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Yucheng Lu |
Short Bio
I am a Ph.D. student 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 and provably correct machine learning systems. I obtained my BEng degree in Electronic Engineering from Shanghai Jiao Tong University.
Updates
[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!
[Apr’21] Short version of HyperDeception is accepted by Robust ML workshop in ICLR’21, we discussed how hyperparameter optimization can be misleading!
[May’20] Moniqua is accepted by ICML’20, we discussed how to compress communication in learning systems without additional memory!
Experience
Microsoft Research, Research Intern, Summer 2021
Amazon Web Services (AWS) AI Lab, Applied Scientist Intern, Summer and Fall 2020
NetDB at University of Pennsylvania, Visiting Scholar, Summer 2017
IIOT at Shanghai Jiao Tong University, Undergrad Intern, Spring 2017
Teaching
CS 4787 Principles of Large-Scale Machine Learning, Teaching Assistant, Spring 2021
CS 6787 Advanced Machine Learning Systems, Teaching Assistant, Fall 2019
Service
Journals: TON-2017; TPAMI-2020; Big Data-2021
Conferences: ICML-2020,-2021; NeurIPS-2020,-2021; ICLR-2022
Cornell CS PhD Admission, 2021