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From Vacant to Valuable: Building Community Wealth through Brownfield Redevelopment in Legacy Industrial Cities
No full textRecent federal investments in domestic manufacturing have renewed economic interest in legacy industrial cities across the United States. As these places attract new development, it is critical to safeguard against repeating the harms of the 20th-century exodus of industry and manufacturing jobs—when offshoring, suburbanization, and discriminatory housing policies deepened spatialized racial and economic inequalities. How can communities retain the wealth generated by new industrial investments, even if companies leave? This thesis explores how industrial brownfield redevelopment might utilize community wealth-building (CWB) strategies to advance equitable economic development. Focusing on the work of the Site Readiness for Good Jobs Fund in Cleveland, Ohio—a nonprofit preparing long-vacant industrial land for job-dense uses—it examines the potential for mission-driven organizations to use brownfield redevelopment to anchor wealth locally and proactively resist displacement. By analyzing case studies in Buffalo, Milwaukee, Chicago, and Philadelphia, the research tackles three questions: How do mission-driven organizations deliver community benefits through industrial brownfield redevelopments? In what ways do CWB models reshape how capital flows through redevelopment projects? And, what questions and decisions must the Site Readiness Fund consider to build lasting community wealth in Cleveland? Findings suggest that industrial brownfield redevelopment, when paired with strategic partnerships, site control, and a clear vision, offers a unique opportunity to implement CWB models. These strategies can help mission-driven organizations redistribute the risks and rewards of necessary public investments in brownfields and build trust with the community, ensuring that residents surrounding these reactivated sites benefit not just from new jobs, but from ownership and long-term economic power over their futures. The thesis concludes by applying these lessons to the Site Readiness Fund, outlining potential paths forward that embed economic democracy in the redevelopment of Cleveland’s legacy industrial areas.M.C.P
ΔB₀ Field Control in High Field MRI with Local Multcoil Shim Arrays
No full textLocal multicoil ΔB₀ shim arrays enable low-cost, simple to fabricate, and physically small, static magnetic field control in magnetic resonance imaging. The presented thesis will show frameworks for coil current calculation for homogeneity and novel selective excitation applications. As MRI RF coils trend towards repositionable and flexible systems for their ease of use and tight-to-the-patient fit, ΔB₀ shim arrays are left behind for lack of rapid, patient-on-the-table calibration. We show an inverse problem approach with physics-based regularization and adaptation to accelerate calibration by over 50 fold. The numerical tools developed for calibration also proved useful for design to enable novel upper bounds on ΔB₀ shim performance and new tools for automatic application and anatomy-specific local multicoil array design.Ph.D
Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication
No full textIUI ’25, Cagliari, ItalyTextual content (including titles, annotations, and captions) plays a central role in helping readers understand a visualization by emphasizing, contextualizing, or summarizing the depicted data. Yet, existing visualization tools provide limited support for jointly authoring the two modalities of text and visuals such that both convey semantically-rich information and are cohesively integrated. In response, we introduce Pluto, a mixed-initiative authoring system that uses features of a chart’s construction (e.g., visual encodings) as well as any textual descriptions a user may have drafted to make suggestions about the content and presentation of the two modalities. For instance, a user can begin to type out a description and interactively brush a region of interest in the chart, and Pluto will generate a relevant auto-completion of the sentence. Similarly, based on a written description, Pluto may suggest lifting a sentence out as an annotation or the visualization’s title, or may suggest applying a data transformation (e.g., sort) to better align the two modalities. A preliminary user study revealed that Pluto’s recommendations were particularly useful for bootstrapping the authoring process and helped identify different strategies participants adopt when jointly authoring text and charts. Based on study feedback, we discuss design implications for integrating interactive verification features between charts and text, offering control over text verbosity and tone, and enhancing the bidirectional flow in unified text and chart authoring tools
ALFA-Chains: An Artificial Intelligence Approach to Exploit Chain Discovery in Networks
No full textExploit chains play a crucial role in advanced persistent threats (APTs) and other malicious cyber campaigns. Sophisticated attackers can navigate across a network, escalate their privileges, and compromise valuable targets by executing the right exploits in the right order. However, finding these exploits chains is a challenging task requiring a broad knowledge of the vulnerabilities present in computer systems and the exploits that take advantage of them. Networks can be complex, with many hosts and intricate software stacks. Moreover, the range of known exploits and vulnerabilities is constantly growing, complicating the process of determining how they can be linked. This thesis introduces a solution, ALFA-Chains, that automates the discovery of exploit chains by leveraging classical AI planning, Large Language Models (LLMs), and existing exploit/vulnerability databases. ALFA-Chains describes networks and exploits using the Planning Domain Description Language (PDDL), a formal language to represent planning problems. This allows us to use optimized off-the-shelf planners that have been developed by the AI planning community over many years. Our system takes natural language descriptions of exploits and classifies them into categories based on their preconditions and effects. From this intermediary representation, we can programmatically generate PDDL that captures the requirements needed to run the exploit and the access gained by the attacker. Due to this automated approach, ALFA-Chains is able to consider a vast set of exploits when determining if a network is susceptible to exploit chaining. We show how ALFA-Chains can process 1,880 Metasploit exploits and their corresponding 2,002 CVEs to detect exploit chains in a variety of realistic network configurations. We proceed to discuss potential applications of ALFA-Chains, including automated penetration testing and vulnerability prioritization.M.Eng
