We are the Graph Mining and Exploration at Scale (GEMS) lab at the University of Michigan, started in 2015 and led by Danai Koutra. Our team researches important data mining and machine learning problems involving interconnected data: in other words, graphs or networks.
From airline flights to traffic routing to neuronal interactions in the brain, graphs are ubiquitous in the real world. Their properties and complexities have long been studied in fields ranging from mathematics to the social sciences. However, many pressing problems involving graph data are still open. One well-known problem is scalability. With continual advances in data generation and storage capabilities, the size of graph datasets has dramatically increased, making scalable graph methods indispensible. Another is the changing nature of data. Real graphs are almost always dynamic, evolving over time. Finally, many important problems in the social and biological sciences involve analyzing not one but multiple networks.
The problems described above call for principled, practical, and highly scalable graph mining methods, both theoretical and application-oriented. As such, our work connects to fields like linear algebra, distributed systems, deep learning, and even neuroscience. Some of our ongoing projects include:
We’re grateful for funding from Adobe, Amazon, the Army Research Lab, the Michigan Institute for Data Science (MIDAS), Microsoft Azure, NSF, and Trove.
If you’re interested in joining our group, send an email with your interests and CV to email@example.com.
Welcome new PhDs!
1 paper accepted to CIKM!
1 paper accepted to KDD
Grant for music + big data
Danai awarded Adobe Digital Experience Research award
Tara and Marlena awarded NSF Graduate Research Fellowships
Survey accepted at ACM Computing Surveys
Paper on graph summarization accepted at SNAM
1 paper accepted at PAKDD
1 paper accepted at SDM
ICDM 2017 best paper nomination
Danai has a book!
Danai awarded an NSF EAGER grant
Danai awarded a Trove grant