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Short BioI am a Ph.D. candidate in Computer Science at Cornell University, advised by Prof. Chris De Sa. I am broadly interested in building scalable, provably correct and ubiquitous machine learning systems. My projects have touched on model compression, communication compression, decentralized/distributed ML, sample ordering, etc. 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. I'm currently looking for full-time jobs in both academia and industry, please reach out! Updates[Jan’23] 0/1 Adam is accepted by ICLR’23, we propose an Adam variant to accelerate LLM pretraining in distributed systems! [Oct’22] NeurIPS’22 Scholar Award, thanks! [Sep’22] GraB is accepted by NeurIPS’22, we propose algorithms to construct provably better data permutations than random reshuffling! [Feb’22] Won Meta PhD 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] 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 won 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! |