Skip to main content

Dr Giannis Haralabopoulos

Lecturer in Data Analytics

IRC Seminar Coordinator

Giannis Haralabopoulos

Specialisms

  • PhD Supervision: Available ✔, 
  • DBA Supervision: Unavailable, 
  • 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.

Topics or projects for PhD supervision:

  • Machine Learning
  • Artificial Intelligence
  • Large Language Models Applications in Businesses
  • Machine Learning
  • Artificial Intelligence Efficiency

Reference: Haralabopoulos, G. , Razis, G. and Anagnostopoulos, I. (2023) A modified long short term memory cell. International Journal of Neural Systems, 33 (7). 2350039. ISSN 1793-6462 doi: https://doi.org/10.1142/S0129065723500399
Henley faculty authors:
Dr Giannis Haralabopoulos
Reference: Haralabopoulos, G. and Anagnostopoulos, I. (2022) A custom state LSTM cell for text classification tasks. In: Iliadis, L., Jayne, C., Tefas, A. and Pimenidis, E. (eds.) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science (1600). Springer, pp. 489-504. ISBN 9783031082221 doi: https://doi.org/10.1007/978-3-031-08223-8_40
Henley faculty authors:
Dr Giannis Haralabopoulos
Reference: Razis, G., Georgilas, S., Haralabopoulos, G. and Anagnostopoulos, I. (2022) User analytics in online social networks: evolving from social instances to social individuals. Computers, 11 (10). 149. ISSN 2073-431X doi: https://doi.org/10.3390/computers11100149
Henley faculty authors:
Dr Giannis Haralabopoulos
Reference: Haralabopoulos, G. , Tsikandilakis, M., Torres Torres, M. and McAuley, D. (2020) Objective assessment of subjective tasks in crowdsourcing applications. In: Language Resources and Evaluation Conference, 11–16 May 2020, Marseille, France.
Henley faculty authors:
Dr Giannis Haralabopoulos
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
Henley faculty authors:
Dr Giannis Haralabopoulos
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
Henley faculty authors:
Dr Giannis Haralabopoulos

Introduction to Machine Learning

Data-driven processes are becoming increasingly popular amongst organisations; quickly replacing qualitative assessments that were, until recently, based on experience and tacit knowledge. Machine learning is widely used in industry and...

Module code: MM257

Business Applications Development

The development of full stack applications is becoming a core requirement for supporting day to day operations across increasingly diverse organisational entities. This is particularly true for digital businesses that...

Module code: MM282