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I'm a Quantitative Researcher at Point72. I was a Master's student at Carnegie Mellon University, where I studied machine learning and large-scale systems. I received my Bachelor's Degree at Shanghai Jiao Tong University (SJTU), where I worked on the interpretability of deep learning with Professor Quanshi Zhang. I also worked on self-supervised learning in NLP and machine learning for healthcare at UC San Diego with Professor Pengtao Xie. I spent a wonderful time doing NLP research and building production tools at Alibaba DAMO Academy. I also worked as a quantitative research intern at Higgs Asset, building high-frequency predictive models in the financial market, which is a lot of fun. I'm interested in building high-performance reliable machine learning solutions to real-world problems. I'm exicited about the intersection of machine learning and systems, which is a promising direction to deal with large-scale datasets and models. Email / Google Scholar / Linkedin / Github / More |
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Meng Zhou, Xin Li, Yue Jiang, Lidong Bing Findings of the Association for Computational Linguistics: ACL 2023 Meng Zhou, Zechen Li, Pengtao Xie Transactions of the Association for Computational Linguistics, Volume 9 (TACL), 2021 Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP), 2021 Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang Proceedings of the 39th International Conference on Machine Learning (ICML) 2022 Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang Advances in Neural Information Processing Systems 34 (NeurIPS), 2021 Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, Pengtao Xie arXiv:2005.12766 Weiwen Xu, Xin Li, Wenxuan Zhang, Meng Zhou, Lidong Bing, Wai Lam, Luo Si arXiv:2212.04755 |
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Here are a few projects that I enjoyed much: |
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Achieved 1st place in the ASR and attention-based speech recognition task and 2nd place in the face verification task among ∼200 students. Additional details regarding these competitions can be found here and here. (1) Utilized Terraform for the automation of AWS resource provisioning and fine-tuned AWS Auto-Scaling Policies and Controller to suit various workloads, leading to cost reductions and a 30% improvement in RPS. (2) Constructed a large-scale ETL Processing pipeline using Spark to process the CommonCrawl dataset, which contains 2.85 billion web pages. Implemented forward and backward functionality of various operators to support a minimal functional PyTorch-like deep learning library. By leveraging these operators, built common neural network modules, e.g. Linear layer, Residual Block, Convolution layer, and LSTM. Further, some hardware acceleration techniques like tiling in the array backend for matrix multiplication were used to speed up the Needle framework. Built a fully-functional hybrid file system using a local SSD and Amazon S3, known as CloudFS. The system supports block-level deduplication to reduce cloud storage costs and snapshots to back up the state of the file system at a certain timestamp. Caching further reduces the data transfer cost by about 50%. Implemented essential components for BusTub including: (1) A buffer pool manager moving physical pages back and forth from main memory to disk; (2) A disk-backed extendible hash table for indexing; (3) A query executor with the iterator query processing model; (4) A lock manager used to support concurrent query execution. Developed various features in the xv6 operating system, including: (1) Copy-on-write fork to defer allocating copying physical memory pages (2) System calls like symlink, sysinfo, sigalarm and mmap, etc.; (3) Concurrent memory allocator and buffer cache. |
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| 11-785 Introduction to Deep Learning (PhD Level), Fall 2023, CMU |
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| Reviewer for: NeurIPS 2022-2023, ICML 2022, ACL 2022, ACL Rolling Review, AAAI 2021, NAACL 2021, ICLR 2021 workshop “Machine Learning for Preventing and Combating Pandemics” |
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Modified from here
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