Agriculture in a Changing Climate: Applications of Machine Learning and Remote Sensing for Measurement and Adaptation

This work considers how large-scale datasets and novel machine learning methods can be applied to challenges in climate and sustainability, with a particular focus on agriculture. Effectively leveraging these advancements for sustainable development research requires answering two questions: first, how can complex data be translated into useful and accurate information? And second, under what circumstances does this information offer real insight into an important problem? In answer to the second of these questions, the research in the three chapters of this dissertation falls broadly into one of two categories: problems for which high spatial- or temporal-resolution data is necessary but infeasible to collect at scale (Chapters 1 and 3); and problems for which the structure of relationships between features and outcomes is complex, with important non-linearities, interactions, or other nuances that may be overlooked by traditional approaches (Chapters 1 and 2). Both such categories of problem are common in the domain of agriculture, an industry which is critical for food security and economic well-being, but highly susceptible to fluctuations in weather and climate. In Chapter 1, I introduce and validate a method for creating high-resolution estimates of planting and harvest dates for United States crops with satellite imagery. This data is an important input for many research applications, but is only tracked at the state level. The resulting dataset is then used to generate more accurate measures of the weather conditions crops are exposed to during their growing season, and thus more precise estimates of how these conditions impact yields. These estimates suggest a 17% larger impact of extreme heat (>29C) on crop yields than previously documented, with substantial variation in heat sensitivity over the course of the growing season. However, the overall impact of increased temperatures is partially offset by a reduced estimate of growing season duration and a 276% increase in the estimated benefits of warm (10-29C) temperatures. Finally, I present novel evidence that farmers use early planting as a form of adaptation to warming, with planting dates shifting earlier by 0.13 days for each additional 30C day during the growing season. Chapter 2 presents an even more flexible formulation for estimating US crop yields. I introduce a deep learning model that predicts yields directly from daily weather data, and show that it reduces out-of-sample error by 10.7% relative to standard linear modeling approaches. Using interpretable machine learning techniques, I demonstrate that this model learns a number of nuanced patterns consistent with expectations from agronomic theory, including spatial and geographic variation, interactions between weather features, and nonlinearity over weather feature values. Over several simulations, these models estimate future impacts of warming that are two to three times less severe than prior modeling approaches would suggest. However, the complexities of causal identification with highly flexible models mean that these results must be interpreted with caution; primarily, they suggest that estimates of climate impacts may be highly sensitive to feature selection, and to precise trends in warming over the course of the growing season. Finally, Chapter 3 turns to smallholder farms in Kenya, as part of research done with support from Atlas AI. A collection of approaches for real-time yield monitoring at the field level are introduced and tested, using satellite-based assessment of vegetation health. I discuss a remotely-sensed proxy for crop yields for use in environments where reliable ground truth data is unavailable, and present a model that can capture 73.5% of variation in this yield proxy by roughly 6 weeks post-planting. A range of approaches are evaluated for incorporating location- and crop-specific features, handling low volumes of training data, and adjusting for variable timing of satellite imagery collection. Taken together, these chapters demonstrate the value of remote sensing and machine learning for understanding the impacts of climate on crops and identifying strategies for adaptation. They also emphasize the complementarity between novel machine learning approaches and traditional statistical and economic methods: in Chapter 1, for example, satellite imagery is used to generate a novel dataset for analysis with more standard models; and in Chapter 2, I present a non-parametric approach to feature discovery for future causal inference work. Finally, these chapters demonstrate that estimates of climate impacts can be highly sensitive to what features are used and how they are encoded; this underscores the importance of careful consideration in constructing accurate feature inputs, and caution in interpreting the results of any one model

Optimizing Interdomain Routing for Today's and Tomorrow's Services

Large cloud and content (service) providers serve applications that are responsible for the vast majority of Internet traffic today. However, service providers have to contend with decades-old Internet protocols to do so and, in particular, to route latency sensitive user traffic over the public Internet to service provider networks. This reliance creates urgent problems as businesses/people/governments increasingly rely on the Internet for critical activities, and as new applications such as VR introduce increasingly strict network performance requirements. This dissertation explores the extent to which current ways service providers use the Internet's old protocols are sufficient to meet demands of today's and tomorrows applications. It then proposes using these old Internet protocols in new ways to reliably route user traffic over an unreliable public Internet by solving challenging optimization problems using new Internet measurement and modeling techniques. The systems described in this dissertation can help service providers work with existing infrastructure to deliver the reliable, performant service our increasingly connected society needs

