VictorJuiz/thesis huggingface dataset:
Migrant Youth Disadvantages in China: How Teacher Biases Shape Student Outcomes
Keywords: Migrant Students
Teacher Perceptions
Stereotype Threat
Value-added Model
China
Public Schools
Stanford Graduate School of Education International Education Policy Analysis
Thesis

Over the past three decades, rural to urban migration in China has had unprecedented implications for millions of migrant children. Many of them have been excluded from urban public schools due to their Hukou (residence) status. Those fortunate enough to enter public schools often encounter stereotyping and subtle discrimination from peers and teachers, which may lead to lower academic outcomes. This paper uses a value-added model to analyze the correlation between teachers’ perceptions towards migrant students and their respective Chinese and math test performances in public middle school settings. With a nationally representative panel dataset, we find that rural migrant students are performing at least as well as their local peers on 8th grade midterm exams. However, having a teacher with negative biases against migrant students’ is harmful to their academic outcomes, while having a teacher with a preference for teaching local students over migrant students is not. We conclude by discussing the implications of our findings.
Mixed-signal processing for machine learning
Keywords: Electrical Engineering.

Recent advancements in machine learning algorithms, hardware, and datasets have led to the successful deployment of deep neural networks (DNNs) in various cloud-based services. Today, new applications are emerging where sensor bandwidth is higher than network bandwidth, and where network latency is not tolerable. By pushing DNN processing closer to the sensor, we can avoid throwing data away and improve the user experience. In the long term, it is foreseeable that such DNN processors will run on harvested energy, eliminating the cost and overhead of wired power connections and battery replacement. A significant challenge arises from the fact that DNNs are both memory and compute intensive, requiring millions of parameters and billions of arithmetic operations to perform a single inference. This thesis presents circuit and architecture techniques that leverage the noise tolerance and parallel structure of DNNs to bring inference systems closer to the energy-efficiency limits of CMOS technology. In the low signal to noise ratio (SNR) regime where DNNs operate, thermally-limited analog signal processing circuits are more energy-efficient than digital. However, the massive scale of DNNs favors circuits compatible with dense digital memory. Mixed-signal processing allows us to integrate analog efficiency with digital scalability, but close attention must be paid to energy consumed at the analog-digital interface and in memory access. Binarized neural networks minimize this overhead, and hence operate closer to the analog energy limit. This thesis describes a mixed-signal binary convolutional neural network (CNN) processor for embedded inference applications that achieves 3.8 uJ/classification at 86% accuracy on the CIFAR-10 image classification dataset. A weight-stationary, parallel-processing architecture amortizes memory access across many computations, leaving wide vector summation as the remaining energy bottleneck. This design features an energy-efficient switched-capacitor neuron that addresses this challenge, employing a 1024-bit thermometer-coded capacitive DAC section for summing point-wise products of CNN filter weights and activations and a 9-bit binary-weighted section for adding the filter bias. The design occupies 6 mm^2 in 28 nm CMOS, contains 328 KB of on-chip SRAM, operates at 237 frames/s (FPS), and consumes 0.9 mW from 0.6 V/0.8 V supplies. The corresponding energy per classification (3.8 uJ) amounts to a 40x improvement over the previous low energy benchmark on CIFAR-10, achieved by both architectural specialization and mixed-signal processing. The switched-capacitor neuron array is 12.9x more energy efficient than a synthesized digital implementation, which amounts to a 4x advantage in system-level energy per classification at the same classification accuracy. We provide an apples-to-apples comparison of the mixed-signal, hand-designed digital, and synthesized digital implementations of this architecture.
Modeling and interpreting molecular kinetics from simulation data
Keywords: Chemistry.

Atomistic simulations provide detailed information about a molecular system's dynamics at finer time and length scales than experiments can access. However, the modeling and interpretation of simulation datasets in an unbiased and statistically sound way require dedicated algorithms. One analysis method is the so-called variational approach to conformational dynamics, which introduces an objective framework for the ranking of models. One such class of models are Markov state models (MSMs), which separate the configuration space explored by a simulated molecule, such as a protein, into discrete, disjoint states between which the transitions can be modeled as Markovian. In Chapter 1, I summarize the essentials of MSM construction and the variational approach. In Chapters 2 and 3, I describe systematic studies in varying MSM parameters, in pursuit of general trends for variationally optimal models of proteins, with a focus on clustering into states. In Chapters 4 and 5, I present algorithmic advances in comparing multiple related models and in coarse-graining MSMs, also using clustering. In Chapter 6, I summarize the current state of the art in MSM methods and identify frontiers in their application and methods development. Finally, in Chapters 7 and 8, I apply clustering to different problems, such as in fluid dynamics. I discuss the possibility of extending the methods motivated here to a broader class of dynamical systems in Chapter 9. Overall, the methods presented in this work focus on interpretability and statistical robustness.
Modeling the optical response of monolayer molybenum disulfide in a cavity
Keywords: Physics.

