Dr. Zheng (Thomas) Tang

Dr. Zheng (Thomas) Tang

Senior Deep Learning Engineer at NVIDIA, expert in computer vision and machine learning

Bio

Dr. Zheng (Thomas) Tang is currently a Senior Deep Learning Engineer on the Metropolis team at NVIDIA (2021–present). He was previously an Applied Scientist on the Amazon One team at Amazon (2019–2021). He earned his Ph.D. in Electrical & Computer Engineering (ECE) from the University of Washington (UW) in 2019.

His research interests span intelligent transportation systems, multi-/single-camera object tracking, re-identification, pose estimation, action recognition, camera calibration, synthetic data generation, biometric identification, and related areas in computer vision and machine learning. He holds 9 filed U.S. patents and has published 25 peer-reviewed papers in top journals and conferences. Dr. Tang is the Tech Lead of the Metropolis Multi-Camera Tracking AI Workflow, integrated into the Mega Omniverse Blueprint and showcased during CES’25 and GTC’25 keynotes by NVIDIA CEO Jensen Huang.

He currently serves as a Senior Area Editor (SAE) (2025–present) for the IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT) and as an Organizing Committee Lead (2020–present) for the AI City Challenges at CVPR. He is also an Area Chair for ACM MM 2024 and MLSP 2021. As a reviewer, Dr. Tang regularly serves top-tier journals and conferences including IJCV, T-PAMI, T-IP, T-MM, T-CSVT, T-ITS, NeurIPS, CVPR, ICCV, ICASSP, and ICME. He received the T-CSVT Best Associate Editor Award in 2021.

He led the winning team in Track 1 (Traffic Flow Analysis) and Track 3 (Multi-Camera Vehicle Detection and Re-Identification) at the 2nd AI City Challenge Workshop at CVPR 2018. His research was also recognized as a finalist for two Best Student Paper Awards at ICPR 2016.

News

  • [Mar. 2025] Metropolis spatial AI agents, integrated into the Mega Omniverse Blueprint, featured in CES 2025 & NVIDIA GTC 2025 Keynotes by CEO Jensen Huang
  • [Mar. 2025] Released MTMC (multi-target multi-camera) tracking datasets for AI City Challenge 2024 & 2025 as part of the Open Physical AI Dataset at NVIDIA GTC 2025
  • [Jan. 2025] Promoted to Senior Area Editor (SAE) of IEEE T-CSVT
  • [Oct. 2024] Served as Area Chair for ACM Multimedia 2024
  • [Jul. 2024] Paper titled "UAV First-Person Viewers Are Radiance Field Learners" accepted to ECCV 2024
  • [Jun. 2024] Creation of the largest indoor synthetic dataset to support the 8th AI City Challenge at CVPR 2024 featured in an official NVIDIA blog
  • [Mar. 2024] Fusing Real-Time AI With Digital Twins demo highlighted in NVIDIA GTC 2024 Keynote by CEO Jensen Huang
  • [Mar. 2023] Released NVIDIA Metropolis Microservices 1.0, including the Multi-Camera Tracking app I developed and trained using synthetic Omniverse data
  • [Jan. 2023] Released the 7th AI City Challenge Workshop in conjunction with CVPR 2023
  • [Dec. 2022] Released NVIDIA TAO Toolkit 4.0, where I developed re-identification and pose-based action recognition networks, and end-to-end video analytics pipelines
  • [Oct. 2022] Special issue "Label-Efficient Learning on Video Data" accepted to T-CSVT
  • [Feb. 2022] Received the 2021 Best Associate Editor Award from IEEE T-CSVT
  • [Jan. 2022] Released the 6th AI City Challenge Workshop in conjunction with CVPR 2022
  • [Oct. 2021] Served as Area Chair for MLSP 2021
  • [May 2021] Joined NVIDIA Metropolis team as a Senior Deep Learning Engineer
  • [Jan. 2021] Released the 5th AI City Challenge Workshop in conjunction with CVPR 2021
  • [Dec. 2020] Appointed Associate Editor for IEEE T-CSVT (Impact Factor: 5.859)
  • [Sep. 2020] Amazon One launched; contributed as a research scientist and filed two U.S. patents
  • [May 2020] Source code for “PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data” (ICCV 2019) released on GitHub
  • [Jan. 2020] Released the 4th AI City Challenge Workshop in conjunction with CVPR 2020
  • [Jul. 2019] “PAMTRI” paper accepted to ICCV 2019
  • [Jun. 2019] Earned Ph.D. in Electrical & Computer Engineering from University of Washington
  • [Feb. 2019] “CityFlow” benchmark paper accepted to CVPR 2019 (Oral)
  • [Jun. 2018] Led the #1 winning team in Track 1: Traffic Flow Analysis (Demo) and Track 3: Multi-Camera Vehicle Detection and Re-ID (Demo) at the 2nd AI City Challenge Workshop, CVPR 2018
  • [Jun. 2018] “Joint Multi-View People Tracking and Pose Estimation for 3D Scene Reconstruction” accepted to ICME 2018 (Oral)
  • [Aug. 2017] Winner of Track 2: AI City Applications (Demo) at the 1st AI City Challenge Workshop, SmartWorld 2017
  • [May 2017] “Online-Learning-Based Human Tracking Across Non-Overlapping Cameras” accepted to T-CSVT
  • [Dec. 2016] Finalist for two Best Student Paper Awards at ICPR 2016 for “Camera Self-Calibration from Tracking of Moving Persons”
  • [Dec. 2015] “Multiple-Kernel Adaptive Segmentation and Tracking (MAST)” accepted to ICASSP 2016 (Oral)
  • [Jun. 2014] Graduated with First Class Honours from the BUPT-QMUL Joint Programme (B.Sc. in Engineering)