Defamiliarizing the Voice: Approaches to Vocal Composition in the Music of Anna Korsun, Charmaine Lee, and Anna-Louise Walton

The voice is a unique instrument which is integrated into the body and culturally trained from birth. Due to its distinctive identity, many composers have the instinct to obfuscate its humanness in search of an instrument more malleable to their creative pursuits. I present Viktor Shklovsky’s concept of defamiliarization, "to increase the difficulty and length of perception,” as a framework for this approach to the voice. I posit that making the voice unfamiliar or strange provokes a heightened state of listening. By contemplating whether a sound’s source is human or not, the listener perceives the voice anew. I look first to Anna Korsun’s piece Ulenflucht for twenty singing and playing performers, which integrates primal vocal techniques with animal calls and bird whistles to create an imagined environment in which the voice is one of many creatures. I then turn to the vocal improvisations of Charmaine Lee, whose intimate integration of her voice with technology blurs the boundaries between human and machine. Finally, I relate the music of Korsun and Lee to one of my pieces, the deep glens where they lived for vocal sextet, which mediates the voice with PVC pipes and close microphone techniques. This music explores the rich and fragile nature of the human voice, and in so doing reframes, reimagines, and redefines it

America's Campus: Spiritual Psychology in History and in The Present

America’s college campuses have contributed directly to two contemporary international trends: secularization and the “spiritual rather than religious” movement. In history and in the present, one channel through which spirituality and religion has been forged in America is through its tenuous relationship with conscious-altering substances, including alcohol: its consumption and its regulation. This relationship has historically been the ‘battleground’ for the ‘fight’ that has precipitated evolution in the practices and sociology of religion and spirituality. The current project explores pertinent history and practice of religion and spirituality on the campus of America, its colleges and universities. This research and its dissemination is directed towards individual and institutional policy-makers and decision-makers, who, equipped with this knowledge, can impact the human experience for the better: one decision, one policy or one person at a time, ideally at an accelerated rate — towards the purpose and ongoing realization of a more happy and healthy world

Managing Marginality: Jails, Health, and Inequality

Jails play a unique role in the criminal legal system, incarcerating people who are awaiting trial or serving short sentences of less than a year. At midyear 2023, jails incarcerated 664,200 people and admitted 7.6 million people in the preceding 12 months (Zeng 2024). People incarcerated in jail often face several co-occurring hardships, including housing instability, untreated mental illness, and substance use problems, which jails can exacerbate. This dissertation argues jails create and respond to many of these problems associated with poverty, especially problems related to the health of incarcerated people. Across three papers, I demonstrate jails (1) were used as a punitive response to the prescription opioid crisis, especially in rural communities; (2) became a highly infectious environment in New York City during the COVID-19 pandemic after failing to enforce many basic preventative measures like masking and social distancing, threatening the health of incarcerated people; and (3) readmit people with mental illness and substance use problems at much higher rates than people in good health. Taken together, these papers demonstrate the complex relationship between jails on the one hand and the health of incarcerated people and the public on the other

Other Selves: Critical Self-Portraiture in Cuba during the “Special Period in the Time of Peace,” 1991-1999

