PhD Thesis Defense
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.
PhD Thesis Defense: Devdhar Patel, Time Aware Intelligence for Efficient and Resilient Control
This thesis proposes an alternative path toward intelligent control by drawing inspiration from biological systems and exploring the concept of time-aware...
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.