Xin Dong

Jie Lei

Cambridge, MA, May 2020

I am a final-year Ph.D. student at Harvard University. I have general research interests in deep learning, with a focus on designing accurate, efficient and trustworthy systems for autonomous machines and AI.

I earned bachelor's degree in computer science from Yingcai Honors College, University of Electronic Science and Technology of China (UESTC) in 2017. I was a research assistant at Nanyang Technological University (NTU) and UC San Diego (UCSD). I am a recipient of the Harvard James Miller Peirce Fellowship.

google scholar github

Email: firstnamelastname [at]
Office: Science and Engineering Complex, 150 Western Avenue, Allston, MA 02134

I am actively looking for full-time Research Scientist/Engineer positions for 2023 Spring.


Selected Research Topics

Distributed Training and Inference for DL (e.g., Federated Learning, Split Computing)
Trustworthy and Private DL (e.g., Out-of-Distribution/Adversarial Samples, Data Privacy)
Data and Computation Efficient Training and Inference (e.g., Pruning, Quantization) for DL Applications (e.g., CV, NLP)


Meta Reality Lab
Research Scientist Intern
Redmond WA, June 2022
Facebook Reality Lab
Research Scientist Intern
Remote, June 2021
NVIDIA Research
Research Scientist Intern
Remote, June 2020
Tencent America
Research Scientist Intern
Bellevue WA, June 2019


Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks
British Machine Vision Conference (BMVC 2022)
Xin Dong, Hongxu Yin, Jose M. Alvarez, Jan Kautz, Pavlo Molchanov, H.T. Kung
SphereFed: Hyperspherical Federated Learning
European Conference on Computer Vision (ECCV 2022)
Xin Dong, Sai Qian Zhang, Ang Li, H.T. Kung
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
Xin Dong, Barbara De Salvo, Meng Li, Chiao Liu, Zhongnan Qu, H.T. Kung, Ziyun Li
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
Xin Dong, Junfeng Guo, Ang Li, Wei-Te Ting, Cong Liu, H.T. Kung
Training for Multi-resolution Inference Using Reusable Quantization Terms
The 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2021)
Sai Qian Zhang, Bradley McDanel, H.T. Kung, Xin Dong
A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration
International Conference on Machine Learning (ICML 2021)
Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Shi Gu
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)
Yuhang Li, Feng Zhu, Ruihao Gong, Mingzhu Shen, Xin Dong, Fengwei Yu, Shaoqing Lu, Shi Gu
Additive Powers-of-Two Quantization: A Non-uniform Discretization for Neural Networks
International Conference on Learning Representations (ICLR 2020)
Yuhang Li*, Xin Dong*, Wei Wang
(*: equal contribution)
RTN: Reparameterized Ternary Network
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
Yuhang Li*, Xin Dong*, Sai Qian Zhang, HaoliBai, Yuanpeng Chen, Wei Wang
(*: equal contribution)
exBERT: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources
The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)
Wen Tai, H.T. Kung, Xin Dong, Marcus Comiter, Chang-Fu Kuo
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Yuhang Li, Wei Wang, Haoli Bai, Ruihao Gong, Xin Dong, Fengwei Yu
Differentiable Dimension Search for Binary Neural Networks
1st Workshop on Neural Architecture Search at ICLR 2020 (ICLR 2020 Workshops))
Yuhang Li, Ruihao Gong, Fengwei Yu, Xin Dong, Xianglong Liu
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)
Yinghao Xu*, Xin Dong*, Yudian Li, Hao Su
(*: equal contribution)
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)
Shilin Zhu, Xin Dong, Hao Su
image classification
Full-stack Optimization for Accelerating CNNs with FPGA Validation
The ACM International Conference on Supercomputing (ICS 2019)
Bradley McDanel, Sai Qian Zhang, H.T. Kung, Xin Dong
image classification
Maestro: A Memory-on-Logic Architecture for Coordinated Parallel Use of Many Systolic Arrays
The 30th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP 2019)
Bradley McDanel, Sai Qian Zhang, H.T. Kung, Xin Dong
image classification
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Thirty-first Conference on Neural Information Processing Systems (NeurIPS 2017)
Xin Dong, Shangyu Chen, Sinno Jialin Pan

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