Welcome to VIDA

The Visualization and Data Analytics Research Center at NYU consists of computer scientists who work closely with domain experts to apply the latest advances in computing to problems of critical societal importance, and simultaneously generate hypotheses and methods that new data sources and data types demand.

We work in the areas of Visualization, Imaging and Data Analysis.

VISUALIZATION

Research in graphic design methods as well as the effective use of visualization methods and techniques to explore and interpret high-dimensional data. Our group has worked in urban and sports computing, as well as in the areas of biochemistry, cybersecurity, development, healthcare, climate sciene and human rights.

IMAGING

Development of novel imaging analysis methodologies related to segmentation, shape analysis and image statistics. Our work is multidisciplinary, primarily collaborating with experts in clinical imaging on research in areas including schizophrenia, autism, multiple sclerosis.

DATA ANALYSIS

Creation of novel data management and analysis methods applied to urban, scientfic, web, and public health domains. Our group has particular expertise in provenance and computational reproducibility and personal-data informatics.

NEWS & EVENTS

01/2020 Congratulations Gromit Yeuk-Yin Chan on his paper that has just been accepted (for oral presentation) at WWW 2020: Real-Time Clustering for Large Sparse Online Visitor Data.
01/2020 Congratulations Francis Williams on his paper accepted to CHI 2020. Unwind: Interactive Fish Straightening.
01/2020 Congratulations Fabio Miranda on his paper accepted to CHI 2020Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data
02/2020Congratulations to Masayo Ota and Heiko Mueller on their paper that has just been accepted to VLDB 2020, entitled:
“Data-Driven Domain Discovery for Structured Datasets”.
02/2020 Congratulations to Aline Bessa on her paper “Efficient Discovery of Meaningful Outlier Relationships”¬†which was accepted to the ACM Transactions on Data Science.

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