Learning from Past Market Outcomes: Evidence from the Music Industry
No full textWe leverage unique features of music albums to investigate how musicians learn from current products when developing new products. We find that songs on a musician’s next album tend to be more similar to the songs that are more successful on that musician’s current album. This effect is stronger when the musician has less experience, and when the song on the current album is more novel (for that musician). Our findings suggest that musicians learn from the success of previous songs when developing new songs, and that learning is stronger if the musician has more need to learn, and when the song contains more new information.S.M
Multimodal Graphical User Interface for 3D Model Fabrication Through Generative AI
No full textIn recent years, three-dimensional model generation and manipulation through generative AI has seen significant developments. Current projects enable the generation of threedimensional assets from natural language prompts and input images, as well as functionalityaware model manipulation through mesh segmentation and categorization. However, all these workflows lack a coherent, unified platform that caters to users’ needs and each method’s technologies. Programs that rely on terminal-based commands lack the graphics needed for model interactions, and plugin extensions for 3D modeling applications are unintuitive and hard to extend for new functionalities. Additionally, both approaches require users to have prior computer engineering and/or 3D graphics knowledge. For this thesis, I propose the creation of a web-based, multimodal graphical user interface that consolidates all these different technologies in a single platform. By supporting model stylization and model generation (both from text prompts and input images), users can utilize combined workflows and expand the range of output possibilities for 3D asset creation. Other features in our interface include model uploading, saving, and downloading to enable a continuous stream of work on a single 3D asset. Apart from all this, we expand the current capabilities of existing image-to-3D generation programs by enabling users to combine up to six images together and create a merged 3D object. Each of these images corresponds to a view angle from which the outputted mesh will be built.M.Eng
Perseus: a simple and optimal high-order method for variational inequalities
No full textThis paper settles an open and challenging question pertaining to the design of simple and optimal high-order methods for solving smooth and monotone variational inequalities (VIs). A VI involves finding x ⋆ ∈ X such that ⟨ F ( x ) , x - x ⋆ ⟩ ≥ 0 for all x ∈ X . We consider the setting in which F : R d → R d is smooth with up to ( p - 1 ) th -order derivatives. For p = 2 , the cubic regularization of Newton’s method has been extended to VIs with a global rate of O ( ϵ - 1 ) (Nesterov in Cubic regularization of Newton’s method for convex problems with constraints, Tech. rep., Université catholique de Louvain, Center for Operations Research and Econometrics (CORE), 2006). An improved rate of O ( ϵ - 2 / 3 log log ( 1 / ϵ ) ) can be obtained via an alternative second-order method, but this method requires a nontrivial line-search procedure as an inner loop. Similarly, the existing high-order methods based on line-search procedures have been shown to achieve a rate of O ( ϵ - 2 / ( p + 1 ) log log ( 1 / ϵ ) ) (Bullins and Lai in SIAM J Optim 32(3):2208–2229, 2022; Jiang and Mokhtari in Generalized optimistic methods for convex–concave saddle point problems, 2022; Lin and Jordan in Math Oper Res 48(4):2353–2382, 2023). As emphasized by Nesterov (Lectures on convex optimization, vol 137, Springer, Berlin, 2018), however, such procedures do not necessarily imply the practical applicability in large-scale applications, and it is desirable to complement these results with a simple high-order VI method that retains the optimality of the more complex methods. We propose a p th -order method that does not require any line search procedure and provably converges to a weak solution at a rate of O ( ϵ - 2 / ( p + 1 ) ) . We prove that our p th -order method is optimal in the monotone setting by establishing a lower bound of Ω ( ϵ - 2 / ( p + 1 ) ) under a generalized linear span assumption. A restarted version of our p th -order method attains a linear rate for smooth and p th -order uniformly monotone VIs and another restarted version of our p th -order method attains a local superlinear rate for smooth and strongly monotone VIs. Further, the similar p th -order method achieves a global rate of O ( ϵ - 2 / p ) for solving smooth and nonmonotone VIs satisfying the Minty condition. Two restarted versions attain a global linear rate under additional p th -order uniform Minty condition and a local superlinear rate under additional strong Minty condition
Deep Learning-Based Classification of Phonotraumatic Vocal Hyperfunction Severity from Stroboscopic Images
