Joe (Zhou) Ren - 任洲 Applied Science Manager (CV, Google Scholar, Linkedin)
Amazon AWS, Seattle, WA
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Publish under Zhou Ren
Email: renzhou200622 [at-gmail] [dotcom] Contact me with your CV if you are interested in full-time or doing an internship with us. :)
About me
I am an Applied Science Manager at Amazon AWS, working on Just Walk Out Technology. Previously, I was a Principal Research Manager and founding member of Wormpex AI Research, the AI branch of BianLiFeng (便利蜂), a top-10 convenience store chain in China. I was responsible for building state-of-the-art human-centric AI technologies to facilitate new retail business from new site selection, storefront management, to storefront operation. Before that, I have spent 3 wonderful years at Snap Research as a senior research scientist, working on multimodal understanding to support Snapchat’s content monetization, content security, and creative content creation.
Selected honors: 1. The 1st Prize in ICCV 2021 Low Power Computer Vision Challenge (among 31 teams); 2. Runner-up winner in NIPS 2017 Adversarial Attack and Defense Competition (among 107 teams); 3. “CVPR 2017 Best Student Paper Award” nominee; 4. winner of the “IEEE Trans. on Multimedia 2016 Best Paper Award”; 5. developed the first part-based hand gesture recognition system using Kinect sensor with Nanyang Technological University and Microsoft Research Redmond (Demo1, Demo2, Demo3).
Services
Area Chair of CVPR 2021, CVPR 2022, WACV 2022, WACV 2023.
My research interests lie in the fields of Computer Vision, Multimedia, Machine Learning, and Natural Language Processing. I have worked on Human Centric Understanding (including hand gesture recognition, hand pose estimation, human pose estimation and tracking, human ReID, action detection, etc.), Multi-modal Joint Understanding (including image captioning, video captioning, visual-semantic embedding, etc.), shape understanding, adversarial machine learning, etc.
My current focuses are: 1. human centric understanding (pose, hand, gesture, human Re-ID, and tracking); 2. object detection, action detection and video representation learning, 3. multi-modal joint understanding, vision and language.