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Enmao Diao (刁恩茂)

Entrepreneur and Researcher at Duke University
Ambitious, Creative, Curious, Honest, Passionate

Research interests
Distributed Machine Learning, Efficient Machine Learning,
Signal Processing, Artificial General Intelligence
To develop ground-breaking ML methods and cutting-edge AI applications

I was born in Chengdu, Sichuan, China in 1994. I received my B.S. with the highest honor in Electrical Engineering and Computer Science from Georgia Institute of Technology in 2016. I received my M.S. degree in Electrical Engineering from Harvard University in 2018. I received my Ph.D. degree in Electrical Engineering from Duke University in 2023.

news

May 8, 2023 One paper is accepted in UAI 2023.
Jan 23, 2023 One paper is accepted in AISTATS 2023.
Sep 15, 2022 Two papers are accepted in NeurIPS 2022.

selected publications [full list]

2023

  1. AISTATS
    Score-based Quickest Change Detection for Unnormalized Models
    Suya Wu, Enmao Diao, Taposh Banerjee, and 2 more authors
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
  2. UAI
    Robust Quickest Change Detection for Unnormalized Models
    Suya Wu, Enmao Diao, Jie Ding, and 2 more authors
    In Uncertainty in Artificial Intelligence (UAI), 2023

2022

  1. NeurIPS
    GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
    Enmao Diao, Jie Ding, and Vahid Tarokh
    Advances in Neural Information Processing Systems (NeurIPS), 2022
  2. NeurIPS
    SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
    Enmao Diao, Jie Ding, and Vahid Tarokh
    Advances in Neural Information Processing Systems (NeurIPS), 2022
  3. ICLR
    Pruning Deep Neural Networks from a Sparsity Perspective
    Enmao Diao, Ganghua Wang, Jiawei Zhang, and 3 more authors
    In The Eleventh International Conference on Learning Representations (ICLR), 2022

2020

  1. DCC
    DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
    Enmao Diao, Jie Ding, and Vahid Tarokh
    In 2020 Data Compression Conference (DCC), 2020
  2. ICASSP
    Speech Emotion Recognition with Dual-Sequence LSTM Architecture
    Jianyou Wang, Michael Xue, Ryan Culhane, and 3 more authors
    In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
  3. ICLR
    HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
    Enmao Diao, Jie Ding, and Vahid Tarokh
    In International Conference on Learning Representations (ICLR), 2020

2019

  1. Big Data
    Restricted Recurrent Neural Networks
    Enmao Diao, Jie Ding, and Vahid Tarokh
    In 2019 IEEE International Conference on Big Data (Big Data), 2019