This thesis explores iterative methods for solving elliptic partial differential equations (PDEs), which are used in computational fluid dynamics (CFD) to model a wide range of physical phenomena. The primary […]
Deep learning models trained on simulation and reanalysis data can now emulate turbulent geophysical flows at a small fraction of the computational cost of numerical solvers. Their scientific utility depends […]
Host-microbe systems are core to some of biology’s most consequential interactions, from the pathogens that drive infectious disease to symbionts affecting agricultural pest control and vector-borne disease transmission. Yet unlike […]
Advances in microscopy have enabled the collection of high-quality single-cell datasets, providing new opportunities to identify the mechanisms underlying complex biological processes. In this work, we develop machine-learning frameworks using single-cell temporal data with the goal of predicting and providing insights into these mechanisms. We produce frameworks for two biological systems, bacterial spore germination, the […]
This PhD research focuses on developing reliable medical foundation models capable of reasoning across textual, visual, and interactive clinical information. The work investigates three complementary directions: improving medical reasoning through test-time scaling, training multimodal medical models with verifiable rewards, and synthesizing high-quality visual question-answering data from biomedical literature using generator-verifier frameworks. Building on these efforts, […]
Modern autonomous-driving models increasingly rely on learned representations and generated reasoning to interpret complex scenes and produce predictions or actions. However, it remains unclear what information these models encode, how that information is exposed through common interpretation methods, and whether their stated reasoning meaningfully influences their behavior. This research investigates these questions across motion-forecasting and […]
In March 2024, a backdoor was discovered in xz Utils, a widely used open source data compression library present in nearly every major Linux distribution. The attack was discovered days before merging into major distributions, and if this had happened, it would have allowed attackers to execute arbitrary code on millions of systems worldwide via […]
The COVID-19 pandemic generated genomic data at unprecedented scale, with tens of millions of SARS-CoV-2 genomes deposited in public repositories and thousands of new sequences added each day. This dissertation develops methods for analyzing genomic datasets at this scale, unified by the idea that encoding genomes according to their evolutionary relationships can make otherwise intractable […]
The accelerating adoption of zero-emission vehicles (ZEVs) in California is reshaping both the transportation and electrical grids at the moment as climate-driven disasters are intensifying in frequency and severity. This dual transition exposes a critical structural gap: existing resilience research treats pre-disaster evacuation readiness and post-disaster grid restoration as separate problems, even though both are […]
Many somatic variant-calling pipelines begin by aligning tumor and matched-normal sequencing reads to a single linear reference genome, such as GRCh38. Because every individual differs substantially from this reference, this approach can introduce reference bias, causing reads to map incorrectly or not at all and potentially leading to missed somatic variants or germline variants being […]
Verbal autopsy (VA) is a well developed tool to collect information describing deaths outside of hospitals by conducting surveys to the relatives and caregivers of the deceased person. It is routinely-implemented in low and middle income countries, where it often lacks sufficient resources to conduct the autopsy. The main task is to estimate both individual […]
Spatially dependent data are common in environmental and climate studies, where Gaussian process models are used for prediction and uncertainty quantification. Learning their covariance structure involves both statistical and computational challenges. Likelihood-based inference may be sensitive to covariance misspecification, while repeated evaluation of prediction-oriented criteria can be computationally demanding. To study these issues, we develop […]
Understanding neuronal function requires measurements that capture both the rapid dynamics and spatial organization of neural activity. Conventional neural interfaces often prioritize either high temporal resolution through electrophysiological recording or […]
Multivariate dependent discrete data routinely arise in microbiome studies. Analyzing these data presents interesting statistical challenges, such as high dimensionality, excess zeros, large heterogeneity across samples, and temporal dependence in […]
Coherent wireless reception depends on accurate channel state information (CSI), yet acquiring CSI through pilot transmission consumes time–frequency resources that could otherwise carry data. This tradeoff becomes more pronounced in […]
Artificial intelligence and machine learning models depend on large and diverse datasets to learn patterns across different situations and generalize to cases they have not seen before. In cybersecurity, suitable […]
The separation of memory and processing in conventional computer architectures leads to significant energy consumption associated with data transfer, known as the von Neumann bottleneck. Two-dimensional (2D) materials provide a […]