Engineering 2

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 […]

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 […]

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 […]

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 […]

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 on physical consistency, which for the systems studied here rests in large part on spectral fidelity, the accurate reconstruction of variance across spatial scales. This […]

Medical image representation has long been dominated by voxel-grid matrices. While their inherent structure and order work efficiently for various linear transformations and provide a seamless visualization method on monitors, they fail to preserve the topology of the scan and to encode sparse information in a memory-efficient way.   The recent emergence of machine learning-based continuous coordinate-based […]

As datacenters scale up and become more geographically distributed, wide-area network inter-datacenter traffic, which typically consists of data-heavy tasks, has become increasingly prevalent. Some of the noteworthy challenges raised by […]

A ubiquitous task in statistical practice is that of variable selection, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models […]

While large language models can match or exceed human performance, they do so with memory and energy costs orders of magnitude greater than biological cognition. We investigate sparsity as a brain-inspired computational principle to address both. We first establish a framework for evaluating small language model construction methods, using the next-token logit distribution as a […]

While the human brain learns continually, mastering new tasks without forgetting the old and adapting to unfamiliar ones from context alone, modern neural networks still lack both. To bridge the gap between biological adaptivity and modern AI, we have established foundational work on sparsity as a computational principle at three levels of neural computation, through […]

Graphics Processing Units (GPUs) have become essential platforms for parallel computing, supporting applications far beyond graphics. Central to GPU programming models is its memory consistency specification (MCS), which defines the semantics of concurrent shared-memory operations and interacts with other language features to determine security guarantees such as memory safety. Understanding whether implementations conform to an […]

Due to their increasing complexity, autonomous decision-making agents rely on increasingly advanced algorithms, from classical control theory to reinforcement learning (RL) and, more recently, large vision-language models. While these algorithms help automate the decision-making in complex systems, they bring newer attack vulnerabilities that an adversary can exploit. In this dissertation, we study the security of […]

Real-time monitoring of infectious disease outbreaks calls for statistical models that recover interpretable quantities such as the time-varying reproduction number from noisy count data, track posterior uncertainty, and run on time scales compatible with daily updates. Existing methods address these aims through separate model classes. Discretized Hawkes processes, Poisson autoregressions, and distributed lag models each […]

Edge computing and distributed AI systems increasingly operate under heterogeneous resources, dynamic workloads, and frequent failures, requiring both adaptivity and fault tolerance for efficient execution. In heterogeneous edge clusters, nodes differ significantly in CPU throughput, memory capacity, and network bandwidth, while modern distributed GPU clusters supporting agentic LLM inference must recover large amounts of runtime […]

Modern software increasingly relies on distributed systems to provide accessible, scalable, and reliable services. Choreographic programming brings a global perspective to distributed system development: programmers write a single program that describes the behavior of a whole system, and a compiler projects that global description into local programs run by each node. By making distributed control […]

Many genetics pipelines start by aligning sequencing reads to a reference genome. Aligners attempt to find the position in the reference sequence which best matches the read sequence, but this breaks down when the reads come from a sample with variation relative to the reference. A proposed alternative, pangenome graphs, is supposed to fix such […]

As quantum computing transitions from theory to practice, understanding which algorithms suit near-term devices becomes critical. Current quantum computers are severely constrained by limited qubit counts, short coherence times, and high error rates that quickly degrade computation into noise. This thesis addresses two interconnected questions: what non-trivial computational tasks can near-term devices execute and how […]