I am currently a second-year master’s student at New York University(NYU) majoring in Computer Engineering. I am working with Prof. Anna Choromanska and Prof. ‪Parijat Dube‬‬. Previously, I received my B.S. in Computer Science and Engineering from The Chinese University of Hong Kong(CUHK) advised by Prof. ‪David Zhang, Dapeng‬‬ and Prof. Rui Huang. My research interests are distributed machine learning/ applied machine learning/ federated machine learning. Currently, My research mainly focuses on accelerating the convergence speed of distributed machine learning systems using novel system schemes and machine learning methods. I will graduate from NYU in May 2023 and intend to apply for Ph.D. ECE/CS programs.

• Next Step News: Exciting developments ahead! I am thrilled to announce that I will be embarking on a Ph.D. journey at Carnegie Mellon University(CMU), School of Computer Science, starting from the Fall Semester of 2023.
• (This personal website is updated as of February 2023.)

News

• 2-2023: Honored to serve as a reviewer for International Conference on Acoustics, Speech and Signal Processing(ICASSP).
• 1-2023：One paper submitted to International Conference on Machine Learning (ICML-2023) under the supervision of Prof. Anna Choromanska.
• 1-2023: I will work as a graduate research assistant in Learning Systems Laboratory at NYU supervised by Prof. Anna Choromanska in 2023 Spring semester.
• 11- 2022: Open-source a general framework to implement any (de)centralized, (a)synchronous distributed SGD algorithms when models fit into a single machine. The paper, which proposes a novel distributed SGD algorithm, will be submitted to International Conference on Machine Learning (ICML).
• 10-2022: One paper submitted to Conference on Machine Learning and Systems (MLSys).
• 10-2022: One paper submitted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
• 09-2022: I started working with Prof. Anna Choromanska. Our research focuses on novel optimizers for decentralized distributed machine learning systems.
• 05-2022: I started working with Prof. Parijat Dube. He is an adjunct professor at New York University and Columbia University, and a researcher at IBM. Our research focuses on distributed machine learning systems.
• 11-2021: One paper submitted and accepted by Computers in Biology and Medicine.
• 10-2020: I started working with Prof. ‪David Zhang, Dapeng‬‬, researching applied machine learning for health care.

Publications

• Haoze He, Jing Wang, Anna Choromanska, “Local Leader Decentralized Stochastic Gradient Descent”, International Conference on Machine Learning (ICML), submitted, Jan.2023. [code]
• Haoze He, Dube Parijat, “Accelerating Parallel Stochastic Gradient Descent via Non-blocking Mini-batches”, Conference on Machine Learning and Systems (MLSys), Submitted, Oct.2022
• Haoze He, Dube Parijat “RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous Environment via Submodular Partitioning”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Submitted, Oct.2022
• Chaoxun Guo, Zhixing Jiang, Haoze He, David Zhang, “Pulse Signal Acquisition and Analysis for Disease Diagnosis: A review”, Computers in Biology and Medicine, Accepted, Nov. 2021

Education

• M.S. in Computer Engineering at New York University, 2020
  • Current GPA (After 2022 Fall semester): 3.917/4.0, Rank: top3%
• B.S. in Computer Science and Engineering at The Chinese University of Hong Kong, 2020

Open-Source

• Built an open-source/general framework for anyone interested in distributed machine learning. Using this framework, you can implement any centralized/ decentralized, synchronous/ asynchronous distributed SGD algorithms when models fit into a single machine. In addition, this framework provides you a continent way to fulfill any network topology for decentralized SGD.
• Build an open-source website for NYU EECS/DS community and help 150+ NYU students each semester. This website summary the open-source courses in NYU EECS/DS, provide links and repositories for each course, list the workload, and provide course experiences for reference. Anyone from the NYU community is welcome to fork and contribute!