As semiconductor-based systems face heat dissipation and signal distribution issues, the focus has shifted to on-chip nano-photonics as a promising replacement. Unlike the traditional architectures, photonic resonators such as optical cavities are controlled by electromagnetic radiation instead of electrical voltage. The key challenge lies in generating a nonlinear input-output relationship. To this end, one popular solution is to couple the resonator to a saturable absorber. Here, we will focus on the prospect of using monolayer molybdenum disulfide (MoS2) as the saturable absorber, with the material placed inside a Fabry-Perot cavity at low temperature. The strength of the interaction between the excitons of monolayer MoS2 and the electromagnetic field is calculated by deriving the electronic band structure of the material and superposing transition amplitudes between valence and conduction band states. Unlike a multi-atom cavity, the interaction between different excitons has a significant effect on the input-output relationship of the cavity. We will show the theoretical model required to calculate the exciton-photon coupling coefficient and the exciton-exciton annihilation rate, eventually culminating in the use of a Lindblad-Hamiltonian formalism to derive the optical nonlinearity for the cavity-material system.
Models of microbial dynamics : from genetic exchange to ecological interactions
Keywords: Physics.

How can physics approaches illuminate evolutionary and ecological dynamics? This thesis contains two physics-style toy models in these areas. Fine-scale diversity within traditionally defined species is observed to be maintained over long timespans. How can ecological interactions lead to such coexistence? Previous ap- proaches have required conditions less reasonable for closely-related strains. Chapter 1 introduces a model in which structured interactions allow such strains to coexist in a state of spatio-temporal chaos with broad distributions of abundances. The dynamics of many interacting strains are understood using methods from statistical physics. Genetic exchange in microbial populations can be rare enough that evolution is almost entirely asexual. What is the benefit of such rare genetic exchange? In Chapter 2, a minimal model is presented to understand how the speed of evolution depends quantitatively on the rate of genetic exchange. The rates needed for initial speed ups are found to decrease as a power of the population size and the evolution exhibits spontaneous oscillations.
A modern maximum likelihood theory for high-dimensional logistic regression
Keywords: Statistics.

Logistic regression is arguably the most widely used and studied non-linear model in statistics. Classical maximum-likelihood theory based statistical inference is ubiquitous in this context. This theory hinges on well-known fundamental results: (1) the maximum-likelihood-estimate (MLE) is asymptotically unbiased and normally distributed, (2) its variability can be quantified via the inverse Fisher information, and (3) the log-likelihood ratio (LLR) statistic is asymptotically a Chi-Squared. This thesis uncovers that in the common modern setting where the number of features and the sample size are both large and comparable, classical results are far from accurate. In fact, (1) the MLE is biased, (2) its variability is far greater than classical results, and (3) the LLR statistic is not distributed as a Chi-Square. Consequently, p-values obtained based on classical theory are completely invalid in such settings. This thesis provides a modern perspective on classical maximum likelihood theory in the context of logistic regression (developed jointly by the author and her collaborators). The contributions here are two-fold: first, it discovers a phase transition in the existence of the MLE and explicitly pins down the phase transition curve; second, in the regime where the MLE is finite, it characterizes the asymptotic behavior of the MLE and the LLR for a class of covariate distributions, under the aforementioned high-dimensional regime. Empirical evidence demonstrates that this asymptotic theory provides accurate inference in finite samples and is robust to certain violations of the underlying assumptions. Practical implementation of these results necessitates the estimation of a single scalar, the overall signal strength---a procedure for estimating this parameter is also discussed. This asymptotic theory can be extended to characterize distributions of penalized maximum likelihood estimators in some settings. Along the way, this thesis surveys relevant works in the field of high-dimensional inference, particularly those developing methodology for valid inference in high-dimensional regression problems.
Modern methods in extremal combinatorics
Keywords: Mathematics.

