Ph.D. Presentations

Sequencing technologies have revolutionized our understanding of biology, yet many existing methods require fragmentation of DNA or RNA, fundamentally limiting our ability to study these molecules in their native, intact forms. Long-read sequencing overcomes this constraint by enabling the sequencing of long, single-molecule native DNA and RNA, providing simultaneous access to both sequence and base […]

Centromeric DNA is critical for accurate chromosome segregation and genome stability, but due to its repetitive nature, it was only recently fully included in a human reference. Rapid evolution and sequence diversity in these regions limit the utility of one reference sequence, however. Integrating centromeric and pericentromeric satellite DNA – which together constitute over 5% […]

Every year, tens of thousands of tons of plaster-based materials are used in restoration and conservation applications, many of which are derived from non-renewable sources and discarded at the end of their service life. Here, we introduce a biodegradable, bio-derived composite based on chitosan and calcium carbonate that is composed of simple, widely available constituents […]

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 […]

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 […]

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, […]