VictorJuiz/thesis huggingface dataset:
Reliable machine learning in the wild

Machine learning systems today are widely deployed but unreliable: they can work well in some contexts but fail in others, with severe consequences. For instance, facial recognition models can do well on average but fail on particular demographic subpopulations; and medical models can do well in the hospitals they are trained on, but fail in different hospitals. This dissertation studies three aspects of building reliable machine learning systems that are robust to these failures. First, we introduce a tool for inspecting models and understanding how and why they fail. Since training data is the key ingredient in any machine learning model, we build on the classical notion of influence functions to quantify the influence of each training example on a model's predictions, allowing us to trace errors back to the training data and better understand how to correct them. Second, we develop a distributionally robust optimization (DRO) method for directly training models to perform well across different subpopulations. These subpopulation shifts arise in many important applications, from the facial recognition example above, to medical applications (with different patient subpopulations) and to remote sensing applications (with different geographic subregions). Third, we discuss benchmarks for distribution shifts more generally; this includes subpopulation shifts, as well as shifts across different environments, such as different hospitals. We introduce WILDS---a benchmark of in-the-wild distribution shifts spanning applications such as pathology, conservation, remote sensing, and drug discovery---and show how state-of-the-art methods, which perform well on synthetic shifts, fail on these real-world shifts. Our results underscore the urgent need to ground method development and evaluation in the application settings that arise in the wild

Advisor: Liang Percy
Leskovec Jurij
Ma Tengyu
Reprogramming cancer into antigen presenting cells as a novel immunotherapy

Therapeutic cancer vaccination seeks to elicit activation of tumor-reactive T cells capable of recognizing tumor-associated antigens (TAAs) and eradicating malignant cells. Here, we present a cancer vaccination approach utilizing myeloid lineage reprogramming to directly convert cancer cells into tumor reprogrammed-APCs (TR-APCs). Using syngeneic murine leukemia models, we demonstrate that TR-APCs acquire both myeloid phenotype and function, process and present endogenous TAAs, and potently stimulate TAA-specific CD4+ and CD8+ T cells. In vivo TR-APC induction elicits clonal expansion of cancer-specific T cells, establishes cancer-specific immune memory, and ultimately promotes leukemia eradication. We further show that both hematologic cancers and solid tumors, including sarcomas and carcinomas, are amenable to myeloid-lineage reprogramming into TR-APCs. Finally, we demonstrate the clinical applicability of this approach by generating TR-APCs from primary clinical specimens and stimulating autologous patient-derived T cells. Thus, TR-APCs represent a cancer vaccination therapeutic strategy with broad implications for clinical immuno-oncology

Advisor: Majeti Ravindra
Bendall Sean
Engleman Edgar G.
Levy Ronald
Resolving Conformational Changes Within the Mu-opioid receptor via smFRET and its Correlation with G-Protein Activation

The Mu-opioid receptor is a therapeutic target of great importance and underlies the site of action for opiates. These opiates can have adverse effects that are related to the ability of the Mu-opioid receptor to signal with a defined level of activity. Alternatively, opiates may cause a particular bias to produce favorable therapeutic goals such as analgesia and avoid side-effects such as respiratory depression that may lead to death. Here, we have purified and labelled the Mu-opioid receptor to study the change in conformation it undergoes when binding opiates. Conformational changes were measured via single molecule FRET (smFRET), a biophysical technique that reveals the functions, kinetics, and conformation dynamics of individual molecules by monitoring the energy transfer between two probes (i.e., a donor and acceptor probe). We identified ligand dependent changes in conformational states in real time and directly measured the conformational changes that the receptor undergoes when it interacts with its cognate G-protein. Additionally, we attempted to reinsert purified Mu-opioid receptor by nanopore electroporation into live cells to confirm its functionality in the cell membrane. This attempt laid the groundwork for an in vivo model required for future evaluation of ligand driven conformational effects. Continued work will use more sophisticated methods of single-molecule FRET to resolve the conformational states of the receptor and to characterize the effects that ligands of varying efficacies have on G-protein interactions.

