VictorJuiz/thesis huggingface dataset:
Expanding the capabilities of biosensors with novel molecular switches

Biosensors capable of tracking the body's molecular state have tremendous potential to improve lives by making healthcare more accessible, personalized, and preventative. At the same time, developing biosensors that are both compatible with point-of-care or continuous monitoring and can accurately track a wide range of clinically important biomarkers poses a tremendous technical challenge. These sensors must operate without the sample processing employed by conventional laboratory diagnostic assays and must measure targets rapidly with molecular sensitivity and specificity within complex biofluid samples. These demands may be met by biosensors based on "molecular switches" -- engineered receptors that bind molecular targets with high specificity and undergo a binding-induced conformational change that produces measurable optical or electronic signals. However, to date these molecular switch biosensors have remained limited to sensing only a handful of molecules. It remains an outstanding challenge to develop generalizable strategies to methods for engineering molecular switches to sense the complete range of health biomarkers. In this dissertation, I begin by considering how we can develop sensors that are compatible with real world, day-to-day use. I analyze the diagnostic value of biosensing in dermal interstitial fluid, a biofluid with emerging interest for use with minimally invasive and continuous wearable sensors. The remainder of the dissertation focuses on three projects where I expand the generalizability molecular switches, enabling more rapid development of biosensors for new biomarker targets. In the first project, I introduce an approach for engineering off-the-shelf antibodies into antibody-based molecular switches that achieve continuous optical biosensing by augmenting them with a rationally designed DNA-linked competitor molecule. In the second project, I develop a universal protein-based competitor molecule that provides an even more universal method for our antibody-based molecular switch design to be applied to sensing new targets. In the third project, I develop a rational design approach for engineering DNA-based molecular switches that have pH-dependent properties which can be tailored to improve biosensor performance, and for potential drug delivery applications. Taken together, these advances in molecular switch design offer a toolkit of generalizable strategies that can be used to develop next-generation biosensors which sense a wide range of biomarkers for addressing a wide range of clinical needs

Keywords: Engineering.
Electrical Engineering.
Advisor: Soh H. Tom
Huang Possu
Wang Shan X.
Expanding the Horizons of Synthesizable Materials: A Study of Various Machine Learning Techniques on Materials Synthesizability Prediction

This study aims to provide insights into better ways of applying machine learning (ML) to predict and discover new materials. Specifically, this research focuses on comparing the performance of different ML techniques that are trained with data from the Materials Project Database and the Inorganic Crystal Structure Database to identify effective ML techniques that can perform materials synthesizability prediction. Machine learning (ML) is a subfield of artificial intelligence that allows computers to learn and improve from data without being explicitly programmed. By using ML, researchers can analyze and interpret large amounts of data related to materials' properties, structure, and performance. This can lead to the discovery of new materials with desirable properties, as well as the optimization of materials synthesis and processing. In this study of synthesizability, we report results on a number of different models for predicting synthesizability under different data imbalance scenarios. By defining the 100% precision-recall curve area be the best performance a perfect ideal model can achieve, our best performance model is a Multi-Layer Perceptron (MLP) model that exhibits a hold-out set precision-recall curve area of 99% when the dataset is balanced. Our results also show that the MLP model is the most resistant to the influence of data imbalance with a hold-out set precision-recall curve area of 98% when the imbalanced dataset has an unlabeled-to-labeled ratio of 4:1 and a hold-out set precision-recall curve area of 90% when the imbalanced dataset has an unlabeled-to-labeled ratio of 16:1.

Keywords: Program in Engineering Physics
Machine learning
computational material science
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Thesis
Advisor: Sendek Austin
Dionne Jennifer
Expansion microscopy and other tools for investigating the cell-material interface

Surface topography on the scale of tens of nanometers to several micrometers substantially affects cell adhesion, migration, and differentiation. Recent studies using electron microscopy and super-resolution microscopy provide insight into how cells interact with surface nanotopography; however, the complex sample preparation and expensive imaging equipment required for these methods makes them not easily accessible. Expansion microscopy (ExM) is an affordable approach to image beyond the diffraction limit, but ExM cannot be readily applied to image the cell-material interface as most materials do not expand. Here, we develop a protocol that allows the use of ExM to resolve the cell-material interface with high resolution. We apply the technique to image the interface between U2OS cells and nanostructured substrates as well as the interface between primary osteoblasts with titanium dental implants. The high spatial resolution enabled by ExM reveals that although two proteins, such as AP2 and F-actin, both accumulate at curved membranes induced by vertical nanostructures, they are spatially segregated. We also employ upconversion nanoparticles as a better fluorophore to visualize proteins of interest in ExM. We believe that our protocol will enable the use of ExM as a powerful tool for cell-material interface studies. Additionally, we explore extracellular matrix fiber synthesis and proteomics as further tools to investigate the cell-material interface.

