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Programs, Origins, and Niches of Immunomodulatory Myeloid Cells in Gliomas
No full textABSTRACTGliomas are incurable malignancies notable for an immunosuppressive microenvironment with abundant myeloid cells whose immunomodulatory properties remain poorly defined. Here, utilizing scRNA-seq data for 183,062 myeloid cells from 85 human tumors, we discover that nearly all glioma-associated myeloid cells express at least one of four immunomodulatory activity programs: Scavenger Immunosuppressive, C1Q Immunosuppressive, CXCR4 Inflammatory, and IL1B Inflammatory. All four programs are present in IDH1 mutant and wild-type gliomas and are expressed in macrophages, monocytes, and microglia whether of blood or resident myeloid cell origins. Integrating our scRNA-seq data with mitochondrial DNA-based lineage tracing, spatial transcriptomics, and organoid explant systems that model peripheral monocyte infiltration, we show that these programs are driven by microenvironmental cues and therapies rather than myeloid cell type, origin, or mutation status. The C1Q Immunosuppressive program is driven by routinely administered dexamethasone. The Scavenger Immunosuppressive program includes ligands with established roles in T-cell suppression, is induced in hypoxic regions, and is associated with immunotherapy resistance. Both immunosuppressive programs are less prevalent in lower-grade gliomas, which are instead enriched for the CXCR4 Inflammatory program. Our study provides a framework to understand immunomodulatory myeloid cells in glioma, and a foundation to develop more effective immunotherapies.Author's Origina
Transforming Housing for Individuals with Intellectual and Developmental Disabilities In New York State: A Study on AI Technology and Cost-Benefit Analysis
No full textThe lack of accessible, affordable housing for individuals with intellectual and developmental disabilities (IDD) remains a significant challenge, exacerbating existing social, economic, and healthcare disparities. This thesis explored the intersection of artificial intelligence (AI) and modular construction as a potential solution to improving housing accessibility, affordability, and quality of life for adults with IDD in New York State. I used a mixed-methods approach to analyze traditional affordable housing models alongside innovative AI-integrated solutions, evaluating their financial feasibility and long-term impact on residents and caregivers.
A comparative cost-benefit analysis (CBA) revealed that while traditional affordable housing projects require significant upfront investment, they fail to address ongoing care inefficiencies and accessibility barriers. By contrast, AI-enhanced housing, incorporating smart monitoring, automated care systems, and adaptive living environments, demonstrated significant cost savings and quality-of-life improvements. The AI-integrated model I developed reduced caregiver turnover, minimized medical errors, and lowered legal liabilities associated with neglect and injury, leading to an estimated per-resident annual savings of 28,730 for level 2, and 12,885,519, far exceeding the $963,019 NPV of the traditional model. These findings highlight the urgent need for policy reform to support AI-driven innovation in affordable housing.
Ultimately, this research underscores the critical role of technology in bridging the accessibility gap between current housing options and new affordable housing for disabled individuals, ensuring that adults with IDD can live with dignity, independence, and the support they need to thrive. Integrating AI and modular construction can build a more inclusive and financially viable future for housing for individuals with disabilities.Extension Studie
How Pain Came to Matter: The Global Political Economy of the United States Opioid Crisis
No full textAuthor's Origina
Use of Unethically Obtained Human Tissues: Eugenics and Brain Research under National Socialism
No full textAuthor's Origina
Three Essays on Data-Driven Personalization and Targeting for Marketing Interventions
No full textCompanies today face significant challenges in personalizing marketing interventions while balancing long-term objectives, adhering to privacy regulations, and managing complex decision spaces. This dissertation addresses these core issues through three interconnected essays and provides practical methodologies for enhancing personalized marketing interventions in today’s data-driven environment.
The first essay (Chapter 1) demonstrates and tackles the challenges of optimizing long-term business performance through targeted interventions. It highlights how cumulative unexplained variations in repeated customer behavior can weaken the direct optimization of long-term out- comes. To address this, the essay introduces a surrogate index methodology using short-term signals, coupled with a novel separate imputation strategy to handle the churn and purchase processes driving customer value. Through simulations and a real-world marketing application, the essay demonstrates that the proposed approach significantly outperforms traditional methods in enhancing long-term targeting effectiveness.
The second essay (Chapter 2) examines how Local Differential Privacy (LDP) — a strong privacy technique that introduces noise into individual-level data — affects the accuracy of personalized marketing interventions based on Conditional Average Treatment Effect (CATE) predictions. We show that LDP induces heterogeneous and model-dependent errors that hinder accurate personalization. To address these limitations, we propose an honest post-processing method using an unbiased but noisy proxy combined with iterative boosting and a subgroup cross-learning strategy to ensure honesty and mitigate overfitting. Empirical tests demonstrate that our method significantly improves prediction accuracy and treatment prioritization, enabling organizations to achieve effective personalization despite privacy constraints.
