Danai Koutra receives 2023 ICDM Tao Li Award
Danai Koutra, associate professor of computer science and engineering at the University of Michigan and Amazon Scholar, has been selected as the 2023 recipient of the Tao Li Award by the IEEE International Conference on Data Mining (ICDM). The award recognizes her significant contributions in the areas of data mining, machine learning, and artificial intelligence.
The ICDM Tao Li Award is named in memory of the life and accomplishments of computer scientist Tao Li, who made substantial and prolific contributions to the field of data mining during his time as a professor at Florida International University and throughout his career. Awarded to just one individual a year, the honor recognizes excellent early-career researchers (within 10 years after PhD) who have demonstrated significant achievements and impact through research, leadership, and service in data mining and related areas.
Koutra is an internationally recognized leader in data mining and machine learning, with her work focusing on the development of principled, interpretable, and scalable methods for discovering and summarizing patterns in large-scale data by leveraging the inherent connections among them. These connections are naturally modeled as graphs (e.g., email communication networks, social networks, brain networks, artificial neural networks). Koutra devises methods for network- and node-level summaries to harness the scale, heterogeneity, and complexity of these data, and derive insight from them.
One of Koutra’s ongoing projects involves developing algorithms for multi-network tasks, such as brain graph classification which can provide a non-invasive neuroimaging biomarker for the identification of certain psychiatric disorders at an early stage. This also includes embedding-based network alignment, which aims to identify the same node / individual across networks. Her earlier work in this space received the 2022 IEEE ICDM Test-of-Time Award. Koutra has also pioneered graph learning in complex settings where graphs exhibit heterophily or disassortativity (“opposites attract”).
Before joining CSE, Koutra earned her Ph.D. and M.S. in computer science at Carnegie Mellon University in 2015, after completing her diploma in electrical and computer engineering at the National Technical University of Athens. She has authored numerous papers in top data mining and AI conferences, including several that have won awards. Her work in data mining and machine learning has earned her several honors, including an NSF CAREER Award, an ARO Young Investigator Award, the 2020 SIGKDD Rising Star Award, and the 2016 ACM SIGKDD Dissertation Award.