Yinwei Dai

I am a third-year Computer Science Ph.D. student at Princeton University, working with Prof. Ravi Netravali. I am affiliated with Princeton Systems for AI Lab (SAIL).

I obtained my M.S.E. and B.S.E. in Computer Science at University of Michigan, where I worked with Prof. Mosharaf Chowdhury and Prof. Harsha V. Madhyastha on projects related to networked systems, and B.S.E in Electrical and Computer Engineering from Shanghai Jiao Tong University

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My research interests are at the intersection of networked systems and machine learning. Recently, I have been working on improving the efficiency and scalability of systems for LLM inference.

We present Apparate, the first system that automatically injects and manages Early Exits for serving a wide range of models.

We present E3 to address the detrimental trade-off that Early Exits introduce between compute savings (from exits) and resource utilization (from batching) in EE-DNNs.

We propose Auxo, a scalable FL system that enables the server to decompose the large-scale FL task into groups with smaller intra-cohort heterogeneity.

We introduce ModelKeeper, a cluster-scale model service framework to accelerate DNN training, by reducing the computation needed for achieving the same model performance via automated model transformation.

We present FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research.