Ji Lin



jilin AT mit.edu

I am currently a third-year Ph.D. student at MIT EECS, advised by Prof. Song Han. My research interests lie in efficient deep learning algorithms and systems.

I received my B.Eng. in Electronic Engineering from Tsinghua University, and M.Sc. in EECS from MIT. I interned at Adobe Research with Jun-Yan Zhu and Richard Zhang during Summer 2020, and worked at OmniML during summer 2021.


Publications [Full List]

* indicates equal contribution

MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han
Anycost GANs for Interactive Image Synthesis and Editing
Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zhu
MCUNet: Tiny Deep Learning on IoT Devices
Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han
NeurIPS 2020 / arXiv / Project Page / Code / Demo Video
Spotlight Presentation
Differentiable Augmentation for Data-Efficient GAN Training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han
NeurIPS 2020 / arXiv / Project Page / Code / Slides / Colab Tutorial
Press: VentureBeat
GAN Compression: Efficient Architectures for Interactive Conditional GANs
Muyang Li, Ji Lin, Yaoyao Ding, Zhijian Liu, Jun-Yan Zhu, Song Han
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, Song Han
AutoML for Architecting Efficient and Specialized Neural Networks
Han Cai*, Ji Lin*, Yujun Lin*, Zhijian Liu*, Kuan Wang*, Tianzhe Wang*, Ligeng Zhu*, Song Han
TSM: Temporal Shift Module for Efficient Video Understanding
Ji Lin, Chuang Gan, Song Han
ICCV 2019 / arXiv
Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
Ji Lin, Chuang Gan, Song Han
HAQ: Hardware-Aware Automated Quantization
Kuan Wang*, Zhijian Liu*, Yujun Lin*, Ji Lin, Song Han
Oral Presentation
Hardware-Centric AutoML for Mixed-Precision Quantization
Kuan Wang*, Zhijian Liu*, Yujun Lin*, Ji Lin, Song Han
Defensive Quantization: When Efficiency Meets Robustness
Ji Lin, Chuang Gan, Song Han
ICLR 2019 / arXiv / MIT News
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Runtime Neural Pruning
Runtime Network Routing for Efficient Image Classification


Academic Service

  • Conference reviewer: ICLR, ICML, NeurIPS, CVPR, ICCV, ECCV, SIGGRAPH, IJCAI, AAAI, ACMMM, etc.
  • Journel reviewer: T-PAMI, JMLR, T-MM, etc.