Bio:
Huy Vo is an Assistant Professor of Computer Science at the City College of New York and a member of the doctoral faculty at the Graduate Center, City University of New York. He is also a faculty member at the Center for Urban Science and Progress, New York University. He currently leads the Big Data Interaction (BDI) Lab at NYU CUSP and CUNY CCNY. His research focuses on high performance systems for interactive visualization and analysis of big data sets, specifically in urban applications.

3-6 Research Areas/Keywords:
Interactive Visualization, High-Performance Computing, Big Data Analytics, Scalable Displays

Personal Webpage:
serv.cusp.nyu.edu/~hvo

Google Scholar Profile URL:
https://scholar.google.com/citations?user=HNwwWQgAAAAJ

Research Domain:
Transportation, Scientific Computing

Do you lead any research groups?
Yes.

Research Group Name:
Big Data Interaction (BDI) Lab

Research Group Description:
Our research focuses on developing efficient algorithms and interactive systems that enable massive data analysis and visualization at scale. Data-driven exploration is a complex, time-consuming process that often requires techniques from multiple disciplines including statistics, machine learning, data management, and visualization. Tools must be built in such a way that they are usable and within reach to domain experts who often lack computer science expertise. Visual analytics, which couples interactive visual representations with analytical processes, aims to tackle this problem in particular. For big data, the problem is even more challenging due to the fact that data manipulation and analysis techniques must scale to large and complex data sets. To address this problem, novel techniques must be developed and integrated from both areas of big data management and big data visualization. However, such integration is rather limited in current systems. In fact, there is a gap between big data management and how they may be used to empower visual analytic applications. Bridging this gap and providing an end-to-end solution to data-driven exploration is a main focus of our lab. In achieving this goal, we often align our research with practical applications through collaborations with domain experts. We believe this is key to tackle challenges in data science and to solve real-world problems.
3-6 Research Areas/Keywords:
Interactive Visualization, High Performance Computing, Big Data Analytics, Scalable Displays, Spatio-Temporal Data ManagementResearch Domain:
Transportation, Scientific ComputingResearch Group Members:
Yuan Chunyu, Xueqi Huang, Denis Khryashchev, Julia Lau, Motahare Mounesan, Ayman ZeidanGroup Webpage URL:
bdilab.github.io