PhD Thesis Defense
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.
PhD Thesis Defense: Forsad Al Hossain, Towards Privacy-Sensitive Edge-Based Crowd and Syndromic Signal Monitoring Contactless Systems
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...