Welcome to CCVCL
The City College Visual Computing Research Laboratory (CCVCL) is directed by Professor Zhigang Zhu in the Computer Science Department at CCNY. It serves as an experimental environment for both research and education in advanced visual and other media computing. The research activities in the CCVCL primarily focus on the understanding of 3D natural scenes and the events in the scenes from multiple sensor modalities, including visible cameras, thermal sensors, and acoustic sensors. Currently, there are two main research themes in the Lab. The first aspect of the research is three-dimensional scene modeling and rendering from images and videos. The second aspect of the research is human and other subject tracking and human signature extraction from multiple cameras and multimodal sensors. Potential applications of the advanced visual (and other media) computing range across assistive technology, Human-Computer Interaction (HCI), virtual reality, robot/human navigation, aerial/ground surveillance, content-based video coding, surveillance, security and transportation.
News and Announcements
[May 2021 – Now]. AI4IA: Artificial Intelligence for Intelligence Analysis Colloquium Series, part of the IC CAE Center for Critical Intelligence Studies at Rutgers University.
January 17, 2022. Lab Member Xingye (Alex) Li, a master student in the Data Science and Engineering Program at the City College New York, joined Lyft as a Software Engineer / Machine Learning Engineer. Congratulations!
January 12,2022. Our SAT-Hub work is featured in The RICC – Research and Innovation at City College, Vol II, Issue 1, January ’22.
November 09, 2021. Lab Member Steven Alsheimer, a master student in the Data Science Program at the Graduate Center, will be a Data Scientist with Canon, starting by the end of November. Congratulations!
June 03, 2021. Professor Zhigang Zhu and DSE master student Jin Chen were featured on Futurum, a magazine and online platform aimed at inspiring young people to follow a career in the sciences, research and technology.
May 13, 2021. Mr. Xingye (Alex) Li. SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data. Master Thesis, Data Science and Engineering, The City College of New York. Congratulations!
May 1, 2021. AFOSR STTR Phase I (subaward via Intelligent Fusion Technology, Inc.), Robust Multi-View Target Attitude Determination Using Models, Multi-Cues and Machine Learning. Duration: 05/01/2021-04/31/2022
January 13, 2021. AFOSR Grant (Award #FA9550-21-1-0082) on Dynamic data driven applications systems with multimodal sensing, collaborative perception and deep computing [CUNY Newswire] [CCNY News] [GSOE LinkedIn] [CCNY Tweet]