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Implementing a Digital Common Application for Affordable Housing in Massachusetts
No full textThe need for affordable housing in Massachusetts is immense, with fragmented housing application processes further compounding barriers for low-income residents to access stable housing. To address these challenges, the Massachusetts Executive Office of Housing and Livable Communities (EOHLC) initiated the development of a digital common application (Common App) in 2024 to streamline tenant application and selection processes for privately owned publicly subsidized housing opportunities throughout the state. This client-based thesis offers an implementation roadmap for EOHLC to successfully operationalize the Common App within the agency.
The roadmap is structured around three topics as requested by EOHLC: (1) organizational design considerations as the Common App scales, including internal staffing models, external vendor relationship management, and budget planning; (2) long-term technical integration opportunities, including identifying relevant data systems likely to interact with the Common App and potential areas for alignment; and (3) compliance mechanisms to ensure housing providers’ participation in the Common App, including a review of Massachusetts fair housing regulations as one possible strategy to require or incentivize providers to use the platform.
Each topic draws from a review of state policies as well as academic literature in organization studies, information systems, and public administration; stakeholder interviews; and case study research on digital affordable housing search and application platforms in Massachusetts, Detroit, San Francisco, and the Bay Area—culminating in a series of recommendations for EOHLC to effectively administer the Common App over the long term.M.B.A.M.C.P
Toward a political economy of the power sector: green capitalism, eco-socialism, and co-operative power in decarbonized climate policy
No full textThe political economy of the power sector has been characterized by a putative transition from fossil capitalism to green capitalism in an attempt to mitigate the worst effects of anthropogenic climate change on nature and society. In recent years the rise of green industrial policy, such as the passage of the Inflation Reduction Act of 2022, has sought to stimulate domestic economic development of green-technology projects and implement protectionist trade policies with the normative intent of protecting the geopolitical hegemony of U.S. industry. Yet the objectives of such industrial policies, which function less to reduce carbon emissions than to increase resource- and carbon-intensive consumption patterns, run antithetical to putative state objectives of the decarbonization of the power grid and industrial operations, and in fact green capitalism does not exist without the continued influence of fossil capital.
In this thesis I look to Marxist theories of the state, capital, labor, and nature to illustrate the crises of capitalism that have been occurring due to the exponential increase in power demand by data centers and large technology companies. In reshaping the governance of power markets, electricity generation, and transmission and distribution infrastructure through this increase in demand, called load growth, I show the illusion of sustainability under a green-capitalist political economy that purports to advance decarbonization goals, yet which in actuality facilitates conditions for the centralization and monopolization of private capital, as well as the continued destruction of nature and exploitation of workers. However, this crisis of load growth and the issue of governance that it raises open a window for experimentation into new state systems, socialized modes of production, and labor and environmental solidarity in the creation of a new climate policy: one that prioritizes equity, welfare, ecological preservation, and a truly decarbonized society. I propose a socialization of the power sector to increase community autonomy over their energy needs and to begin to dismantle the technocratic influence of fossil-fuel and large technology companies over electricity generation and access.M.C.P
Characterizing microearthquakes and shallow structure with dense array and optical fibers
No full textSource properties of small earthquakes, such as source dimension and stress drop, help us to constrain source physics and assess seismic hazards. Small events carry information about the stress state in the subsurface. They also help us predict the behavior of larger earthquakes. However, the source properties of small earthquakes (magnitude less than 3) are poorly constrained because of trade-offs with other wave propagation effects. The trade-offs with attenuation can cause the apparent stress drop to vary, resulting in an apparent breakdown of earthquake self-similarity. To date, researchers are still trying to understand the uncertainty in source parameter measurements and to improve their accuracy. In the first part of the thesis, I use a dense array in Oklahoma to investigate the influence of site effects on source parameter modeling. By analyzing ground motions, subsurface velocity structure, and attenuation, I show how these factors relate to site effects, and how source parameter estimations vary under different modeling assumptions. To avoid large site-effect-related biases and uncertainties when modeling source parameters, I suggest (1) assuming a realistic attenuation model, (2) using selected stations on hard rocks instead of using many stations with unknown site conditions, and (3) constraining variables in the model during the inversion to avoid parameter trade-offs.
