Ph.D. Presentations

Modern datacenter CPUs lose 25–66% of execution cycles to instruction-delivery stalls. This bottleneck persists, despite the recent trend towards accelerators and GPUs, as there is continuing demand by applications that only execute on CPUs. Two workload classes dominate today’s datacenter execution cycles: hyperscale server software (databases, build systems, and content stores), whose large instruction footprints […]

This dissertation investigates how neuromorphic and brain-inspired principles can guide the design of efficient neural language architectures. It addresses two central limitations of modern Transformer-based language models: memory growth with context length and high computational cost from dense matrix multiplication. Through studies of spiking neural networks, linear-recurrent language models, hybrid attention architectures, MatMul-free models, and […]

Recently, a class of non-invasive hardware side-channel attacks has been discovered in field-programmable gate arrays (FPGAs). These attacks extract remnants of prior users’ activity that persist as transistor defect states within reconfigurable routing resources. These remnants are known as FPGA Pentimenti. Resistive random-access memory (ReRAM) is a compelling candidate for pentimenti-like attacks beyond FPGAs. However, […]

The electric grid is essential to modern society, and recent developments such as renewable energy sources (RESs), battery energy storage systems (ESSs), and microgrids (MGs) have necessitated novel computational methods for planning and operations. Machine learning offers a promising lever here, both as an accelerator for and proxy to traditional optimization-based problems. In this thesis, […]

Intracellular symbionts are microorganisms, such as bacteria, that live within host cells. These associations are widespread throughout the invertebrate tree of life, and can perform a diversity of key metabolic, immune-response, or other functions that the host is dependent on for survival or reproduction. Intracellular symbioses allow both the host and the symbiont to occupy […]

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 […]

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 […]

Self-reflection holds significant potential for learning, behavior change, and emotional processing, yet designing technologies that effectively support it remains challenging, particularly when reflection involves difficult experiences such as failure. Most current technologies avoid negative experiences altogether, leaving users without support at precisely the moments when reflection could be most valuable. This dissertation investigates how technology […]

The growing complexity of high-performance computing (HPC) systems poses a fundamental challenge for domain scientists, whose primary objective is to obtain scientifically valid results rather than to optimize resource utilization. Modern leadership-class facilities combine heterogeneous CPUs, GPUs, and specialized accelerators across systems that simultaneously support traditional scientific simulations and AI-driven workloads. This creates a vast, […]

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 […]

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 […]