The path of Cuba’s cultural economy and patrimony deviated substantially during the “Special Period in the Time of Peace” (1991-1999), including the collapse of state sponsorship for the arts and the opening of the Cuban economy to foreign investment. This opening was slight but significant. Artists found themselves in a position where their work no longer solely existed as patrimony of the state but as personal methods of success and survival. My dissertation analyzes how three Black Cuban artists, René Peña, Belkis Ayón, and Elio Rodríguez, engineer and manipulate self-portraiture as a critical tool through which they can explore issues of belonging and place in connection to the Cuban national project. I attest that each artist positions representations of themselves, or their avatars, within their work to examine what it means to be Cuban, Black, and human. I begin my project by establishing how the figure of the White, hyper-masculine man has served as the ideal Cuban citizen following the revolution and independence. Cuban artists have explored themes of national identity and belonging since the mid-nineteenth century, in many instances reflecting on race and the presence of African descendants in Cuban society. The continued discourse on “racelessness” and the supposed eradication of racism in the country made the potential to be both Black and Cuban impossible. Official discourses on race after the 1959 revolution attempted to erase, and in many senses, whitewash, the historical legacy of racism in Cuba through the expressly public abolishment of discrimination and difference in Cuban society. An attempt to erase all forms of difference, or the visibility of difference, within Cuban society accompanied advances in equal opportunity to jobs, education, and housing for the Black Cuban community after the revolution. My project focuses on how Peña, Ayón, and Rodriguez contest the long-established hierarchy of race and gender in official cubanía [Cubanness] through visual discourses. I argue that the works of Peña, Ayón, and Rodríguez are not examples of a hybrid, creolized synthesis but instead working products of investigation and play. Considering identity as a process and project always in flux, I contend that these three artists use aesthetic strategies to represent Cubanness and Blackness as not mutually exclusive but simultaneously iterative and dynamic. Considering their artistic practices as performances of Blackness and self, I present these artists as critical interlocutors of the cultural moment. I argue that Peña, Rodríguez, and Ayón mobilize the Afro-diasporic conception of the self as external and multiple through their avatars as a form of self-fashioning. An avatar functions as a proxy for a person, acting as an extension of their self, traversing locations and discourses otherwise inaccessible to the primary self. Avatars blur the boundaries between the material and the virtual world and muddle the distinctions between subject and object, flesh and body. Peña, Rodríguez, and Ayón create portraits of their “other selves” to assert their subjectivity and personhood in realms that otherwise negate their presence. Through a close visual analysis of the work created by Peña, Ayón, and Rodríguez, I show how their use of alter-egos elucidates their experiences of the materiality of Blackness and the multiplicity of being. I argue that this is mainly present in the material processes inherent in the print-making and performative productions included in each. For example, in terms of color, Peña and Ayón use black and white critically, manipulating the various gray scales between the two tones to illustrate the many potentialities of cubanía. Rodríguez has interestingly moved into soft sculptural forms of blacks and whites, but the works discussed here use fixed colors to create a humorous play with traditional Cuban aesthetics. Each artist uses color differently, but through their processes, they imbue their works with a sense of materiality and personhood that is only possible through print. For these artists, the work’s creation becomes a performance of self-definition that parallels the many ways we perform race, nationhood, and belonging

Exploring Quantum Many-Body Physics with Computational Methods

This thesis presents an investigation into quantum many-body systems using both theoretical and innovative computational techniques. It has two parts: an investigation of a new class of materials using established methods, and the development of a new set of methods. First, we use Hartree-Fock calculation to study the moiré Hubbard model that represents the low energy physics of twisted WSe₂ and related materials. In these materials, interaction strength, carrier concentration, and band structure can be controlled by the twist angle and gate voltage. A notable feature is the tunable displacement field, i.e., the gate voltage difference between two layers, leading to a highly tunable van Hove singularity. We calculate the magnetic and metal-insulator phase diagrams and find a reentrant metal-insulator transition controlled by the displacement field. Experimental results for devices with twist angle ∼ 4-5° indicate a similar reentrance, placing these devices in the intermediate coupling regime. Building on this, the next chapter employs dynamical mean field theory (DMFT) to study the moiré Hubbard model, extending our analysis to include temperature-dependent transport behaviors and phase transitions. We observe that the cube-root van Hove singularity () ∼ ||⁻¹/³ contributes to strange metal behavior, characterized by a linear temperature-dependent scattering rate and / scaling. We compare the results to the experimental findings in twisted homobilayer WSe₂ and heterobilayer MoTe₂ /WSe₂. We find that in twisted WSe₂, the continuous metal-insulator transition is driven by a magnetic transition associated with a change of the displacement field that brings the high order van Hove point of degree three to the Fermi level. The proximity to this van Hove point also induces a linear resistivity. In MoTe₂/WSe₂, one has a paramagnetic metal to paramagnetic Mott insulator transition driven by variation of the bandwidth, with the displacement field effects being unimportant. In the third study we use the example of magic angle twisted bilayer graphene (TBG) to study the interplay between correlation and band topology. We construct the Wannier basis for TBG involving two triangular site-centered Wannier functions per unit cell derived from the two flat bands per spin per valley. The two crucial point symmetries ₂ and ₃ act locally on the Wannier functions. The Wannier functions have a power-law tail indicative of topological obstruction, but are mostly localized with most charge density concentrated within a single unit cell. This localization significantly enhances the on-site Coulomb interactions relative to interactions with further neighbors, allowing for more accurate estimation of Hamiltonian parameters using a limited set of Wannier functions. Using DMFT, we show that a mixed position/ momentum space representation can be employed, in which the kinetic energy is expressed in the momentum space basis of non-interacting eigenstates, so that all the topological features are exact and well preserved, while the interaction part may be expressed in position space and inherit convenient locality and symmetry properties from the Wannier functions. Finally, we introduce a novel, data-driven approach to compress the two-particle vertex function. Using PCA and an autoencoder neural network, we achieve significant reductions in complexity while maintaining high fidelity in representing the underlying physics. We demonstrate that a linear PCA not only provides deeper physical insights but also exhibits superior zero-shot generalization compared to more complex nonlinear models. Further, we explore the relationships between different quantum states by identifying principal component subspaces common across known phases. Our analysis reveals that while the vertex functions necessary for describing ferromagnetic states differ significantly from those describing the Fermi liquid state, those required for antiferromagnetic and superconducting states share a common foundation, hinting at their emergence from pre-existing fluctuations in the Fermi liquid state