In the second part of the thesis, I explore the use of fiber-optic cables in several seismic applications. Distributed Acoustic Sensing (DAS) turns optical fibers into dense receiver arrays. These fiber-optic cables have the advantage of being resilient and easier to maintain compared to mechanical sensors. The cable provides a dense array that helps us separate source and wave propagation effects for different purposes. Here, I use cables in wells in geothermal reservoirs and a telecom cable on the MIT campus. The applications include structure monitoring and imaging, seismic hazard assessment, and earthquake source characterization. DAS measures strain and requires special considerations to fit into conventional seismic methods built on particle motions. Deconvolution-based methods help deal with the DAS instrument response. The gauge length adds a velocity-dependent amplitude response that we need to consider when modeling the DAS spectrum. I provide workflows for conducting seismic imaging surveys using telecom cable and downhole DAS for temporal monitoring and source parameter analysis. The cables can reach places that were difficult to reach in the past. With careful processing, DAS can be a promising tool for structure monitoring, urban seismic hazard assessment, and microearthquake source analysis.Ph.D
Accurate Protein Function Prediction with Graph Transformer-Based Function Localization
No full textProtein function prediction is a fundamental challenge in biology, crucial for understanding biological processes, disease mechanisms, and accelerating drug discovery. While computational methods leveraging sequence or structural information have advanced, accurately translating protein structure to function and pinpointing the specific residues responsible remain significant hurdles. Many existing deep learning approaches fall short, often relying on post-hoc analyses that lack specificity or fail to directly integrate functional site identification into the prediction process. In this study, we introduce the Protein Region Proposal Network (ProteinRPN), a novel graphbased deep learning framework designed to address these limitations. ProteinRPN is the first model to integrate the proactive identification of functional regions within the Gene Ontology term prediction pipeline. The core of the model is a Region Proposal Network module that processes protein structure graphs (residues as nodes, contacts as edges) to identify potential functional regions, termed anchors. These anchors are subsequently refined using a multi-stage process involving a novel differentiable node drop pooling layer that incorporates domain knowledge. A functional attention layer further enhances the representations of predicted functional nodes, and a Graph Multiset Transformer aggregates this localized information into a comprehensive graph-level embedding for final prediction. The model is optimized using a combination of cross-entropy classification loss, supervised and self-supervised contrastive learning losses (SupCon and InfoNCE) for robust representation learning. Evaluated on standard benchmarks derived from the DeepFRI/HEAL datasets, ProteinRPN demonstrates state-of-the-art performance, consistently outperforming existing sequencebased and structure-based methods across all three Gene Ontology domains (Molecular Function, Biological Process, Cellular Component) based on standard CAFA metrics (Fmax, AUPR, Smin). Notably, ProteinRPN achieves significant improvements over strong baselines like HEAL, with AUPR (Area under Precision Recall curve) gains of approximately 15.4% (BP), 8.5% (CC), and 1.3% (MF). Furthermore, ablation studies validate the contribution of each key component, particularly the region proposal mechanism. Qualitative analysis confirms the model’s ability to accurately localize known functional residues within protein structures, offering enhanced interpretability. By directly modeling and identifying functionally relevant structural regions, ProteinRPN presents a robust, interpretable, and high-performing approach to structure-based protein function prediction. This work contributes a novel framework that bridges the gap between structural information and functional annotation, offering potential for deeper biological insights and advancing computational tools for understanding the proteome.S.M
Accelerating Diverse Cell-Based Therapies Through Scalable Design
No full textAugmenting cells with novel, genetically encoded functions will support therapies that expand beyond natural capacity for immune surveillance and tissue regeneration. However, engineering cells at scale with transgenic cargoes remains a challenge in realizing the potential of cell-based therapies. In this review, we introduce a range of applications for engineering primary cells and stem cells for cell-based therapies. We highlight tools and advances that have launched mammalian cell engineering from bioproduction to precision editing of therapeutically relevant cells. Additionally, we examine how transgenesis methods and genetic cargo designs can be tailored for performance. Altogether, we offer a vision for accelerating the translation of innovative cell-based therapies by harnessing diverse cell types, integrating the expanding array of synthetic biology tools, and building cellular tools through advanced genome writing techniques
Invertible Functorial Field Theory for Symmetry Breaking and Interactions in Quantum Field Theory
No full textWe apply invertible field theories to study two questions in quantum field theory. Specifically, we study reflection-positive fully-extended invertible field theories on manifolds with twisted spin structures, which are computed as Anderson-dual bordism groups [1, 2].
In high energy physics, invertible field theories represent anomalies of quantum field theories. Our first application is toward ’t Hooft anomaly matching—a method first developed in the 1980s in which one treats anomalies as invariants of theories of interest and uses them to compute how quantum field theories change under physical processes. Specifically, we model three related processes around a form of spontaneous symmetry breaking via a charged order parameter using a twisted Gysin sequence of Anderson-dual bordism groups. We study the Smith maps of Madsen-Tillmann spectra that underlie the sequence, collecting examples and cataloging periodicities. Finally, we compute an extensive set of examples of physical interest and draw physical predictions from the results.
