Yinwei Dai

I am a Computer Science Ph.D. student at Princeton University, working with Prof. Ravi Netravali.

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, I also collaborated with Prof. David Fouhey and B.S.E in Electrical and Computer Engineering from Shanghai Jiao Tong University

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

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My research interests are at the intersection of networked systems and data-intensive computing.

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Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving
Yinwei Dai*, Rui Pan*, Anand Iyer, Kai Li Ravi Netravali
In Submission / Paper

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

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Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu, Fan Lai, Yinwei Dai, Aditya Akella, Harsha Madhyastha, Mosharaf Chowdhury
SoCC, 2023 Acceptance Rate: 31% / Github / Paper

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.

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ModelKeeper: Accelerating DNN Training via Automated Training Warmup
Fan Lai, Yinwei Dai, Harsha Madhyastha, Mosharaf Chowdhury
NSDI, 2023 Acceptance Rate: 18.38% / Github / Paper / Talk

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

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FedScale: Benchmarking Model and System Performance of Federated Learning
Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, Mosharaf Chowdhury
ICML, 2022 Acceptance Rate: 21.94% / Website / Github
Deployed at Linkedin Best Paper Award at SOSP ResilientFL 2021

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

COS 316: Principles of Computer System Design, Fall 2023

COS 418: Distributed Systems , Winter 2024

EECS 442 Computer Vision, Winter 2022

EECS 489 Computer Network , Fall 2021

Conference Reviewer: NeurIPS (Datasets and Benchmarks) 2022, 2023

Journal Reviewer: Transactions on Mobile Computing 2022

Artifact Evaluation Committee: SIGCOMM 2022, MLSys 2023

My name in Chinese: name photo

If you want to chat with me, please send me an email. :)