No full textPhonotraumatic vocal hyperfunction (PVH) is a vocal disorder characterized by damaged vocal folds from excessive or abusive voice use. Clinical assessment of PVH relies on timeconsuming videostroboscopy examination, which poses challenges for large-scale clinical studies. We address the need for more efficient clinical assessment tools by proposing deep learning approaches for automatically detecting PVH severity from stroboscopic images. One of the main challenges in building deep learning models for this task is a lack of labeled stroboscopy data. Motivated by this challenge, we explore two approaches: direct classification and segmentation-then-classification. In the segmentation-then-classification approach, we first train a model to segment the glottis, a clinically relevant part of the vocal fold anatomy. Then, we use the predicted segmentation along with the stroboscopic image as inputs into a classification model. This approach helps to guide the model towards key anatomical features. We achieve up to 0.53 accuracy in four-class PVH severity prediction with the direct classification approach. Incorporating glottal segmentations improves the accuracy to 0.64, underscoring the value of providing anatomically-informed segmentations when assessing PVH severity. By creating an automated PVH severity tool, our work has the potential to help clinicians more efficiently monitor disease progression and to facilitate large-scale screening, thereby contributing to improved patient care.M.Eng
Algorithmic Advances in Range-Aided Navigation
No full textThis thesis contributes to the advancement of range-aided simultaneous localization and mapping (RA-SLAM) through algorithmic developments and real-world demonstrations. Broadly speaking, SLAM is the process by which an agent combines sensor measurements to simultaneously create a map of the world and localize itself within this map. SLAM has been called the ‘holy grail’ of field robotics, and in many instances it is a critical enabling capability for autonomous agents to operate in the real world. RA-SLAM is the specific case of SLAM which incorporates point-to-point distance measurements (e.g., distance measurements between an autonomous underwater vehicle and an acoustic buoy) into the inference process. The ability to leverage such measurements is desirable, as they can help in resolving ambiguities (e.g., am I in hallway A or B) and the relevant sensors are often low-cost and simple to integrate (and thus pose the potential to be widely deployed). However, there are theoretical challenges that have historically limited the reliability of RASLAM approaches. At the root of these challenges is the issue that a single range measurement does not uniquely determine the relative position between two points. In state-of-the-art RASLAM formulations, this ambiguity manifests as non-convexity in the maximum a posteriori inference problem. As a result of this non-convexity, standard local-search optimizers are highly dependent on quality initializations to obtain the correct state estimate. To address this issue of reliability, this thesis presents the first certifiably correct algorithm for RA-SLAM. This algorithm, Certifiably Correct RA-SLAM (CORA), is capable of (i) obtaining globally optimal solutions for many real-world RA-SLAM problem instances and (ii) providing certificates of correctness for these solutions. CORA leverages a novel semidefinite programming (SDP) relaxation of the RA-SLAM problem, which it solves efficiently using the Riemannian Staircase methodology. This methodology allows CORA to typically obtain globally optimal solutions faster than the existing state-of-the-art local solvers. These results expand our understanding of problems suited for efficient global solvers and highlight the key problem structures that appear necessary to develop and deploy such solvers, pointing towards exciting future directions in trustworthy model-based autonomy. We demonstrated the performance of CORA on a range of real-world RA-SLAM datasets, including a set of large-scale multi-agent experiments conducted as part of this work. In these experiments CORA reliably estimates agents’ trajectories in both single- and multi-robot settings. CORA gracefully scales to large problems consisting of multiple agents and tens of thousands of robot poses. These experiments not only validate CORA’s performance, but also fill an existing gap in open-source datasets available to the research community and provide practical insights to guide future deployments of autonomous navigation systems in large, complex environments.Ph.D
Transcriptomic insights into methanol utilization in Pichia pastoris lacking AOX genes under co-feeding conditions
No full textThe methylotrophic yeast Pichia pastoris (P. pastoris) exhibits remarkable capability for methanol-driven protein biosynthesis, positioning it as an attractive platform for carbon-neutral biomanufacturing utilizing methanol as a renewable feedstock. However, challenges arising from methanol metabolism, particularly the accumulation of toxic formaldehyde intermediates, significantly hinder efficient methanol biotransformation. To address this limitation, we implemented a metabolic engineering strategy involving dual knockout of alcohol oxidase genes (aox1 and aox2) combined with glycerol co-substrate supplementation. Using enhanced green fluorescent protein (EGFP) as a model heterologous product, we demonstrated that the ΔAOX1/2 strain achieved superior protein productivity in glycerol-methanol co-feeding cultures. Under optimized conditions (0.5% methanol + 0.4% glycerol), the engineered strain attained a biomass density of 38.5 (OD600) and EGFP fluorescence intensity of 494,723 units, representing improvements of 32.8% and 53.6%, respectively, compared to the wild-type (WT) strain cultivated with 1% methanol alone. Transcriptome profiling revealed that the observed enhancement in protein synthesis originated from optimized methanol utilization through coordinated upregulation of both assimilatory and dissimilatory metabolic modules. This study demonstrates that alcohol oxidase suppression coupled with glycerol co-metabolism constitutes an effective strategy to alleviate methanol-derived metabolic stress while enhancing heterologous protein yields in P. pastoris