In condensed matter physics, invertible field theories model the low energy field theories of symmetry-protected topological phases (SPTs). In this second application, we develop and compute homotopical free-to-interacting maps to compare two classifications of fermionic SPTs: those for free (i.e. non-interacting) models, and more general interacting classifications. These maps contribute to what has been a prolific line of research in the physics literature for the past fifteen years. Generalizing Freed--Hopkins [1], we construct maps from K-theory to twisted spin IFTs using T-duality and twisted versions of the spin orientation of K-theory [3]. We focus on two situations: weak phases [4, 5], which are SPTs protected by discrete translation symmetry, and primed phases [6], which are closely related to the famous tenfold way [7, 8], but which have a very different interacting classification. In the latter case, we demonstrate the dependence of the interacting classification on more than the Morita class of the symmetry algebra.Ph.D
Hybridizable Discontinuous Galerkin Methods for the Two-Dimensional Monge–Ampère Equation
No full textWe introduce two hybridizable discontinuous Galerkin (HDG) methods for numerically solving the two-dimensional Monge–Ampère equation. The first HDG method is devised to solve the nonlinear elliptic Monge–Ampère equation by using Newton’s method. The second HDG method is devised to solve a sequence of the Poisson equation until convergence to a fixed-point solution of the Monge–Ampère equation is reached. Numerical examples are presented to demonstrate the convergence and accuracy of the HDG methods. Furthermore, the HDG methods are applied to r-adaptive mesh generation by redistributing a given scalar density function via the optimal transport theory. This r-adaptivity methodology leads to the Monge–Ampère equation with a nonlinear Neumann boundary condition arising from the optimal transport of the density function to conform the resulting high-order mesh to the boundary. Hence, we extend the HDG methods to treat the nonlinear Neumann boundary condition. Numerical experiments are presented to illustrate the generation of r-adaptive high-order meshes on planar and curved domains
Quantum power flows: from theory to practice
No full textThe high-level integration of spatial-dispersed renewable energies can greatly enlarge future smart grid size and complicate system operations. Existing numerical methods based on classical computational oracles may be challenged to fulfill efficiency requirements for future smart grid evaluations, where modern advanced computational technologies, specifically quantum computing, have significant potential to help. In this paper, we discuss applications of quantum computing algorithms toward state-of-the-art smart grid problems. We suggest potential, exponential quantum speedup by the use of the Harrow-Hassidim-Lloyd (HHL) algorithms for solving sparse linear systems of equations in Newton’s method of power-flow problems. However, practical implementations of the algorithm are limited by the noise of quantum circuits, the hardness of realizations of quantum random access memories (QRAM), and the depth of the required quantum circuits. We benchmark the hardware and software requirements from the state-of-the-art power-flow algorithms, including QRAM requirements from hybrid phonon-transmon systems, and explicit gate counting used in HHL for explicit realizations. We also develop near-term algorithms of power flow by variational quantum circuits and implement physical experiments for 6 qubits with a truncated version of power flows
Unlocking the Potential of MBenes in Li/Na-Ion Batteries
No full textMBenes, an emerging family of two-dimensional transition metal boride materials, are gaining prominence in alkali metal-ion battery research owing to their distinctive stratified architecture, enhanced charge transport properties, and exceptional electrochemical durability. This analysis provides a comprehensive examination of morphological characteristics and fabrication protocols for MBenes, with particular focus on strategies for optimizing energy storage metrics through controlled adjustment of interlayer distance and tailored surface modifications. The discussion highlights these materials’ unique capability to host substantial alkali metal ions, translating to exceptional longevity during charge–discharge cycling and remarkable high-current performance in both lithium and sodium battery systems. Current obstacles to materials development are critically evaluated, encompassing precision control in nanoscale synthesis, reproducibility in large-scale production, enhancement of thermodynamic stability, and eco-friendly processing requirements. Prospective research pathways are proposed, including sustainable manufacturing innovations, atomic-level structural tailoring through computational modeling, and expansion into hybrid energy storage-conversion platforms. By integrating fundamental material science principles with practical engineering considerations, this work seeks to establish actionable frameworks for advancing MBene-based technologies toward next-generation electrochemical storage solutions with enhanced energy density and operational reliability
Measurement of the multiplicity dependence of Υ production ratios in pp collisions at √s = 13 TeV
No full textThe Υ(2S) and Υ(3S) production cross-sections are measured relative to that of the Υ(1S) meson, as a function of charged-particle multiplicity in proton-proton collisions at a centre-of-mass energy of 13 TeV. The measurement uses data collected by the LHCb experiment in 2018 corresponding to an integrated luminosity of 2 fb−1. Both the Υ(2S)-to-Υ(1S) and Υ(3S)-to-Υ(1S) cross-section ratios are found to decrease significantly as a function of event multiplicity, with the Υ(3S)-to-Υ(1S) ratio showing a steeper decline towards high multiplicity. This hierarchy is qualitatively consistent with the comover model predictions, indicating that final-state interactions play an important role in bottomonia production in high-multiplicity events