I am a third-year Ph.D. student at HAN LAB of MIT EECS, advised by Prof. Song Han. My research interest is systems and machine learning (SysML). During my Ph.D. study, I work with my labmates on designing efficient 3D deep learning primitives (PVConv, NeurIPS’19 spotlight), networks (SPVNAS, ECCV’20 and TPAMI’21), inference libraries (TorchSparse, MLSys’22) and specialized accelerators (PointAcc, MICRO’21). We then apply them in real-world auto-driving applications (BEVFusion, ICRA’23).

I did my master of science in EECS at MIT in 2022. Before that, I graduated with highest honor from the Department of Computer Science and Engineering of Shanghai Jiao Tong University in 2020, where I was fortunately advised by Prof. Hongtao Lu. I was also affiliated with the IEEE Honor Class at SJTU.


Apr 30, 2023 I am going to intern at Waymo Research this summer and work on exciting projects in behavior prediction. See you in Mountain View, CA!
May 28, 2022 Check out our latest research BEVFusion, an efficient and generic multi-task multi-sensor fusion framework for 3D perception. Code has been released at here.
Jan 14, 2022 TorchSparse is accepted to MLSys 2022!
Sep 7, 2021 Two recent journal papers on efficient deep learning and PVNAS are accepted to TODAES and TPAMI!
Jul 15, 2021 Recent papers SemAlign and PointAcc are accepted to IROS 2021 and MICRO 2021!

Recent Publications

  1. BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation Zhijian Liu*, Haotian Tang*, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela Rus, and Song Han ICRA 2023 [Abs] [arXiv] [Website] [Code]
  1. TorchSparse: Efficient Point Cloud Inference Engine Haotian Tang*, Zhijian Liu*, Xiuyu Li*, Yujun Lin, and Song Han MLSys 2022 [Abs] [arXiv] [Website] [PDF] [Code]
  2. Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications Han Cai*, Ji Lin*, Yujun Lin*, Zhijian Liu*, Haotian Tang*, Hanrui Wang*, Ligeng Zhu*, and Song Han ACM Transactions on Design Automation of Electronic Systems (TODAES) 2022 [Abs] [arXiv] [PDF]
  1. PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution Zhijian Liu*, Haotian Tang*, Shengyu Zhao, Kevin Shao, and Song Han IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2021 [Abs] [arXiv] [PDF]
  2. PointAcc: Efficient Point Cloud Accelerator Yujun Lin, Zhekai Zhang, Haotian Tang, Hanrui Wang, and Song Han MICRO 2021 [Abs] [arXiv] [Website]
  3. SemAlign: Annotation-Free Camera-LiDAR Calibration with Semantic Alignment Loss Zhijian Liu*, Haotian Tang*, Sibo Zhu*, and Song Han IROS 2021 [Abs] [Website] [PDF]
  1. Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution Haotian Tang*, Zhijian Liu*, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han ECCV 2020 [Abs] [arXiv] [Website] [Code]
  1. Point-Voxel CNN for Efficient 3D Deep Learning Zhijian Liu*, Haotian Tang*, Yujun Lin, and Song Han NeurIPS (Spotlight) 2019 [Abs] [arXiv] [Website] [Code]


    I regularly serve as a reviewer for ICML (outstanding reviewer, 2022), NeurIPS (top reviewer, 2022), ICLR (highlighted reviewer, 2022), TPAMI, IJCV, CVPR, ICCV.