Data lab @ Northeastern

The Data Lab @ Northeastern University is a team of faculty and students who explore a range of research problems in scalable data management and analysis. Our work ranges from fundamental questions on the complexity of data management problems to practical applications with domain scientists and covers areas such as large-scale and parallel data analysis algorithms, graph data management, and uncertain data. We participate in a number of interdisciplinary research projects and collaborate with other faculty at Northeastern and database groups across the world. And we are growing …

Open Positions

We are actively looking for several new PhD students with strong background in algorithms, theory, or systems for Fall 2018. For details, please see our page on research opportunities.

Collaborations with Sciences and Industry

For more than 15 years, Prof. Mirek Riedewald has been collaborating with scientists from various domains. This includes summarization techniques for digital libraries, data mining and exploratory analysis in collaboration with the Cornell Lab of Ornithology, speeding up of high-dimensional simulations (for combustions), data and provenance management for astronomy and high-energy physics, and reconstruction, tracing, and connection analysis of massive collections of high-resolution brain images. We also developed new technology for pattern analysis with industrial partners.

If your research team or company has reached a point where data management and analysis has become a bottleneck, please contact us. We are excited to learn about real-world applications that will lead to opportunities for novel research, joint proposals for funding, or consulting. Example areas include Scientific applications, graph analysis, medical data, cloud computing.

Current Courses

CS 6240: Parallel Data Processing in MapReduce

CS 7290: Special topics: Foundations in Scalable Data Management


[12/22/2017] Congratulations to Xiaofeng for her upcoming WWW 2018 paper!
[11/27/2017] Congratulations to Rundong and Xinyan for their upcoming SIGMOD 2018 paper!
[10/11/2017] Our new Data lab website is online