to not only consider the probability that users are malicious, but also their range of potential consequences. We also develop a decision analysis framework for platform referral decisions (whether to request human intervention) using the value of clairvoyance on human agent observations to determine when to involve human agents in the decision-making process (and when to rely on automated decisions to remove users from the platform)

Keywords: Management Science and Engineering.
Advisor: Paté-Cornell M. Elisabeth (Marie Elisabeth)
Ashlagi Itai
Shachter Ross D.
Operando surface-sensitive UV-Vis methods on nickel-based oxygen evolution reaction electrocatalysts

Operando surface-sensitive measurements are crucial for understanding the dynamics of the electrode surface chemistry. However, existing methods, such as adopting the meniscus-method with the ambient pressure X-ray photoelectron spectroscopy, are often inaccessible or expensive, impeding the development of advanced electrodes used in electrocatalysts and batteries. In this thesis, UV-Vis- based operando surface methods are developed to study the dynamic surface of nickel-based electrocatalysts. First, this study applies direct current-based UV-Vis spectroscopy on epitaxial lanthanum nickel oxide (LNO) to study the active surface layer for oxygen evolution reaction (OER). Through careful control of the epitaxial film thickness and terminating atomic layer, this study deconvolutes the surface UV-Vis spectrum from the bulk one and proves the active OER surface phase on Ni-terminating LNO to be a single layer of nickel (oxy)hydroxide. The lack of such a surface layer explains the poor OER activity on La-terminating LNO. Next, the thesis proposes a color impedance spectroscopy (CIS)-based technique that unlocks more accessible, lab- based operando surface measurements on a broader range of systems. The setup controls the depth of charge carriers' movement within the electrode by tuning the frequency of the applied AC signal. Theoretically, any coupled light source yields a surface-sensitive spectrum at higher AC frequencies. A physics-based transmission line impedance model for general mixed ionic-electronic conductors is built to simulate both the EIS and CIS outcome. The impedance model provides insights on EIS results analysis that is crucial for interpreting impedance data. Then, the model validates the idea through forward simulation and demonstrates that bulk kinetics strongly affects the CIS spectral outcome. Adopting nickel (oxy)hydroxide film as a model system presents how CIS turns UV-Vis spectroscopy surface-sensitive. Analysis of samples with varying thicknesses deconvolutes the spectrum and reveals a highly oxidized surface layer with strong absorptivity at redox potential (~1.4 V) and OER conditions (1.6 V vs. RHE). This study expects the CIS experiment and model analysis combination to enable a wide range of novel operando electrode surface studies

Keywords: Chemistry.
Advisor: Chueh William
Kanan Matthew William
Moerner W. E. (William Esco)
Operator shifting and model reduction techniques for efficient and accurate computational physics

This thesis is split into two distinct parts. The first (and principal) part of the thesis will discuss the problem of more accurately estimating the solution of a linear system corrupted by noise. The second part will discuss a handful of gaps in the literature on model reduction and introduce novel techniques to address these shortcomings. In part I, we note that many problems in computational physics require knowledge of quantities subject to uncertainty or noise. For example, one may be interested in simulating the scattering pattern of light passing through a scattering medium, but only have a noisy estimate of the scattering background. Since most computational physics problems reduce to linear systems, we want to build more accurate estimates of linear systems corrupted by noise. While many techniques exist for rectifying this in solution space (i.e., Tikhonov regularization, etc.), we instead examine this problem as one of producing a better estimate of a matrix inverse from a noisy matrix sample. We introduce the operator shifting technique, a loose adaptation of James-Stein estimation to matrices, and examine theoretical guarantees about shift direction and magnitude in both positive definite and general settings, as well as discuss efficient Monte Carlo estimation of shift magnitude via monotonic polynomial truncations of power series. In part II, we will pivot to model reduction techniques. While operator shifting helps provide more accurate solutions to physics problems, model reduction helps provide efficiently computable surrogate models for computationally expensive high-fidelity physics models. We will discuss novel model reduction techniques for integral equations and dense linear systems, as well as new adaptive refinement and compression schemes for linear reduced models. Finally, we will demonstrate new work in realtime model reduction for soft-body physics

Advisor: Ying Lexing
Chiaramonte Maurizio M. 
Papanicolaou George
Opium, economic thought, and the making of Britain's free trade empire, 1773-1839

This dissertation provides a history of the East India Company's opium trade in Asia from the creation of a monopoly in Bengal in 1773 to the outbreak of the First Opium War in 1839. It examines debates over the commodification of opium in Bengal to explain the origins of the "imperialism of free trade.".

Keywords: History.
Advisor: Satia Priya
Como David R.
Press Steven
Optical fiber-based silicon sensors and systems

Modern sensors optimized for a small footprint, high resolution, scalable production, and networkability are typically microelectromechanical systems. However, optical fiber-based sensor systems offer improvements in all of these metrics with the added benefit of immunity to electromagnetic interference. I have developed an optical fiber-based sensor platform that uses standard communications hardware for wavelength-multiplexed, optically-balanced, and thermally-compensated measurements of sensors arrays. I have also developed system-compatible silicon pressure, temperature, and acoustic sensors that I nanofabricate at low-cost on the wafer scale. I focus on the design, fabrication, characterization, and system-integration of these optical fiber sensors that are among the world's smallest and most sensitive for their target phenomena. For aerospace, cellular detection, and acoustic localization applications, I show that my optical fiber sensor systems have sufficient performance to outcompete even microelectromechanical systems

Keywords: Electrical Engineering.
Advisor: Solgaard Olav
Kahn Joseph M. (Joseph Mardell)
Vuckovic Jelena
Optimal substrate for direct detection of light dark matter

The topic of dark matter (DM) and its possible interaction with ordinary matter has occupied the central stage of the field of particle physics in recent decades. One line of research attempts to directly detect such interaction in a controlled lab environment with extremely sensitive (and in some cases even quantum-limited) sensors. SuperCDMS is one such experiment that utilizes transition-edge sensors (TES) fabricated on large silicon or germanium crystals cooled to cryogenic temperatures (around 50 mK). Silicon and germanium are typically used as the substrate material since they are cost-efficient and well-understood. Numerous alternative substrates have been proposed over the years but these novel substrates are often complex and difficult/costly to grow or make. In other cases they may require significant improvement in detector technology in order to work as intended. As such silicon and germanium remain the dominant substrate material in DM direct detection experiments. Expanding the repertoire of substrate materials is valuable. It can lead to better sensitivity to DM interactions, and a higher degree of material complementarity across different detectors and experiments. The latter point is especially important as we move deeper into the low mass region and look for lighter and lighter DM particles. We can begin to encounter backgrounds caused by non-perturbative many-body effects that are highly material-specific. Having substrates of different materials operating under similar conditions provides information about the nature of such backgrounds as we can compare the response in different materials to break any potential degeneracy not resolved by just having silicon and germanium. The work presented in this thesis is an attempt to systematically survey and understand the various solid state physics pertinent to the DM direct detection, and as application suggest criteria for the optimal substrate material to be used for various interaction channels. Towards the end of this work we will present concrete examples of substrates that can potentially be used in the next generation of DM detection experiments

Keywords: Physics.
Advisor: Cabrera Blas
Kurinsky Noah
Graham Peter (Peter Wickelgren)
Kuo Chao-Lin (Physics professor)