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HTTP/2 301 server: GitHub.com content-type: text/html location: https://www.linji.me/ x-github-request-id: 1940:36A0B4:7BFDD5:8AF5D2:6951444D accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 14:53:01 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210047-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766933582.610806,VS0,VE194 vary: Accept-Encoding x-fastly-request-id: b339eea152b62029441391ec3dc338818cb7cbfe content-length: 162 HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Mon, 23 Jun 2025 07:25:35 GMT access-control-allow-origin: * etag: W/"6859016f-e668" expires: Sun, 28 Dec 2025 15:03:02 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: EDFA:15317B:7BDCA1:8AD4B2:6951444D accept-ranges: bytes date: Sun, 28 Dec 2025 14:53:02 GMT via: 1.1 varnish age: 0 x-served-by: cache-bom-vanm7210090-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766933582.906300,VS0,VE200 vary: Accept-Encoding x-fastly-request-id: 7b83edffd6f9e57e20ab953023eecbaa24eeb354 content-length: 9589 Ji Lin's Homepage

Ji Lin

Contact:

jilin.eecs AT gmail

I am a research scientist at OpenAI, working on multimodal, reasoning, and synthetic data. I contributed to o3/o4-mini, GPT-4o, GPT-4.1, GPT-4.5, Operator, 4o imagegen, etc.

💡 Read our latest blog post on visual reasoning: Thinking with images!

Previously, I completed my PhD at MIT EECS advised by Prof. Song Han. Before that, I received my B.Eng. in Electronic Engineering from Tsinghua University, and M.Sc. in EECS from MIT. I've interned/worked at Adobe Research, OmniML, and NVIDIA Research.

Publications [Full List]

* indicates equal contribution

VILA: On Pre-training for Visual Language Models

Ji Lin*, Hongxu Yin*, Wei Ping, Yao Lu, Pavlo Molchanov, Andrew Tao, Huizi Mao, Jan Kautz, Mohammad Shoeybi, Song Han,

CVPR 2024 / arXiv / Code

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Ji Lin*, Jiaming Tang*, Haotian Tang, Shang Yang, Xingyu Dang, Song Han,

MLSys 2024 / arXiv / Code / TinyChat🔥

Integration: NVIDIA TRT-LLM / Intel Neural Compressor / vLLM / FastChat / HuggingFace TGI / LMDeploy / FriendliAI

Best Paper Award

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Guangxuan Xiao*, Ji Lin*, Mickael Seznec, Julien Demouth, Song Han,

ICML 2023 / arXiv / Code / Faster Transformer Integration / Intel Neural Compressor Integration

PockEngine: Sparse and Efficient Fine-tuning in a Pocket

Ligeng Zhu, Lanxiang Hu, Ji Lin, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, Song Han,

MICRO-56 / arXiv

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu

NeurIPS 2022 / arXiv / Project Page / Code / Video

On-Device Training Under 256KB Memory

Ji Lin*, Ligeng Zhu*, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, Song Han

NeurIPS 2022 / arXiv / Project Page

Press: MIT News (homepage spotlight)

Network Augmentation for Tiny Deep Learning

Han Cai, Chuang Gan, Ji Lin, Song Han

NeurIPS 2021 / arXiv / Code

MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han

NeurIPS 2021 / arXiv / Project Page

Press: MIT News / TechTalks / TheNextWeb / AIM / TechMonitor

Anycost GANs for Interactive Image Synthesis and Editing

Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zhu

CVPR 2021 / arXiv / Project Page / Code / Video / Demo Video / Colab Tutorial

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

Press: MIT News (homepage spotlight) / WIRED / MIT TR-China / IBM / Morning Brew / Stacey on IoT / Analytics Insight / Techable / Tendencias

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

CVPR 2020 / arXiv / Project Page / Code / Video / Demo Video / Slides

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

CVPR 2020 / arXiv

AutoML for Architecting Efficient and Specialized Neural Networks

Han Cai*, Ji Lin*, Yujun Lin*, Zhijian Liu*, Kuan Wang*, Tianzhe Wang*, Ligeng Zhu*, Song Han

IEEE Micro

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

NeurIPS 2019 Workshop / arXiv
Project Page / Demo / Code

Press: MIT News / MIT Technology Review / WIRED / Engadget/ NVIDIA News / Industry Integration@NVIDIA

HAQ: Hardware-Aware Automated Quantization

Kuan Wang*, Zhijian Liu*, Yujun Lin*, Ji Lin, Song Han

CVPR 2019 / arXiv

Hardware-Centric AutoML for Mixed-Precision Quantization

Kuan Wang*, Zhijian Liu*, Yujun Lin*, Ji Lin, Song Han

IJCV
Project Page / Code

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Yihui He*, Ji Lin*, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han

ECCV 2018 / arXiv / Project Page / Poster / Code

Runtime Neural Pruning

Ji Lin*, Yongming Rao*, Jiwen Lu, Jie Zhou

NIPS 2017

Runtime Network Routing for Efficient Image Classification

Yongming Rao, Jiwen Lu, Ji Lin, Jie Zhou

T-PAMI

Academic Service

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

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