Keywords: Humanities and Sciences.
Chemistry.
Advisor: Cui Bianxiao
Bertozzi Carolyn R.
Solomon Edward I.
Experimental methods in economics : three applications

This dissertation comprises three papers employing a diverse set of experimental methods to examine behavioral aspects of individual decision-making across distinct domains: charitable giving, misinformation on social media, and considerations surrounding others' autonomy of choice. Chapter 1 presents two experiments conducted on PayPal's Give at Checkout microgiving feature to learn about the effect of 1) information about charity outcomes on donations, and 2) exposure to these point-of-sale micro-giving requests on subsequent giving. Chapter 2 evaluates three distinct versions of a low-cost and scalable five-day text message educational course on drivers of misinformation sharing by conducting a field experiment with approximately 9,000 participants. Chapter 3 experimentally investigates the objective function of third parties who can impose binding minimum contribution requirements on a group of ten players playing a linear Public Goods Game.

Keywords: Humanities and Sciences.
Economics.
Advisor: Athey Susan
Bernheim B. Douglas
Wager Stefan
Exploiting contact dynamics for grasping and anchoring in the field

This thesis is about how to equip robotic platforms with the capability to forcefully interact with their environments. These forceful interactions often entail designing grippers or end-effectors that can grasp or anchor to different environmental features. When designing these grippers, the general design approach has four common considerations. The first is the contact dynamics, where the gripper interacts with the surface. This includes both the contact mechanics at the surface and the dynamics caused by the interaction. The second is how to achieve the desired surface conformity between the gripper and the often irregular contact surface. The third is the incorporation of load sharing elements to uniformly distribute the interaction force across the contact area, especially in the case where astrictive contact dynamics are used. Last, we consider how the integrated system should respond to the dynamic interaction to achieve the desired system response. In this thesis, we present three different mobile robots intended for operations in the field with forceful interaction. Although they are different platforms, the design approach of their grippers is consistent and can be applied to many other different robotic platforms and field scenarios. The first of these robot platforms is Astrobee, a free-flying robot inside of the International Space Station (ISS). A gripper was designed for Astrobee and allowed it to grasp and perch onto flat surfaces inside of the ISS with gecko-inspired adhesives. The second gripper enabled a quadrotor to capture a flying target. We define a velocity sufficiency region of acceptable initial conditions immediately prior to contact and show that the incorporation of passive mechanisms greatly expands the region. Last, a new climbing robot concept, ReachBot, is designed for interaction with rocky caves. ReachBot is a robot that consists of a small body with long limbs created by extendable booms. We present the system design considerations, a planar prototype, and tests conducted with a partial (single arm) 3D system prototype with a microspine gripper in the Mojave Desert. In these examples, the dynamic interaction results in limits in the force space that must be satisfied to achieve successful grasping. Then, along with the incorporation of passive mechanisms, one can create a sufficiency region of possible initial conditions immediately prior to the contact, with a probability of grasp success. This approach to modeling and passive mechanism design can simplify the control of the robot and inform future design iterations and modifications to meet the requirements of other robotic platforms.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Cutkosky Mark
Okamura Allison
Pavone Marco
Exploring Moiré Transition Metal Dichalcogenide Heterostructures

In recent years moiré transition metal dichalogenide (TMD) heterostructures have been studied extensively, realizing a tunable platform to study electronic correlations. In this thesis, we describe efforts to develop new moiré TMD device architectures to study and tune interacting ground states and characterize them using electrical transport measurements. We focus in particular on a novel geometry, using bilayer graphene as a sensing layer to measure the chemical potential of a WS2/WSe2 heterobilayer. We observe interaction-driven gaps in the TMD bilayer in agreement with previous work upon both electron and hole doping. We also discuss preliminary attempts to tune the TMD superlattice potential and Coulomb screening in situ, though these remain inconclusive so far. Our work highlights some of the experimental challenges involved in fabricating moiré TMD devices, and we end with an outlook on improving future devices.

