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Yucheng Lu (陆昱成) |
Short Bio
I am a Ph.D. candidate in Computer Science at Cornell University, advised by Prof. Chris De Sa. Our group webpage can be found here. I am broadly interested in building scalable, provably correct and ubiquitous machine learning systems. My work has been recognized by ICML Outstanding Paper Award (Honorable Mention) and Meta PhD Fellowship. I've also worked/interned at Microsoft DeepSpeed, Google Cerebra and Amazon Forecast. I obtained my BEng degree in Electronic Engineering from Shanghai Jiao Tong University.
Please reach out if you'd like to chat!
Updates
[Feb’22] Won Meta PhD Research Fellowship 2022, thanks Meta!
[Jan’22] QMC-Example-Selection is accepted by ICLR’22 as spotlight (5%), we analyzed the complexity for example selection and proposed two related algorithms!
[Oct’21] Won Outstanding Reviewer Award (8%) at NeurIPS’21!
[Sep’21] HyperDeception is accepted by NeurIPS’21, we studied justifiable hyperparameter optimization via modal logic!
[Jul’21] DeTAG wins Outstanding Paper Award Honorable Mention at ICML’21 (5 out of 5513 submissions)!
[May’21] DeTAG is accepted by ICML’21 as Long Oral (3%), we discussed the theoretical limits of decentralized training, and how to achieve it!
[May’21] SCott is accepted by ICML’21, we discussed how to use stratification in training forecasting models!
[May’20] Moniqua is accepted by ICML’20, we discussed how to compress communication in learning systems without additional memory!