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 VLM benchmarking, multimodal embedding search, intelligent transportation systems, multi-/single-camera object tracking, re-identification, camera calibration, synthetic data generation, and related areas in computer vision and machine learning. He holds 15 filed U.S. patents and has published 23 peer-reviewed papers in top journals and conferences. At NVIDIA, Dr. Tang developed multimodal fusion search for the NVIDIA VSS Blueprint, featured in the GTC’26 booth demo for video analytics AI agents, and prepared VANTAGE-Bench and TAR benchmarks for physical AI VLM evaluation, highlighted in the Cosmos 3 release at Computex’26.

He currently serves as an Organizing Committee Lead (2020-present) for the AI City Challenges at CVPR, ICCV, and ECCV, and as a Senior Area Editor (SAE) (2025-present) for the IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT). He is also an Area Chair for ACM Multimedia (ACM MM). 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.

Dr. Tang was an invited keynote speaker at the UrbanAI Workshop at NeurIPS 2025. He received the Best Presentation and Best Poster Awards at NTECH 2024 (NVIDIA’s internal technical conference). 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 Best Scientific Paper Award and Best Student Paper Award at ICPR 2016.

News

[Jun. 2026] Supported VANTAGE-Bench and TAR benchmark efforts highlighted with NVIDIA Cosmos 3 at Computex 2026, including VANTAGE-Bench preparation as a NeurIPS 2026 competition effort and TAR launch alignment for AI City Challenge 2026 Track 3 at ECCV 2026
[Apr. 2026] The 10th AI City Challenge was accepted as a half-day workshop at ECCV 2026 in Malmo, advancing Sim2Real transfer, unified reasoning, and smart-city video AI across six tracks
[Mar. 2026] Supported the GTC 2026 VSS booth demo for video analytics AI agents, contributing to video embedding search, search-model preparation, demo evaluation data, and VSS 3.1.0 search components
[Dec. 2025] Presented NVIDIA Metropolis multi-camera 3D perception and spatial AI research at the UrbanAI Workshop at NeurIPS 2025, covering AI City Challenge progress, cloud-native tracking workflows, MCBLT, and Sparse4D
[Oct. 2025] Vision AI agents for Foxconn factory digital twins showcased in NVIDIA GTC DC 2025 Keynote, and hosted the 9th AI City Challenge Workshop at ICCV 2025 in Honolulu with 245 teams worldwide
[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] Won Best Presentation and Best Poster Awards in the Imaging and Computer Vision Track at NTECH 2024 for BEV-SUSHI and MTMC-to-RTLS work
[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)