Data: Can Arctic Sea Ice Melting Lead to More Summertime Heat Extremes?

The dataset includes the key variables that were used to generate the figures in "Can Arctic Sea Ice Melting Lead to More Summertime Heat Extremes?" submitted. We quantify the impact of late 21st century Arctic sea ice loss on Northern Hemisphere (NH) summertime heat extremes using model simulations that are forced by the future Arctic sea ice loss. First, we find an overall increase of heat extreme frequency in the NH continents in our model simulations, but only in the presence of ocean-atmosphere coupling. The increased frequency of heat extremes is mostly due to mean temperature increase. However, in comparison to future warming scenario, in general, increases in heat extremes in NH continents due to the future Arctic sea ice loss are relatively small. The results suggest a non-negligible but limited role of the future Arctic sea ice loss on contributing to the NH summertime heat extremes. More on variables: The dataset includes variables such as the Arctic sea ice fraction, boreal summer (June-July-August) mean near-surface temperature, and ratio of heat extreme frequency that are calculated from the CESM1-WACCM4 constrained sea ice model simulations

Role of preconception nutrition supplements in maternal anemia and intrauterine growth: a systematic review and meta-analysis of randomized controlled trials

Background Impaired intrauterine growth, a significant global health problem, contributes to a higher burden of infant morbidity and mortality, mainly in resource-poor settings. Maternal anemia and undernutrition, two important causes of impaired intrauterine growth, are prioritized by global nutrition targets of 2030. We synthesized the evidence on the role of preconception nutrition supplements in reducing maternal anemia and improving intrauterine growth. Methods We undertook a review of the randomized controlled trials (RCTs) assessing the effect of preconception nutrition supplements on maternal hemoglobin, an indicator to estimate maternal anemia, and markers of intrauterine growth including birth weight, length, head circumference, and small for gestational age. Additionally, we examined preterm birth as an important perinatal outcome. We searched PubMed, CINAHL, Web of Science, Cochrane Central, and Embase. We computed summary mean differences and risk ratios (RR) with 95% confidence intervals (CIs) using random-effect models. We employed I2 and Cochran’s Q test statistics to assess heterogeneity. We used a revised Cochrane risk-of-bias (RoB version 2.0) and GRADE (grading of recommendations, assessment, development, and evaluation) tools to assess the risk of bias and quality of evidence of eligible RCTs, respectively. Results We identified 20 eligible RCTs (n = 27,659 women). Preconception nutrition supplements (iron and folic acid, multiple micronutrients, and a lipid-based nutrient supplement) overall increased maternal hemoglobin by 0.30 g/dL ((0.03, 0.57); I2 = 79%; n=9). However, we did not find a significant effect of the supplements on birth weight (12.25 gm ((− 22.66, 47.16); I2 = 55%; n=10)), length (0.15 cm (− 0.26, 0.56); I2 = 68%; n = 5), head circumference (− 0.23 cm (− 0.88, 0.43); I2 = 84%; n=4), small for gestational age (RR 0.91 (0.80, 1.04); I2 = 31%; n=8), or preterm birth (RR 0.93 (0.69,1.25); I2 = 57%; n=12). In general, the quality of evidence was assessed as very low to moderate. Conclusion Preconception nutrition supplements studied to date appear to reduce maternal anemia. However, it is uncertain whether there are beneficial effects of the supplements on intrauterine growth. Low quality of evidence warrants future well-designed RCTs to produce solid scientific data, particularly of a more comprehensive package of preconception nutrition supplements that include both macro- and micronutrients. Systematic review registration PROSPERO CRD42023464966

