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Dr Giannis Haralabopoulos

Lecturer in Data Analytics

Giannis Haralabopoulos

Specialisms

  • Data Analytics, 
  • Natural Language Processing, 
  • Machine Learning

Location

Edith Morley building, Whiteknights Campus

Assistant Professor Giannis Haralabopoulos is an expert in Machine Learning, Natural Language Processing and Data Science. He is currently working as a Lecturer in Data Analytics in the BISA department in Henley Business School.

Giannis is currently conducting research in natural language processing and machine learning, within a privacy-preserving and ethical framework. His past research contributions include: crowd sourcing methods with subjective evaluation and privacy awareness, demographic analysis for NLP/ML tasks, development of novel multi-label Deep Learning ensembles, NLP applications and novel Deep Learning approaches.

In his previous posts he has collaborated with scientists from law, sociology, medicine and other disciplines in challenging interdisciplinary projects, such as EU2020 funded QROWD, STARS4ALL, and IOF2020, and EPSRC funded DataStories.

His most recent publications have been published to top conferences and journals, such as LREC, AIME, JNCA and ESWA, among others.

He is interested in machine learning subjectivity, deep learning applications, privacy-aware data collection, natural language data augmentation and medicinal deep learning applications. His goal is to improve social welfare through research and development of privacy-aware and human-centered applications, tools and services.

Reference: Haralabopoulos, G. , Torres Torres, M., Anagnostopoulos, I. and McAuley, D. (2021) Privacy-preserving text labelling through crowdsourcing. In: Artificial Intelligence Applications and Innovations 2021 IFIP WG 12.5 International Workshops, 25-27 JUN 2021, Hersonissos, Crete, Greece. doi: https://doi.org/10.1007/978-3-030-79157-5_35
Reference: Haralabopoulos, G. , Torres, M. T., Anagnostopoulos, I. and McAuley, D. (2021) Text data augmentations: permutation, antonyms and negation. Expert Systems with Applications, 177. 114769. ISSN 0957-4174 doi: https://doi.org/10.1016/j.eswa.2021.114769