In this thesis, we apply modern probabilistic and algebraic techniques to different problems in extremal combinatorics. One of the most recent algebraic techniques is the new polynomial method which Croot, Lev and Pach introduced in 2016. This method has lead to the spectacular breakthrough of Ellenberg and Gijswijt on the cap-set problem, and has had many more applications in additive number theory and extremal combinatorics. In Chapter 2, we use various tools that resulted from the Croot-Lev-Pach polynomial method, combined with probabilistic and combinatorial arguments, to prove new upper bounds on the Erdos-Ginzburg-Ziv constant of F_p^n for a fixed prime p \geq 5 and large n. Chapter 3 also relies on developments arising from the Croot-Lev-Pach polynomial method as well as new combinatorial ideas. We prove a polynomial bound relating the parameters in Green's arithmetic k-cycle removal lemma in F_p^n for all k \geq 3. The special case of k = 3 was previously proved by Fox and Lovasz and is used as the base case of an induction on k in our proof for all k \geq 3. In Chapter 4, we use methods from algebraic geometry (and basic differential topology) to prove an asymptotically tight lower bound for the number of graphs of a certain form where the edges are defined algebraically by the signs of a finite list of polynomials. We present many applications of this result, in particular to counting intersection graphs and containment orders for various families of geometric objects (e.g. segments of disks in the plane). Using probabilistic methods, we prove the so-called Edge-statistics conjecture of Alon, Hefetz, Krivelevich and Tyomkyn in Chapter 5. In a certain range of the parameters, this conjecture already follows from a result of Kwan, Sudakov and Tran. We solve the other cases, and thereby establish the full conjecture. Finally, in Chapter 6 we prove a conjecture of Erdos, Faudree, Rousseau and Schelp from 1990 concerning subgraphs of minimum degree k.
Modern probabilistic models for human choices and rankings
Keywords: Management Science and Engineering.

As datasets capturing human choices and rankings grow in richness and scale, particularly in online domains, there is an increasing need for more flexible models of ranking and choice. I introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, which escapes traditional choice-theoretic axioms such as regularity and stochastic transitivity while remaining inferentially tractable. PCMC still satisfies the axiom of uniform expansion, a considerably weaker assumption than Luce's choice axiom, and PCMC retains the Multinomial Logit (MNL) as a special case. Using the notion of a choice representation to break a ranking into a sequence of choices, I develop a framework for turning models for discrete choice into models for ranking data, generalizing the idea used to build the seminal Plackett-Luce (PL) model from MNL. This framework allows us to leverage our progress in choice into novel models for ranking data and allows us to build flexible yet tractable models for complete and partial rankings from recent advances in choice models. I also discuss methods and subtleties of shrinkage estimation for choice data and demonstrate the efficacy of the models I propose in prediction tasks from a broad range of domains including transportation, competitions, voting, and sushi.
Modern statistical approaches for randomized experiments under interference
Keywords: Statistics.

This thesis presents new methodology for handling interference in randomized experiments. Interference, a phenomenon in which individuals interact with each other, is widely prevalent in the social and natural sciences, and has major implications for how experiments are optimally designed and analyzed. I first provide an introduction to interference, including examples and a relevant brief history of causal inference. Next, I demonstrate how researchers can use Stein's method to establish limiting distributional results for estimators under interference. The modern tools afforded by Stein's method allow one to analyze certain regimes of arbitrarily dense interference, which goes beyond the analysis capabilities of existing tools. In the subsequent chapter, I develop new model-based, adjustment estimators for estimating the global average treatment effect. The adjustment variables can be constructed from functions of the treatment assignment vector, and the researcher can use a collection of any functions correlated with the response, turning the problem of detecting interference into a feature engineering problem. The final chapter proposes new methods for designing and analyzing stochastic seeding strategies, which are an appealing way of leveraging network structure for marketing, public health, and behavioral interventions. New importance sampling estimators adapted to this setting can greatly improve precision over existing approaches. This thesis is interdisciplinary in nature. Stein's method (Chapter 2), regression adjustments (Chapter 3), and importance sampling (Chapter 4) all command spheres of influence in certain sectors of the literature, and are here repurposed in new domains. I hope that my work shows how existing statistical technology can arise in new arenas of application while simultaneously giving rise to new methodological questions and problems, and in this way, I hope my work is useful for both practitioners and methodologists.
Moduli spaces of bundles via motivic probabilities
Keywords: Mathematics.

We give formulas for the classes in the completed Grothendieck ring of varieties of the moduli stacks of principal bundles for the special linear or symplectic groups over a curve X in terms of the motivic zeta function of X. The answers agree with predictions of Behrend and Dhillon. The computations rely on interpreting certain classes in the Grothendieck ring in a probabilistic way, using work of Margaret Bilu and Sean Howe.
Molecular counting analysis of infectious disease
Keywords: Bioengineering.

