We are the Graph Exploration and Mining at Scale (GEMS) lab at the University of Michigan, founded 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 indispensable. 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, the National Science Foundation (NSF), and Trove.
If you’re interested in joining our group, send an email with your interests and CV to gemslab-opportunities@umich.edu.
June 2024
Jiong graduated! Congratulations Dr. Zhu!
June 2024
Puja passed her thesis proposal!
May 2024
One paper accepted to ICML! (Graph Generation)
January 2024
One paper accepted to ICLR! (GNN-based Uncertainty)
December 2023
One paper accepted to ICASSP! (Link Prediction Uncertainty)
December 2023
Yujun graduated! Congratulations Dr. Yan!
June 2023
Caleb graduated! Congratulations Dr. Belth!
January 2023
One paper accepted to ICLR as a Spotlight (top 25%)! (Model Adaptation)
December 2023
One paper accepted to ICASSP! (Generalization gap prediction)
November 2023
One paper accepted at Learning on Graphs! (performance discrepancies in GNNs; oral presentation, top 5% of long papers)
May 2023
Donald receives an NSF Graduate Research Fellowship! Congratulations!
September 2023
One paper accepted to NeurIPS! (Data-centric Graph CL)
November 2022
Alumna Tara received the Kuck Dissertation Prize for her dissertation!
September 2022
Donald passes his prelim! Congratulations!
April 2022
Tara graduated! Congratulations Dr. Safavi!