Keywords:  Department of Biology
Biology
single molecule Fluorescent Resonance Energy Transfer (smFRET)
Mu-opioid receptor (µOR)
G-protein Coupled Receptor (GPCR)
Nanopore Electroporation
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Thesis
Advisor: Chu Steven
Barnes Christopher
Resonant photoelastic modulation for time-of-flight imaging with standard image sensors

Imaging has played a pivotal role throughout human history, with various optical imaging systems invented over the last several centuries. Some of these inventions include the pinhole camera, the photographic film, charge coupled device sensors, and most recently CMOS image sensors. CMOS image sensors are the dominant image sensor in the present day, offering high performance at a low cost. Standard CMOS image sensors are designed to mimic human vision; they are 2D sensors capable of capturing the brightness and color per pixel. These sensors were primarily designed for humans to capture images, but there is a fundamental change recently in users of imaging systems. Significant developments in robotics and artificial intelligence in the last decade have enabled intelligent autonomous platforms. These platforms, similar to humans, require sophisticated imaging systems to interact effectively with their environment. The existing standard image sensors (2D sensors) are not the most effective sensors for these platforms; depth (3D) sensing provides significant value. Unfortunately, existing standard CMOS image sensors are unable to measure the depth dimension. Although custom-built 3D sensors are able to measure the depth axis, it would be a significant advance to adapt the already mature and high-performance standard image sensors to function as high-resolution depth sensors. In this work, we describe a method to convert a standard CMOS image sensor into a depth sensor by integrating it with a fundamentally new kind of free-space intensity modulator. We explain the design, fabrication, and working principle of this new free-space intensity modulator that we refer to as a longitudinal piezoelectric resonant photoelastic modulator. The optical modulator described in this work uses a simple fabrication process while exhibiting record-setting energy efficiency among resonant free-space intensity modulators operating in the megahertz frequency regime. The simple fabrication process and low-drive power required to operate the modulator allow for easy integration with any standard image sensor. We demonstrate proof-of-concept time-of-flight imaging experiments by integrating the fabricated modulator with a standard CMOS image sensor

Keywords: Electrical Engineering.
Advisor: Arbabian Amin
Miller D. A. B.
Safavi-Naeini Amir H.
Revealing the unforeseen role and sensitivity of anoxic protection in soil carbon cycling

Increasing soil carbon (C) content promises to mitigate climate change and enhance soil fertility. Soil C content is determined, in part, by microbial respiration, which converts soil C into carbon dioxide. Carbon protection mechanisms represent processes and conditions that limit microbial respiration of soil C. Anoxic microsites, zones of oxygen depletion in otherwise oxic soils, are a recently recognized and under-studied soil C protection mechanism. In this dissertation, I use field and laboratory methods to determine the extent and contribution of anoxic microsites to soil C protection within natural and agricultural systems. In Chapter 2, I measure the dissolved oxygen content of soil porewater from California agricultural soils. I show that physical disturbance destroys anoxic microsites through enhancing oxygen supply to the smallest soil pores. In Chapter 3, I show that oxygen limitations constrain OM turnover in a Hawaiian rainforest soil, an environment where mineral content is presumed to be the dominant soil C protection mechanism. In Chapter 4, I examine a soil textural gradient at the Stanford Dish and demonstrate that anoxic microsites are particularly important for protecting C in coarsely textured soils. Finally, in Chapter 5, I use droplet digital PCR to quantify anaerobe DNA (a proxy for anoxic microsites) in soils from four long-term agricultural experiments across the continental United States. I show that anoxic microsites vary with soil properties, respond to management, and uniquely contribute to soil C stabilization within cropland soils. In sum, this dissertation reveals that anoxic microsites represent a vulnerability in the soil C stock but also an opportunity to enhance soil C storage

Keywords: Earth System Science.
Advisor: Fendorf Scott
Chadwick Oliver A.
Dekas Anne
Jackson Rob
Revisiting program slicing with ownership-based information flow

Program slicing is the technique of automatically identifying the subset of a program that is relevant to a particular piece of code. In theory, slicing addresses one of the core challenges of modern-day programming: sorting through large quantities of information to find what is relevant to the task at hand. However, very little is known about how people would actually use a slicer while programming. This dearth of evidence is compounded by the fact that no practical program slicer exists for programmers to use today. This dissertation contributes to the theoretical and practical foundations of program slicing in three ways. First, I provide evidence for the cognitive basis of slicing. I report on a series of experiments that demonstrate how a person's working memory significantly limits their ability to remember information about a program while engaging in a variety of comprehension tasks. These experiments support the design of tools to reduce working memory load while programming, such as program slicing. Second, I describe the theory and implementation of a modular program slicer for the Rust programming language. This slicer is based on a novel information flow analysis, Flowistry, that leverages Rust's type system, namely ownership types, to approximate the behavior of unknown code solely from its static type. With these approximations, code can be analyzed more efficiently (no whole-program analysis) and robustly (no library source code needed). I show that this approximation is provably sound and reasonably precise in practice. Finally, I describe the design of a new program slicing interface, Focus Mode, that interactively visualizes slices as the user changes their focus in a program. I report on a user study of Rust developers debugging programs with Focus Mode. We find that slices do help programmers find relevant code by focusing their attention on code with unexpected side effects. However, slices may also exclude code which is cognitively relevant to understanding a bug, suggesting the need for further work on the design of program analyses and user interfaces for code relevance. Flowistry and Focus Mode are both free and open-source tools that have been downloaded by thousands of Rust developers. The tools are publicly available at https://github.com/willcrichton/flowistry

