How can learning algorithms reduce sampling, communication, memory, and optimization costs without losing rigor?
Work on bandit sampling, distributed optimization, sparse communication, transformer memory, and memorization capacity.
The problem
Modern learning systems incur high computation, communication, and memory costs at scale.
The approach
Develop sampling, projection-free optimization, communication-efficient training, memory replay, and analytical capacity methods.
Work on bandit sampling, distributed optimization, sparse communication, transformer memory, and memorization capacity.
Main contributions
- Research framing and system design
- Methods, implementation, and empirical evaluation
- Open research artifacts and scholarly dissemination