PhD Thesis Defense
PhD Thesis Defense: Bin Wang, Resource Allocation for Latency-Sensitive Applications in Edge Environments
In this thesis, Wang addresses this gap by presenting model-driven resource allocation algorithms for latency-sensitive applications...
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.
PhD Thesis Defense: Lijun Zhang, Advanced Resource-Efficient Multi-Task Learning
This thesis addresses these challenges through a series of innovations in multi-task learning.
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.
PhD Thesis Defense: Shuwa Miura, Optimized Resource Allocation for Serving Deep Learning Models
This thesis introduces a unifying model for generating behaviors that not only achieve desired goals, but also account for how these behaviors are perceived.
PhD Thesis Defense: Walid A. Hanafy, Carbon-aware Resource Management for Cloud Computing Platforms
In this thesis, Hanafy proposes novel resource management techniques that allow cloud users and operators to reduce their operational carbon emissions.