Keywords: Physics
Transition metal dichalcogenide
Moiré heterostructure
Heterobilayer
WS2/WSe2
Superlattices
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Thesis
Advisor: Feldman Benjamin
Goldhaber-Gordon David
Exploring the introduction of real-life usages of Japanese honorifics in foreign language study

The current dissertation investigates factors that influence usages of Japanese honorifics and explores how instructors can introduce honorifics more effectively to second-language learners. Apart from existing literature, my data includes a corpus of real-life usages of Modern Japanese, classroom observations, interviews with Japanese language instructors, and a workshop conducted as an implementation of new pedagogy. I first examine the definition of honorifics and relevant concepts such as expressions of personal treatment and speech levels. Using diverse usages in real life, I examine multiple layers of meanings associated with honorifics and propose factors that influence usages of honorifics. My analysis suggests that usages of honorifics do not always match their labels, and that honorifics may be used for various purposes. In the latter half of this dissertation, I lay out existing pedagogy and discuss challenges in teaching Japanese honorifics. In addition, how mainstream textbooks introduce honorifics is also reviewed. After qualitatively discussing first-hand data, I make suggestions for introducing Japanese honorifics to learners of different proficiency levels. Bridging latest research on honorifics to teaching practices in second-language classrooms, my main teaching goal is that students will be able to use honorifics to express themselves instead of blindly following normative rules introduced in textbooks.

Keywords: Humanities and Sciences.
East Asian Languages and Cultures.
Advisor: Matsumoto Yoshiko
Dasher Richard
Sun Chao
Exploring the many scales of neural activity underlying perception and decision making

Experimental technologies that probe neural circuits from single neuron to brain-wide scales have allowed us to deepen our understanding of neural phenomena ranging from perception to decision making. For example in visual perception, orientation tuning in primary visual cortex (V1) has long been thought to play an important functional role in perception, but tests of this role have only recently been made possible through direct optogenetic stimulation of mouse cortex. Indeed, optogenetic stimulation reveals that a surprisingly small number of excited neurons (~20) can drive both large-scale neural ensembles (~1000) and perception. We develop a mean field neural circuit theory for how such small stimulation can create large responses. Our theory also explains constraints on avalanche like behavior widely observed in the spontaneous activity of many species. We further study decision-making at a global-brain scale leveraging multi-region neuropixel recordings collected across a collaboration of 11 experimental labs (part of "the International Brain Lab" (IBL)). We analyzed a massive dataset consisting of the neural activity of 300,000 neurons in mice performing a decision making task and developed techniques to elucidate the contribution of many different brain regions to decision making. Through decoding analyses, we discovered that task and behavioral variables can be decoded from regions which are both classically known as well as other regions which are functionally surprising. Overall, through theoretical analysis of unprecedented datasets, we propose a mechanism for perception in V1 and uncover region-specific details of brain-wide activity underlying decision making.

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Ganguli Surya
Baccus Stephen
Good Benjamin
Fast decarbonization in a cost-based wholesale power framework: the case of Chile

This study provides an extensive examination of the Chilean power generation market in the last eight years, with a particular focus on the impact of renewable energy sources such as solar and wind on traditional power generation and in the total producer surplus in the short-term market. The research highlights the changing operating patterns of traditional power plants, with a general trend of following net demand, and a corresponding increase in heat rates.
Key figures from the study indicate that, despite the retirement of inefficient coal units and the operation of more efficient ones, heat rates have been increasing, likely due to the higher variability in operation. Moreover, there has been a clear change in the operating pattern of coal based units, from a stable base load, to an hourly variable scheme following net demand. Similar changes have experienced some hydroelectric power plants.
The study also explores the producer surplus in the Chilean short-term market, showing two main findings. First, due to their high correlation and systemic character, solar generation impacts on spot prices has significantly decreased the average perceived price by that technology, unlike other renewable sources such as wind and hydroelectric. Second, under the current cost-based wholesale market design, all zero-cost generation shocks produce a negative impact on total producer surplus, with a small magnitude until the shock becomes relevant. When the generation of zero-marginal cost source is large as a proportion of the demand, the impact on producer surplus becomes disproportionately big.
The research underscores the significant role of ancillary services in the power generation segment, which have grown significantly in recent years. These services, although outside the spot market, contribute to the variable costs in the power generation segment.
The findings might have important implications for energy policy and future research, particularly in the context of renewable energy integration and the pursuit of sustainable and efficient energy systems. The study also underscores the need for further research into the theoretical underpinnings of the quantitative impact of zero-marginal cost generation shocks on producer surplus and short-term prices.

