PhD Dissertation Proposal Defense
PhD Dissertation Proposal: Jorge Murillo, Carbon-Aware Spatial Shifting for Internet-Scale Services
This thesis explores new spatial shifting techniques to reduce carbon emissions while maintaining performance.
PhD Dissertation Proposal Defense: Shiv Shankar, Counterfactual Inference in the Era of User Privacy and Gen-AI Models
This dissertation addresses emerging challenges and proposes innovative approaches to adapt A/B testing methodologies to modern constraints while maintaining...
PhD Dissertation Proposal: Hansi Zeng, Generative Information Retrieval for the Real World
In this proposal our goal is to make generative retrieval scalable and practical for real-world information retrieval systems.
PhD Dissertation Proposal Defense: Rajarshi Bhattacharjee, Query Efficient Algorithms for Matrix Spectrum Approximation
This thesis develops and analyzes algorithms for several key problems related to eigenvalue and eigenvector estimation...
PhD Dissertation Proposal: Xiao Liu, Communication-Efficient Multi-Device Inference for Transformer Models
This dissertation studies communication-efficient multi-device inference for Transformer models under bandwidth-limited settings.
PhD Dissertation Proposal: Ojaswi Acharya, Practical Advances in Modern Cryptographic Primitives
In this thesis, we identify and rectify some remaining limitations of such cryptographic constructions and their proofs of security.
PhD Dissertation Proposal: Prateek Mantri, Large-Scale Quantum Networks: Architecture and Performance
This thesis investigates architectural principles for large-scale quantum networks across three settings.
PhD Dissertation Proposal: Deep Chakraborty, Information-Theoretic Methods for Understanding and Improving Representations in Neural Networks
In the first part of this thesis, we formulate a general-purpose information-theoretic criterion that allows further improving SSL representations.
PhD Dissertation Proposal Defense: Aparimit Chandra, Performance of Operational Quantum Network Processes
We focus on three fundamental processes: quantum teleportation with noisy memories, error correction in quantum storage, and distributed blind quantum computing
PhD Dissertation Proposal: Elita Lobo, Robust Machine Learning Methods for Uncertain Environments
This thesis addresses challenges with robust algorithms tackling different aspects of uncertainty, including RL, resource allocation, explainability and LLMs.
PhD Dissertation Proposal: Miguel Fuentes, Synthetic Data with Applications to Privacy and Ecology
This dissertation shows that two distinct fields - differential privacy and computational ecology - can be addressed through a unified methodological framework.
PhD Dissertation Proposal Defense: Purity Mugambi, Leveraging Data Science and Machine Learning to Discover and Intervene on Treatment Disparities Captured in EHR Datasets
This thesis seeks to understand the extent of health inequity captured in EHR data and investigate how ML models can be redesigned to ensure they maintain...