Prof. Yi Fang received his Ph.D. from Purdue University with research focus on computer graphics and vision. Upon one year industry experience as a research intern in Siemens in Princeton, New Jersey and a senior research scientist in Riverain Technologies in Dayton, Ohio, and a half-year academic experience as a senior staff scientist at Department of Electrical Engineering and Computer science, Vanderbilt University, Nashville, he joined NYU Abu Dhabi as an Assistant Professor of Electrical and Computer Engineering as well as cross appointed in NYU Tandon School of Engineering. He is currently working on the development of state-of-the-art techniques in large-scale visual computing, deep visual learning, deep cross-domain and cross-modality model, and their applications in engineering, social science, medicine and biology.
Prof. Wong received his Ph.D. degree in Electrical Engineering from Purdue University. He is currently an associate professor and the director of MS CS program in the Department of Computer Science and Engineering at the NYU Polytechnic School of Engineering. His research interests lie in the general areas of computer vision, pattern recognition, and machine learning. His current research focus is on developing novel machine-learning-based techniques for video surveillance applications. He had previously worked on funded projects in document image analysis and security, video scene segmentation and classification, fingerprint verification, morphological image processing, infrared target classification, three-dimensional object recognition, pavement image analysis, and optical character recognition, among others. He had published extensively in image processing and multimedia conferences and journals. Dr. Wong is currently an associate editor for two international journals in multimedia and security, and he had served on the organizing committee and technical program committee of several major IEEE and ACM technical conferences in image processing and multimedia.
Multimedia and Visual Computing Lab at New York University is seeking for talented, self-motivated individuals to work in the group and perform world-class research.
Coupled with New York University’s mission to become a fully connected global network university, faculty from NYU New York and Abu Dhabi created NYU Multimedia and Visual Computing Lab as an intellectual hub for faculty, researchers and students from both New York and Abu Dhabi campuses, who come together to study and address the key challenges in multimedia and visual data processing. With the advancement in data acquisition techniques, we have observed an exponential increase of visual data that present in different domains and modalities, such as, 2D images, 2D videos, 2D sketches, 2.5D depth images, 3D point cloud, 3D meshed surface and so on. We are therefore faced with an ever-increasing demand for approaches towards automatic visual data processing, understanding and analysis. Visual data are often featured with high complexity, subject to large structural variations, intrinsic imprecision and ambiguity, and exhibit heavy noise and incompleteness. For instance, a car built by different manufactures is likely to be significantly distinct in 3D shape representation; a building viewed from different view angles is likely to be distinct in 2D view representation; and a horse sketched by different individuals with experiential and cognitive difference is likely to be distinct in sketch representation. Our research lab aims to develop a unified framework based on the state-of-the-art techniques in big-data and deep learning to address the aforementioned challenges, specifically, multitude ongoing research threads in the lab are as follows:
- 3D computer vision: the development of novel techniques that handles challenging research problems in 3D object detection, classification and registration.
- Large-scale visual computing: the development of novel techniques for addressing challenging research problems caused by the exponential growth of visual data in the era of “Big Data”.
- Deep visual computing: the development of novel techniques for deeply learning and discovering the hidden visual pattern for visual object recognition using the state-of-the-art deep learning techniques.
- Deep cross-domain model: The development of novel techniques for deeply mining the intrinsic relationship among loosely related data across different domains such 2D images and 3D shape.
- Deep cross-modality model: The development of novel techniques for deeply exploring the affinity from diverged relations among data present in different modalities such 3D shapes and semantic text-based description.
- 3D scene understanding: The development of novel techniques for scene segmentation, detection, tracking and semantic scene labeling for RGB-D data.
- 3D Computational Structural Biology: The development of new methods to address challenging issues caused by the significant conformational structural flexibility of biological molecules.