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HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Tue, 24 Dec 2024 01:26:49 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"676a0dd9-4222" expires: Mon, 29 Dec 2025 14:35:24 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: FC2E:15317B:8F2767:A09DA3:69528F53 accept-ranges: bytes age: 0 date: Mon, 29 Dec 2025 14:25:24 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210097-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1767018324.475968,VS0,VE222 vary: Accept-Encoding x-fastly-request-id: 33323140318f84e66558a55c9a1e070cfeec88f3 content-length: 4294 Zhiqiang Tang

Zhiqiang Tang

Senior Applied Scientist

Amazon Web Services

zhiqiang DOT tang AT rutgers DOT edu

Github

Google Scholar

About Me

I am a Senior Applied Scientist at Amazon Web Services. I obtained my Ph.D. degree in Computer Science at Rutgers University–New Brunswick, advised by Prof. Dimitris Metaxas.

I am the author and tech lead of AutoGluon Multimodal (AutoMM), an open-source toolkit for multimodal deep learning. AutoMM aims to democratize state-of-the-art foundation models to everyone with three lines of code.

Research Interests

Deep Learning
Multimodal Learning
Computer Vision
Natural Language Processing
Audio Signal Processing

Work Experience

Senior Applied Scientist, AWS AI
SF Bay Area, CA, USA, March 2021 - Present

Applied Scientist Intern, AWS AI
SF Bay Area, CA, USA, June 2020 - Aug 2020

Research Intern, IBM Thomas J. Watson Research Center
Yorktown Heights, NY, USA, June 2019 - Aug 2019

Selected Publications

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data.

Zhiqiang Tang, Zihan Zhong†, Tong He, Gerald Friedland († indicates the intern I mentored)

ArXiv Preprint.

[paper] [code]
Learning to Generate Answers with Citations via Factual Consistency Models.

Rami Aly†, Zhiqiang Tang, Samson Tan, George Karypis († indicates the intern I mentored)

The Annual Meeting of the Association for Computational Linguistics (ACL), 2024.

[paper] [code]
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models.

Zhiqiang Tang, Haoyang Fang, Su Zhou, Taojiannan Yang, Zihan Zhong, Tony Hu, Katrin Kirchhoff, George Karypis

The International Conference on Automated Machine Learning (AutoML), 2024.

[paper] [code]
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model.

Zihan Zhong†, Zhiqiang Tang, Tong He, Haoyang Fang, Chun Yuan († indicates the intern I mentored)

The International Conference on Learning Representations (ICLR), 2024.

[paper] [code]
Learning Multimodal Data Augmentation in Feature Space.

Zichang Liu†, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson († indicates the intern I mentored)

The International Conference on Learning Representations (ICLR), 2023.

[paper] [code]
CrossNorm and SelfNorm for Generalization under Distribution Shifts.

Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas

IEEE International Conference on Computer Vision (ICCV), 2021.

[paper] [code]
OnlineAugment: Online Data Augmentation with Less Domain Knowledge.

Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogerio Feris, and Dimitris Metaxas

The 16th European Conference on Computer Vision (ECCV), 2020.

[paper] [code]
AdaTransform: Adaptive Data Transformation.

Zhiqiang Tang, Xi Peng , Tingfeng Li, Yizhe Zhu, and Dimitris Metaxas

IEEE International Conference on Computer Vision (ICCV), 2019. (Oral)

[paper]
Towards Efficient U-Nets: A Coupled and Quantized Approach.

Zhiqiang Tang, Xi Peng , Kang Li, and Dimitris Metaxas

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019.

[paper] [code]
Quantized Densely Connected U-Nets for Efficient Landmark Localization.

Zhiqiang Tang, Xi Peng , Shijie Geng, Shaoting Zhang, and Dimitris Metaxas.

The 15th European Conference on Computer Vision (ECCV), 2018.

[paper] [code]
CU-Net: Coupled U-Nets.

Zhiqiang Tang, Xi Peng, Shijie Geng, Yizhe Zhu and Dimitris Metaxas.

British Machine Vision Conference (BMVC), 2018. (Oral)

[paper] [code]
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation.

Xi Peng*, Zhiqiang Tang*, Fei Yang, Rogerio S Feris, and Dimitris Metaxas (* indicates equal contribution)

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[paper] [code]
A Coupled Hidden Conditional Random Field Model for Simultaneous Face Clustering and Naming in Videos.

Yifan Zhang, Zhiqiang Tang, Baoyuan Wu, Hanqing Lu, and Qiang Ji

IEEE Transactions on Image Processing (TIP), 2016.

[paper]
Face Clustering in Videos with Proportion Prior.

Zhiqiang Tang, Yifan Zhang, and Hanqing Lu

The 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. (Long talk)

[paper]

Academic Service

Reviewer of Neural Information Processing Systems (NeurIPS) 2019 - Present
Reviewer of International Conference on Learning Representations (ICLR) 2020 - Present
Reviewer of European Conference on Computer Vision (ECCV) 2018 - Present
Reviewer of IEEE International Conference on Computer Vision (ICCV) 2019 - Present
Reviewer of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 - Present
Reviewer of IEEE Transactions on Image Processing
Reviewer of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

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