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!
Our College is growing with more than four open positions in all areas including data management and data science, at all levels (assistant, associate, or full). For Faculty positions see College ads.
We are actively looking for new PhD students with strong background in data management, algorithms, theory, or systems. 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.
Recent or Upcoming Courses
Spring 2020: cs7240: Principled of scalable data management: theory, algorithms, and database systems
Spring 2018/Fall 2017: cs6240: Parallel Data Processing in MapReduce
Spring 2018/Fall 2017: cs3200: Database design
- [April 2020] VLDB 2020 paper on optimal ranked enumeration accepted.
[March 2020] Five papers accepted to SIGMOD/PODS 2020. One on organizing data lakes for navigation, one on near-optimal band-joins, and one on query understanding through diagrammatic diagrams. In addition to our any-k tutorial, Miller will be leading a SIGMOD keynote session “Toward Exploring, Understanding, and Searching a Billion Data Sets” with colleagues Natasha Noy (Google) and Awez Syed (Informatica). Congratulations everyone!
- [Feb. 2020] SIGMOD 2020 tutorial on tutorial on worst-case optimal joins meet top-k accepted.
- [Jan 2020] We will present our work on algebraic amplification and a few posters at NEDB Day’20
- [Jan. 2020] Miller to give Keynote at SIGMOD aiDM’20 Workshop on research challenges in data lake management.
- [Dec. 2019] SIGMOD 2020 paper on factorized graph representations for semi-supervised learning from sparse data accepted.
- [Nov. 2019] PODS 2020 with new complexity results of resilience accepted.
- [Nov. 2019] Gatterbauer to give keynote at SUM on algebraic approximations for weighted model counting.
- [Apr. 2019] Come to our VLDB 2019 Tutorial on Data Lakes!
- [Apr. 2019] VLDB 2019 demonstration on image search using low-resolution traffic cameras accepted.
- [Mar. 2019] SIGMOD 2019 paper on anytime approximations for probabilistic inference accepted.
- [Jan. 2019] The DATA Lab will attend NEDB Day’19 at MIT and present a talk and multiple posters.
- [Jan. 2019] Welcome to our new post doc Laura! Sorry for the cold weather 🙁