Contributions of the superior colliculus to covert decision termination

Many decisions require us to actively interrogate the world using our senses. Based on what we perceive, we may commit to an immediate course of action or plan for future actions. The neuroscience of perceptual decision-making examines how the brain gathers sensory information and uses it to guide behavior. A widely accepted model represents this decision process as the gradual accumulation of sensory evidence until a threshold or criterion is reached. For decisions about the direction of random-dot motion (RDM) stimuli, extensive research has described the mechanisms of evidence accumulation in association areas such as the lateral intraparietal area (LIP). Recent studies have also made progress in identifying the biological basis of the decision threshold. For overt decisions marked by an immediate saccadic response, neurons in the superior colliculus (SC) are thought to set this threshold by initiating eye movements when LIP activity is sufficiently elevated. Additionally, reversible inactivation of SC neurons has been shown to prolong evidence accumulation in LIP, suggesting that SC neurons play a causal role in terminating the decision process. However, it remains unknown whether SC neurons also contribute to covert decision termination, where commitment results in a planned, rather than an immediate, eye movement. To address this gap, I recorded neural activity from two rhesus macaques as they performed a RDM discrimination task, where I varied the length of the stimulus and delay periods from trial to trial. This design required the animals to make a covert commitment to a choice on each trial, which they later reported with a saccade following the delay period. Using high channel-count electrodes, I recorded simultaneously from large populations of neurons in SC, LIP, and the dorsal pulvinar of the thalamus (dPul) unilaterally. In a majority of trials, SC neurons exhibited non-saccadic bursts — bursts of activity not associated with eye movements or specific trial events. The timing of these non-saccadic bursts suggested they might signal the moment of decision termination. Testing this hypothesis, I found that non-saccadic bursts effectively divided each trial into an early deliberation phase, where sensory evidence informed the decision, and a later commitment phase, where further sensory evidence was ignored. Additionally, the timing of non-saccadic bursts in the SC corresponded with the end of evidence accumulation in LIP. Finally, I identified a population of neurons in dPul that may relay this termination signal from SC to LIP. This study advances our understanding of perceptual decision-making by broadening the function of the SC in decision termination. Beyond its established role in terminating overt decisions with an immediate saccadic report, these findings suggest that the SC also contributes to decision termination following covert commitments, where the saccade occurs after a delay. The results have important implications for systems neuroscience by offering a clear example of serial computations occurring across dedicated cortical and subcortical areas to guide flexible behavior. While the path from sensation to deliberation to commitment is far from fully understood, these results lay the groundwork for future research — both to explore the role of additional nodes in the decision-making network and to investigate the local circuits that give rise to distinct functions within each node. In Chapter 1, I provide historical context on the neurobiology of perceptual decision-making, introduce the bounded evidence accumulation model, and discuss its application to both overt and covert decisions. I also motivate the present study by introducing the superior colliculus and its known role in the decision-making network. In Chapter 2, I investigate physiological markers of decision commitment in the SC. I describe non-saccadic bursts, which are physiological events that occur on single trials, and demonstrate how these events relate to covert decision termination. In Chapter 3, I examine how non-saccadic bursts in the SC might be involved in terminating the process of evidence accumulation in LIP and show how neurons in dPul might mediate this effect. Chapter 4 presents closing considerations and outlines directions for future research