Events

T cell receptors (TCRs) mediate antigen-specific immune responses through recognition of peptides presented by major histocompatibility complex (pMHC) molecules. Accurately predicting TCR–pMHC interactions remains a major barrier to TCR-based immunotherapy, […]

The explosive growth of genomic data, driven by advances in sequencing and inference technologies, presents both an opportunity and a challenge for evolutionary biology and public health. Existing visualization and […]

Neurodevelopmental and neuropsychiatric disorders (NPDs), such as autism spectrum disorder and schizophrenia, are among the most heritable yet mechanistically complex conditions. While large-scale genomic studies have identified hundreds of high-confidence […]

Machine learning endows autonomous systems to uncover underlying structures and physical laws from measured data and to leverage these models for prediction and decision-making. As the costs of data acquisition, […]

Collaborative virtual reality environments have the potential to greatly impact scientific progress, especially those relating to existential human problems. Within these virtual environments, scientists could view and interact with spatial […]

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 […]

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 […]

Modern machine perception is powerful yet brittle: failing in response to subtle data adversaries and lacking mechanisms to learn from their errors. We address this challenge by progressing from diagnosing such failures to developing a framework for persistent learning. We first investigate the sources of this fragility, demonstrating how both naturally occurring adversarial artifacts, like […]

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 wound photographs and the clinical relevance of bacterial burden, the study implements a multi-task U-Net architecture for both image reconstruction and binary classification in a […]