Infectious diseases have a profound impact on humankind, influencing the course of wars and the fate of nations. The case for applying next-generation sequencing (NGS) in clinical microbiology is becoming increasingly clear. A flood of recent studies have shown how this powerful technology can address pressing modern problems in infectious diseases, including resistance, rare infections, and outbreaks. In this thesis, we show that molecular counting of pathogen-derived cell-free DNA is a new diagnostic paradigm for infectious disease. We built a pipeline for counting pathogen-derived cell-free molecules in human plasma and a web appli- cation for browsing the resulting data. We applied these tools to thousands of clin- ical samples collected from hundreds of patients at Stanford hospital. We further processed thousands of clinical test records in order to show that this method can be broadly applied for non-invasive monitoring of viral, bacterial, and fungal infec- tions in deep tissues. Finally, we show that unbiased pathogen monitoring using this technique can track infections that escape hypothesis-centric clinical testing. As well as demonstrating this new diagnostic application of NGS, we show how NGS technology can be used to understand infectious disease mechanism. We de- veloped a pipeline for counting of sequencing reads derived from RNA-protein in- teractions in vivo. We show that this method (CLIP-seq) can be applied to viruses that have infected human cells and use it to reveal novel interactions between the Hepatitis-C virus (HCV) genome and human protein PCBP2. We also applied this method to HERV-K, an endogenous retrovirus. We showed that human embryo development occurs in the presence of retroviral products, which may protect the embryo from exogenous infection and help regulate early development. We highlight three separate ways to validate results from mechanistic CLIP-seq experiments, including comparative analysis, replicate matching, and functional studies. We also developed a multiplexed RNA-protein interaction assay that is compatible with the scale of CLIP-seq experiments and exceeds the throughput of common biochemical assays. We applied this technology to a model RNA-protein interaction (histone stem-loop and stem loop binding protein), recapitulating two decades of biochemistry and revealing novel features of the interaction. In summary, we built computational tools that apply molecular counting to in- fectious disease diagnostics and mechanism. For validation of these results, we developed a novel microfluidic tool for high-throughput biophysical measurements.
Molecular principles to engineer plant microbiomes
Keywords: Bioengineering.

The microbes that live in and on plants (the plant microbiome) are critical for plant health, and exert their influence by facilitating nutrient acquisition, regulating plant hormone levels, and helping to withstand pathogen attack. Due to these potential benefits, the plant microbiome has become an appealing target for engineering sustainable improvements in crop productivity. However, the lack of a mechanistic understanding for plant-microbiome interactions at the molecular level has impeded engineering efforts. In this dissertation, I address the role of plant-derived specialized molecules in shaping the composition of the root microbiome, and that of microbe-produced molecules in influencing plant health. Using broadly-applicable experimental platforms that allow for hypothesis-driven mechanistic studies, along with previously developed reverse genetic tools in the model plant Arabidopsis, I discovered a novel mechanism of action for coumarins (root-exuded, specialized small molecules) in assembling the microbiome of plant roots. Additionally, I describe a role for Pseudomanas-derived pyrroloquinoline quinone (a redox-active small molecule) in improving plant growth under iron-limiting conditions -- a nutrient deficiency prevalent in one-third of the world's soils. Finally, I highlight a serendipitous discovery made while engineering plants to modulate the root microbiome using opines -- unusual conjugates of two common plant metabolites that can be catabolized by select microbes. The microbes inciting the production of opines, Agrobacterium species, hijack the plant cell's splicing machinery as part of its pathogenic lifestyle. This novel finding could be exploited to improve transgene expression in plants, and to survey bacterial genomes for genes that are horizontally transferred across the kingdoms of life. These contributions, which provide mechanistic roles for both plant and microbe-derived small molecules within the phytobiome, offer concrete strategies for engineering phytobiomes to improve agricultural systems.
Molecularly confined polymer nanocomposites
Keywords: Chemistry.

Molecular polymer confinement can be achieved in hybrid nanocomposites where individual polymer molecules are confined by a nanoporous matrix to dimensions comparable to or less than the molecular size of the polymer. Such molecular confinement can induce changes in a wide variety of properties of the confined polymer and the nanocomposite. In this dissertation, I focus on the mobility of the molecularly confined polymers as well as the thermal and fracture properties of the nanocomposite. In my first project, I show that the polymer mobility and the nanocomposite thermal and mechanical properties can be significantly influenced by the interaction of the molecularly confined polymer with the pore surface, which can be manipulated by surface chemical functionalization of the pores. Polyimide nanocomposites hold great promise for applications demanding fracture resistance at high temperature. In my second project, I discuss the strategy of improving fracture resistance of polyimide nanocomposites by increasing polymer fill level and polymer-surface interaction.