PhD Thesis Defense
PhD Thesis Defense: Minhao Cui, Exploiting Pervasive Leakage EM Signals for Communication, Charging, and Sensing
In their thesis, Cui leverages the pervasive ambient leakage signals, which are typically seen as detrimental, to enhance wireless communication performance.
PhD Thesis Defense: Nikko Bovornkeeratiroj, Accelerating Sustainability of the Electric Grid Using Distributed Energy Resources
This thesis puts forth a central focus on sustainability in electricity grids with the presence of distributed energy resources.
PhD Thesis Defense: Sohaib Ahmad, Optimized Resource Allocation for Serving Deep Learning Models
This thesis aims to maximize the resource efficiency of DL model serving by
optimizing resource allocation.
PhD Thesis Defense: Erica Cai, From Text to Networks: Enabling and Investigating Social Measurement via Low-Resource Knowledge Graph Extraction
The thesis is motivated by the challenge of extracting structured instances of action or relationship occurrences from large amounts of unstructured text to...
Advancing Precision Health with Clinical Foundation Models
This dissertation explores the development and application of clinical foundation models (FMs).
PhD Thesis Defense: Abhinav Agrawal, Towards Reliable Black-Box Variational Inference
Probabilistic models are essential for understanding complex systems across various fields, but inference in these models is often intractable.
PhD Thesis Defense: Pratheba Selvaraju, Exploring Representations for 3D Reconstruction From Impaired Real-World
This thesis addresses reconstruction tasks for static and dynamic structures, focusing on buildings and human faces exploring representations...
PhD Thesis Defense: Iman Deznabi, Adaptive Deep Learning Models for Personalized Modeling of Heterogeneous Time-series Data
This thesis focuses on the development and application of adaptive machine-learning models.
PhD Thesis Defense: Jared Yeager, Machine Checked Verification of Validation Tests for Seldonian Algorithms Machine Checked Verification of Validation Tests for Seldonian Algorithms
In this work we produce verified code for a Seldonian algorithm validation test.
Data Driven Expert Assignment
Our algorithms, Greedy Expert Round Robin and FairSequence, assign experts in such a way that no request "envies" another request's assigned experts.
PhD Thesis Defense: Zitian Chen, Toward Unified Expertise: One Model for All Tasks
In this PhD Thesis Defense, Chen will explore neural network architectures that facilitate joint learning across varied tasks.
PhD Thesis Defense: Russell Lee, Learning-Augmented Online Algorithms for Energy Optimization
In this proposal, Lee will present optimal online algorithms for energy optimization in the competitive analysis setting.