Engineering 2

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 […]

Centromeres ensure proper chromosome segregation during cell division, yet the organization and regulation of centromeric chromatin within satellite DNA arrays remain incompletely understood. Here, we leverage the complete diploid human genome benchmark (T2T-HG002) to provide a detailed study of centromeric sequence and chromatin architecture on individual haplotypes. Using adaptive-sampling-enriched, ultra-long-read DiMeLo-seq, we achieve single-molecule chromatin […]

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, […]

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, […]

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 […]

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 […]

Autonomous Cyber Operations (ACO) agents promise effective security automation with minimal human intervention, yet their deployment raises three interconnected challenges: agents must be realistic (reproducing diverse attacker sophistication), secure (preventing autonomy from becoming an attack surface), and feasible (safely replicating human behavior at full autonomy). We argue that these three properties are requirements for ACO […]

This work addresses improvements in branch prediction mechanism to support high perfor- mance processors. The state of the art aims to balance the prediction latency and prediction accuracy using multi level correcting predictors . Prior published work focusses on scalar designs and prediction accuracy improvement for hard to predict branches employing tailor made, non generic […]