Advisor: Agrawala Maneesh
Hanrahan P. M. (Patrick Matthew)
Aiken Alexander
Revisiting the role of long-term potentiation in learning and memory

A central function of the nervous system is its ability to enable an animal to learn, defined as the process by which an animal’s behavior changes in response to experience. Long term potentiation (LTP) is a candidate mechanism for learning and memory in the nervous system because LTP, as a form of synaptic plasticity, has the hallmarks expected for a cellular process that enables an animal to learn. Prior studies have established strong correlations between LTP in the hippocampus, a critical center for learning and memory, and spatial learning in rodents. However, causally linking LTP with learning has proven to be difficult. The present study aims to test the relationship between LTP and learning and memory using a novel mouse line with a conditional knock-out (cKO) of Stx3. The Stx3 gene encodes a SNARE protein that was shown in prior studies to be essential for LTP. The mouse line used deletes Stx3 conditionally in excitatory forebrain neurons by crossing Stx3 cKO mice with CamKIIa-Cre driver mice (CamKIIa-Cre;Syntaxin-3fl/fl). Thus, the goal of the present study is to answer the question: Is LTP necessary for learning and memory? To determine the ability of Stx3 cKO mice to perform learning and memory tasks, this study uses spatial and associative learning behavioral assays. Additionally, to confirm that cKO of Stx3 does not have negative effects on neuronal health, this study quantitatively evaluates neuronal morphology. Analyses of neuronal morphology indicate that the cKO of Stx3 did not cause major effects on the architecture of the brain or the morphology of neurons. Furthermore, results from behavioral assays show that Stx3 cKO mice can perform learning and memory tasks similarly to wild-type mice, with no significant differences between the two groups. These findings suggest that LTP may not be necessary for learning and memory in mice. This disrupts the long-standing hypothesis that LTP is causally linked to learning and memory, indicating that another mechanism may be responsible. This work will contribute to our understanding of how the brain learns and offers the first step towards finding solutions for disorders of learning and memory.

Keywords:  Department of Biology
Biology, Neurobiology, Long-term potentiation, Memory, Learning, Stx3
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Thesis
Advisor: Sudhof Thomas
Chen Xiaoke
Revoked Refuge: How Deported Cambodian American Refugees Negotiate the Contradictions Between Citizenship and Belonging

Since 2002, the United States has deported over 1,000 Cambodian American refugees back to Cambodia, with many of these refugees being from the 1.5 generation, having arrived in the US in the 1980s as children with few memories of Cambodia. Southeast Asian American studies scholars and immigration scholars have primarily focused on 1st and 2nd generation Southeast Asian refugees’ resettlement, incorporation into the US, and remembrance of historical trauma. This project seeks to understand the experiences of the 1.5 generation who called the US home but have now been deported back to Cambodia, the country they once fled from. How does this group understand their political and social membership to the US and to Cambodia given their deportation? Between June 2021 and January 2022, I conducted 8 one-time virtual interviews, 7 with deported Cambodian American refugees and 1 with a Laotian American refugee awaiting deportation. I asked questions about growing up in the US, life in Cambodia now, and thoughts on the criminal legal and immigrant control systems. I find themes of refugees’ continued movement between countries and internally within the US, statelessness and community exclusion, and hope in the ongoing search for a refuge. These results further our understandings of how the war in Southeast Asia, US policy, and Cambodian policy continues to impact the lives of these refugees and demonstrates the divide between political-legal and social membership to the US and Cambodia.

