Dr. Georgina Ifrim’s research focuses on developing scalable predictive models for machine learning and data mining applications. Her current research focuses on the design of efficient and interpretable learning models for sequences (e.g., DNA, time series), and on real-time prediction for streaming data (text mining for news and social media).

Recent publications:

Guyet, T., Ifrim, G., Malinowski, S., Bagnall, A., Shafer, P., Lemaire, V. (2023). Advanced Analytics and Learning on Temporal Data. 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19–23, 2022, Revised Selected Papers

Dhariyal, B., Nguyen, T. L., & Ifrim, G. (2023). Scalable classifier-agnostic channel selection for multivariate time series classification. Data mining and Knowledge Discovery, 37, 1010-1054

Dong, Y., Ifrim, G., Mladenic, D., Saunders, C., Van Hoecke, S. (2021). Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V