Mirek Riedewald

PhotoAssociate Professor
Northeastern University

College of Computer and Information Science, 202 West Village H
360 Huntington Avenue
Boston, MA 02115

phone +1-617-373 4766, fax (dept): +1-617-373 5121

2002 Ph.D. (UC Santa Barbara)
2002-2008 Research Associate (Cornell University)
Since 2009 Associate Professor (Northeastern University)


DATA Lab @ Northeastern logoProf. Riedewald is co-founder and co-leader of the DATA Lab @ Northeastern. He currently focuses on the development of novel techniques for large-scale distributed data analysis, data management, and data mining. Our research agenda is driven by collaborations with domain scientists and industry, with the goal to produce results that are publishable in both premier computer science venues as well as those in the application domain.


Current Projects

Scolopax: Making Analysis of Scientific Data Fast and Easy

Scolopax logo

I have been collaborating with scientists from various disciplines since 1999. While specific challenges vary, there is always the same common theme: scientists are collecting and generating an ever rapidly increasing amount of data. In this new world of data-driven science, groundbreaking discoveries depend on the ability to efficiently analyze and process these massive amounts of data. To let scientists do science, not force them to become experts on parallel algorithms, data mining, and databases, we are developing Scolopax. Scolopax is a tool for scientific discovery. It will support a user-friendly interface for declaratively specifying discovery goals. All data processing will then be optimized automatically for fast and efficient execution on multiple processors, relying on novel data management techniques.

Merlin: Interactive Category Identification

Consider a citizen scientist or casual observer who spots an interesting bird. Later at home, she wants to know the species of this bird. Despite availability of excellent bird guides, this often becomes a tedious process. Traditional classification techniques are not effective due to the nature of the problem, including having to deal with wrong and uncertain user inputs. Similar problems occur in many other contexts. We are developing novel interactive category identification techniques whose goal is to minimize user effort. Merlin is part of a major inter-institutional collaboration led by the Cornell Lab of Ornithology. The overall goal is to build a social networking site that connects citizen scientists, bird experts, and ecology researchers. Users can contribute data, explore birds, interact with others to learn more about ecology, and play online "games with a purpose". This system will broaden interest in (citizen) science and contribute to science education. (Recently started. More information coming soon.)

Selected Past Projects

Cayuga: A Scalable System for Data Stream Processing

Additive Groves Prediction Technique and Automatic Interaction Detection

Teaching and Advising