Keywords: Cambodian Americans
Refugees
Deportation
Return migration
Imprisonment
Immigrants
Southeast Asia
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Thesis
Advisor: Jimenez Tomas
Rewiring of food webs in the anthropocene : measuring the effects of novel species interactions with environmental DNA

Global biodiversity is threatened by the anthropogenic restructuring of animal communities, rewiring species interaction networks in real-time as individual species are extirpated or introduced. Biodiversity and functionality are not only declining, but human-induced hybridization of wildlife and the shuffling of biomes are also becoming more common. Conservation science and adaptive ecosystem management demand more rapid, quantitative, and non-invasive technologies for robustly capturing changing biodiversity, quantifying species interactions, and measuring ecosystem function to protect remaining systems. Using both non-invasive DNA metabarcoding of diet DNA (dDNA) and network theory, the aim of this dissertation was to develop molecular ecological network analysis (MENA) as an ecosystem assessment tool to address these needs. These methods were first tested in a well-studied biological preserve (Jasper Ridge Biological Preserve, Stanford, CA), where they more rapidly, accurately, and effectively captured the broader biodiversity of the area in comparison to other methodologies (camera trap and soil eDNA), while reconstructing and unveiling the hidden complexity in trophic structure and interaction networks within the community. I then applied MENA in a hybrid ecotone in Garamba National Park, Democratic Republic of Congo to understand the consequences of two African elephant species hybridizing, the critically endangered Forest (Loxodonta cyclotis) and endangered Savanna elephant (L. africana). The aim was to determine how hybrid introgression may currently be shaping the ecology (diet selection and habitat use) and health of the individual elephant (gut endoparasites), while assessing a suite of demographic (age, sex), environmental (habitat type), and social (family vs bull groups) characteristics. A third of this population are hybrids and the admixed genetics influenced helminth composition and significantly reduced host-specific parasite infection. Garamba forest elephants and hybrid elephants have very similar ecologies (dietary composition and habitat use), however, this population of elephants is unique in diet and habitat use compared to other typically frugivorous forest elephant populations and might be fulfilling a vacant niche within the grasslands, maintaining ecosystem function. This research will hopefully equip conservation ecologists and managers with a powerful tool to measure biodiversity and species interactions to assess the integration and impact of novel species in the ecosystem, unveil hidden multitrophic interactions and community structure, and identify key and vulnerable species within a terrestrial system

Keywords: Biology.
Advisor: Hadly Elizabeth Anne
Dirzo Rodolfo
O'Connell-Rodwell Caitlin
Petrov Dmitri Alex
Robot in-hand manipulation using roller graspers

This work describes the development of a class of dexterous robot hands that use steerable, continuously rotating fingertips to perform complex in-hand manipulation of grasped objects, a task that has alluded many widely used robot graspers. While a typical laboratory robot with a parallel-jaw gripper can perform basic pick-and-place tasks, they lack the fine manipulation proficiency needed for more demanding, precise and varied tasks. Inspired by the human hand, many research efforts are focused on creating and controlling human-like (anthropomorphic) hands in the hopes of duplicating human dexterity. One goal of these designs has been to enable robotic in-hand manipulation. Except for small motions, in-hand manipulation with these hands requires grasp-gaiting, a complex process where the fingers "walk" across the object, making and breaking contacts in order to propel the object to a desired pose, which can be an inefficient and difficult approach for a variety of tasks. So far, the fragility, cost, and complexity of these devices have precluded use outside of a laboratory setting. While we share with other researchers the goal of enabling in-hand manipulation, we achieve it by completely different means. We have developed a series of highly non-anthropomorphic grasping devices - the Roller Graspers - that use rotating fingertips to perform full six-degrees-of-freedom (DoF) in-hand manipulation on grasped objects. We do this by intelligently driving the fingertips across the object. The first Roller Grasper used steerable cylindrical fingertip rollers mounted on a pivoting linkage, for a total of three DoF for each of its three fingers. Using scripted motion control, it demonstrated the feasibility of our in-hand manipulation concept. The second and third versions used spherical fingertips which afforded better grasp stability and range of motion. These designs also supported our development of a hierarchical manipulation architecture that allowed the roller graspers to achieve autonomous in-hand manipulation. The manipulation architecture consisted of a sample-based high-level planner and a heuristic low-level policy that allowed the grasper to perform full 6-DoF manipulation of objects with a variety of shapes and sizes. The final version of our hand, called Tactile-Enabled Roller Grasper (TERG), incorporated a novel tactile sensing system that could extract the surface contour of a grasped object as well as the shear force applied at the contact location, even while the fingertips were rotating. This enabled more diverse and robust in-hand manipulation that was not possible in the previous generations

Keywords: Mechanical Engineering.
Advisor: Follmer Sean
Salisbury J. Kenneth
Cutkosky Mark R.
Kennedy Monroe