Keywords: Public Policy
Energy
Wholesale Power
Energy Transition
Market Design
Electricity Markets
Chile
Renewable Energy
Stanford University
Humanities and Sciences
Public Policy Program
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Thesis
Advisor: Wolak Frank
Feature-prompting Protocols for Data-driven Health Estimation and Lifetime Prediction of Lithium Metal Batteries

Lithium metal batteries (LMB) represent one of the most promising battery technologies due to their high energy density. However, the commercial adoption of LMBs has been hindered by their limited lifespan and safety concerns resulting from lithium metal’s high electrochemical reactivity. Effective health estimation and lifetime prediction could enable timely maintenance actions before failure and a rapid LMB development iteration. In this work, we first propose novel diagnostic test and aging cycling protocols that enable the tracking of LMB degradation from voltage-current measurements through extracting internal resistance, relaxation voltage, and charging impedance. With these features, we present an automatic data-driven aging modeling framework for LMBs. Validation over test cells demonstrate accurate health estimation performance using only one cycle of voltage and current data, and reliable lifetime prediction using cells’ early-life data. The proposed model provides explanatory insights tailored to LMBs, and does not require additional internal state sensors.

Keywords: Li-metal battery
health estimation
lifetime prediction
Machine learning
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Thesis
Advisor: Onori Simona
Finding Brick Kilns from Space: A Geovisual Search Pipeline

Brick production is central to economic development in Bangladesh. The industry is dominated by traditional brick kilns that generate substantial environmental and human health harms. Despite numerous regulations governing where brick kilns can be established and the type of manufacturing processes used, brick kilns are poorly regulated and tracked by the government. A lack of objective data on how many kilns exist, what type of technology is used, and where they are located inhibits ef- fective governance. Yet obtaining this type of data is difficult given the low-capacity of the government. We address these limitations by taking advantage of advances in machine learning and satellite imagery to develop a scalable and replicable geovisual search pipeline to visually identify all of the brick kilns in Bangladesh. By taking a weakly supervised approach to localization, we create a model that detects kilns using only sparse classification data. We also introduce an approach for scaling that can significantly improve the efficiency of models run on satellite imagery.

Keywords: Brooks, Nina
Ermon, Stefano
computer science
machine learning
computer vision
sustainability
health
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Thesis
Fine-grained importance for perceptual video compression

The proliferation of videos that are consumed by humans over the internet has accelerated the search for better video compression algorithms. Traditional video compression algorithms reduce video file sizes by removing spatial, temporal, and coding redundancies. Because different spatio-temporal regions of the video differ in their relative importance to the human viewer, there is an opportunity to improve video compression algorithms even further, by removing perceptual redundancy. However, it is challenging to infer what the levels of importance are to the viewer in different areas, or to even collect such fine-grained information. Indeed, such information is often not used during compression beyond low-level heuristics. In this dissertation, we present a framework that facilitates research into fine-grained subjective importance in compressed videos, which we have utilized to improve the rate-distortion performance of existing video codecs. The specific contributions of the work presented in this dissertation are threefold: (1) we designed a novel tool, the Perceptual Importance Map collection Tool (PIMTool), an interactive web-tool which allows scalable collection of fine-grained perceptual importance by having users interactively paint spatio-temporal maps over encoded videos. While users use the tool, the videos presented to the users are constantly updated based on their painted spatio-temporal maps, showing the users the trade-off between improving the importance of certain areas and decreasing the importance of other areas. This tool also allows users to control the magnitude of increase or decrease of the importance in different areas in the video, resulting in detailed relative importance maps; (2) Using PIMTool, we collected a dataset of 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos. We call this dataset the Perceptual Importance Map Dataset (PIMD). Via a subjective study, we demonstrate that encoding the videos in our dataset while taking into account the importance maps leads to higher perceptual quality at the same bitrate, with the videos encoded with importance maps preferred 1.8x over the baseline videos; and (3) we used our curated dataset to train a lightweight machine learning model that can predict these spatio-temporal importance regions. We call this model the Perceptual Importance Map Model (PIMM). Our results show that for the 18 videos in our test set, the importance maps predicted by our PIMM model lead to higher perceptual quality videos, 2x preferred over the baseline at the same bitrate

Keywords: Engineering.
Electrical Engineering.
Advisor: Katti Sachin
Rippel Oren 
Weissman Tsachy