His research interests lie at the intersection of computer systems and machine learning, specifically in the area of full-stack efficient 3D deep learning for autonomous driving. Haotian has authored multiple papers with over 1,300 citations and over 4,200 GitHub stars in this field. Haotian has successfully advised several undergraduate students, and the intern students he mentored have continued as PhD students at MIT and UC Berkeley.
Haotian Tang is a fourth-year Ph.D. student at HAN LAB of MIT EECS, advised by Prof. Song Han. His research interest is systems and machine learning (SysML). During his Ph.D. study, he publishes across the entire stack of efficient deep learning, from algorithm to system and hardware. He works with his labmates on designing efficient 3D deep learning primitives (PVConv, NeurIPS’19 spotlight), networks (SPVNAS, ECCV’20 and TPAMI’21), inference libraries (TorchSparse, MLSys’22; TorchSparse++, MICRO'23) and specialized accelerators (PointAcc, MICRO’21). He then applies them in real-world auto-driving applications (BEVFusion, ICRA’23). He received outstanding reviewer awards at ICLR, ICML and NeurIPS. He was a research scientist intern at Waymo Research (2023) and OmniML Inc (2022, now part of NVIDIA).