Presenter: John Ousterhout, Stanford University Abstract: People have been programming computers for more than 80 years, but there is little agreement on how to design software or even what a good design looks like. As a community, we talk a lot about tools and processes, but hardly at all about design. In this talk I […]
Presenter: Dr. Daniel Arnold, Lead Power Systems Engineer, Lawrence Livermore National Laboratory Description: The adoption of new types of generation and loads, such as data centers, small modular reactors, and electric vehicles servicing equipment presents many challenges for system operators who are tasked with maintaining the safety and efficiency of the power grid. New consumption […]
Presenter: Hyeran Jeon, UC Merced Title: Enabling scalable GPU computing via efficient virtual memory systems Abstract: GPUs have become one of the most important accelerators of various emerging workloads. While the massive parallelism makes the GPUs one of the most favorable compute engines, the limited on-device memory capacity hinders their wider adoption. Virtual memory systems […]
Presenter: Dr. Brandon Nelson, Staff Fellow, Division of Imaging, Diagnostics, and Software Reliability (DIDSR), U.S. Food and Drug Administration’s Center for Devices and Radiological Health (CDRH) Description: Artificial intelligence is rapidly transforming diagnostic and interventional radiology, presenting immense opportunities for improving patient care alongside significant regulatory challenges. As AI/ML-enabled devices proliferate, how do we ensure […]
This report presents a one-dimensional Random Choice-based hybrid method for simulating special relativistic hydrodynamics (SRHD) flow problems. The proposed scheme combines a high-order accurate method and a random choice method, […]
Modern applications are increasingly designed with a strong emphasis on scalability and performance, as systems are expected to process ever-growing volumes of data and deliver results with minimal latency. Techniques such as distributed architectures, in-memory computation, and optimized data structures are routinely adopted to meet these performance-driven demands. However, in the pursuit of speed and […]
Open-domain question answering (ODQA) systems come with diverse challenges — ranging from resolving conflicting information to interpreting figurative expressions and representing meaning in a human-understandable form. This dissertation presents three […]
The rapid integration of artificial intelligence and machine learning into clinical practice has driven advances in disease classification, segmentation, and clinical decision support. However, the complexities of medical data pose a challenge to widespread adoption. The rarity of medical conditions, ethical considerations, and varying acquisition protocols leads to limited and noisy data. The time-intensive process […]
WiFi-based indoor positioning is a widely researched area focused on determining the location of devices. Accurate indoor positioning has numerous applications, including asset tracking and indoor navigation. Despite advances, their […]
The presence of coronary artery calcification is a strong predictor for future cardiovascular events where cardiac risk categories are quantified depending on calcification size. Dual-energy chest X-rays provide high contrast […]
This thesis investigates the use of deep learning for classifying wound infections from photographic images, using colony-forming unit (CFU) counts as a quantitative labeling standard. Leveraging the visual information in […]
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence […]
Program verification has been applied in many contexts (including videogames), but the scale and complexity of the examples that have been analyzed fall short of the ability to analyze many existing games without massive computational costs. My research focuses on automatic analysis and design of one particular game: Super Metroid, with the goal of creating […]
Online communities tell stories with the games they play. As continual updates, recurring monetization, and platforms for community discussions have flourished, we have seen a rise in video games using ongoing development to tell stories, and have a community interact with those stories and build upon them. In this dissertation, I study this phenomenon, which […]
Decompositions of large-scale networks are central to many applications in graph mining, network science, and algorithm design. Over several decades, a rich body of work has developed techniques to partition networks with various different objectives. However, a noticeable gap persists between methods with strong theoretical guarantees, and those that perform well in practice. Practical algorithms […]