PhD Dissertation Proposal Defense

Cloud computing and rapid data growth have driven many organizations to outsource large datasets to cloud databases in order to reduce management costs.

This thesis proposes resource management techniques that mitigate performance degradation caused by such real-world dynamics to ensure robust performance. 

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

This thesis explores the design of a multimodal teacher dashboard that is not tied to a single activity or embodiment type.

In this thesis, we explore how recent progress in generative modeling improves solutions to inverse problems.

This thesis develops and analyzes principled, efficient, and scalable algorithms for answering marginal queries under differential privacy.

This dissertation focuses on developing resource-efficient decision-making policies to address key challenges in various networked systems.

This thesis investigates motion planning and learning based control methods with the goal of developing robust controllers for dynamic legged robots.

This thesis addresses the urgent need for more robust evaluation methodologies by developing scalable, accurate tools and benchmarks for factuality assessment.

This thesis reframes low dimensional representation as a coordinate-learning problem.

This thesis significantly generalizes the scope of metareasoning by treating any factor that restricts an agent's ability to optimize its core objective.

This dissertation develops methods for accurately measuring user trust in software and creates automated techniques for making software more trustworthy.