PhD Dissertation Proposal Defense

This thesis explores new spatial shifting techniques to reduce carbon emissions while maintaining performance.

In this proposal our goal is to make generative retrieval scalable and practical for real-world information retrieval systems.

This dissertation studies communication-efficient multi-device inference for Transformer models under bandwidth-limited settings.

This thesis investigates architectural principles for large-scale quantum networks across three settings.

In this thesis, we show that changing current adoption trends could increase CO2 reduction by 40%, using multi-objective evolutionary learning.

This thesis investigates generative models for parametric curves and surfaces.

We focus on three fundamental processes: quantum teleportation with noisy memories, error correction in quantum storage, and distributed blind quantum computing

In this thesis, I focus on the computability of allocations which maximize well known fairness objectives

This thesis addresses challenges with robust algorithms tackling different aspects of uncertainty, including RL, resource allocation, explainability and LLMs.

This proposal outlines a program for bringing that causal toolkit to mechanistic models.

This dissertation shows that two distinct fields - differential privacy and computational ecology - can be addressed through a unified methodological framework.

The central argument of this thesis is that effective search in these regimes requires evolving beliefs.