The final essay (Chapter 3) introduces Incrementality Representation Learning (IRL), a novel multitask framework for predicting heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets personalized interventions without extensive testing. It extracts generalizable low-dimensional representations of intervention features and customer covariates. Empirical validation using data from 274 promotional campaigns demon- strates that IRL significantly enhances targeting accuracy and effectively generalizes predictions to both known and untested interventions and customer segments, addressing challenges in high-dimensional decision spaces and cold-start scenarios. Additionally, the essay develops a decision framework and interpretation tool to assist firms in identifying critical design features and tailoring promotions for maximum profitability.Business Administratio
Driving Security? U.S. Auto Industrial Policy in a Changing World
No full textIndustrial policy may have come back into fashion—controversially—in the last few years, but it was arguably never out for the auto sector. Auto manages to have outsized political importance in the U.S., and around the world, thanks to its relatively high paying jobs for workers without college education, unionization, and the political romance around manufacturing jobs. That political pull wins the auto sector repeat special treatment. Today’s U.S. industrial policy is not about picking winners for export potential, unlike 20th century industrial policy, but about economic security. It aims either to build domestic capacity in industries viewed as pivotal (semiconductors), achieve geopolitically secure supply by reducing dependence on China (critical minerals, rare earths), or protect politically important domestic industries and their share in the U.S. market (autos). Today is different. Auto is transforming rapidly in the shift to electric and autonomous vehicles (EVs and AVs)—a change that requires different engineering and supply chains. Automakers around the world continue to emphasize that EVs are the future of the industry, although Toyota is a notable skeptic. The EV shift is upending a long-stable competitive landscape. China’s indigenous EV automakers have become major producers and exporters in a handful of years. The Chinese government is pervasively involved in the growth of its EV industry, while having developed a national monopoly position in global EV supply chains. Adding to security considerations, domestic auto manufacturing offers contingent defense manufacturing capability—as has long been true— while the computerization of vehicles raises new surveillance and cyberattack risks. This article examines today’s U.S. auto industrial policy in international and technological context, reviews the short track record of Biden EV policies, discusses how we should evaluate Trump’s auto industrial policy, and closes with recommendations for where industrial policy would be best focused to support the U.S. auto sector.Version of Recor
Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings
No full textHigh-dimensional data are increasingly common across fields such as genomics, economics, and neuroscience, often challenging conventional statistical methods. This dissertation develops new tools for uncertainty quantification in high-dimensional settings where standard assumptions—like sparsity or data homogeneity—may not apply. The first part focuses on high-dimensional causal inference without sparsity. We analyze cross-fitted estimators and derive the asymptotic distribution of the cross-fitted augmented inverse probability weighting (AIPW) estimator under a proportional asymptotics regime. Our results highlight how cross-fitting and regularization impact estimation risk, enabling more accurate inference even in dense, high-dimensional designs. The second part addresses predictive inference under distributional heterogeneity. Classical conformal methods assume identically distributed data, an assumption violated in many real-world applications. We propose conformal algorithms for multi-environment settings, offering valid prediction intervals under minimal assumptions. These methods apply to both regression and classification, support general loss functions, and can incorporate auxiliary information to reduce interval size without compromising coverage. Together, these results advance uncertainty quantification in modern, complex data environments.Statistic
Caste Census Data for a Just Republic
No full textIndia’s return to caste enumeration after a 94-year hiatus is not merely a statistical event but a watershed in the nation’s unfinished struggle for social justice. This paper interrogates the consequences of governing with century-old caste data, exposing how this “data desert” perpetuates the invisibility of OBCs, Denotified Tribes, Dalit sub-castes, and intersectional minorities. Drawing on the frameworks of data justice, intersectionality, and the political economy of enumeration, we analyze empirical evidence from the SECC 2011, Bihar’s 2023 caste survey, and state and central reports. The findings reveal systemic upper-caste overrepresentation, elite capture within OBC reservations, and the ongoing erasure of Denotified Tribes and Dalit women and trans persons. We argue that blanket quotas without granular sub-categorization have hollowed out the transformative promise of affirmative action. This is the empirical foundation—and the policy roadmap—to dismantle caste-based privilege and forge a truly just republic.Only by reckoning with its data deficit can India renew its constitutional contract and advance genuine social equity.Author's Origina
