Bio Georgiana Ifrim

Dr. Georgiana Ifrim

Funded Investigator

Machine Learning & Statistics

Dr. Georgiana Ifrim is an Associate Professor at the School of Computer Science, University College Dublin, Co-Lead of the SFI Centre for Research Training in Machine Learning (ML-Labs) and SFI Funded Investigator with the Insight Centre for Data Analytics and the VistaMilk SFI Research Centre. She is Director of Graduate Research at the School of Computer Science, UCD. Prior to this position, she held research fellow and postdoctoral positions with the Insight Centre for Data Analytics, University College Dublin, Ireland, Cork Constraint Computation Centre (4C), University College Cork, Ireland, and Bioinformatics Research Centre (BiRC), Aarhus University, Denmark. She holds a PhD and MSc from Max-Planck Institute for Informatics, Germany, and a BSc from University of Bucharest, Romania. Dr. Ifrim’s research focuses on developing scalable predictive models for machine learning and data mining applications. She has developed new methods for sequence learning, time series classification, text mining and real-time prediction for news and social streams. Dr. Ifrim has worked in application domains ranging from Web mining, news and social media, energy, biology and sports science. 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)


Research Projects

  • Sequence Learning (Classification and Regression for Symbolic Sequences & Time Series)
    • Sequence learning with all-subsequences (ECMLPKDD17, ICDE17, PlosOne14, KDD11, KDD08)
    • Product review rating using opinion mining (COLING10)​
  • Learning from Massive News and Social Streams
    • Connecting news and Twitter streams (TKDE17, WWW16, ECML16, ECML14)
    • Twitter event detection (Winner of SNOW@WWW14 Data Challenge)
  • Energy-Efficiency Applications
    • Energy price forecasting for cost-aware scheduling design (SUSCOM14, CP13, Opt4Smartcities@CP13, CP12)
  • Extracting and Exploiting Knowledge Graphs (aka ontologies)
      • WordNet/Yago (knowledge graphs), Naga (graph search) (ICDE08, PKDD06, KDD06)

Open Source Software

  • Twitter-Topics: Twitter Event Detection (winner of SNOW@WWW14 Data Challenge, twitter-topics)
  • SLR: Structured Logistic Regression (KDD08, please use SEQL, it extends SLR and is better maintained). Fast Logistic Regression for Text Categorization: learning phrase-classifiers (ngram-classifiers) with variable-length phrases (ngrams).

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