Dr Nina Dethlefs

Dr Nina Dethlefs

Lecturer

Faculty and Department

  • Faculty of Science and Engineering
  • Department of Computer Science and Technology

Summary

My research interests lie at the intersection of machine learning and natural language processing (NLP), particularly in the areas of data-to-text and natural language generation (NLG), interactive systems, assistive technologies, and domain transfer and adaptability for data analytics in a wider AI context. I have spent the last few years working with neural networks as a primary algorithm family but have previously worked with graphical models, clustering and reinforcement learning. Most recently I have become interested in applying AI and NLP in "useful" contexts such as mental health, operations and maintenance in an engineering context, and in a natural world context such as prediction of water flow and quality.

Undergraduate

Data Analysis and Visualisation

Applied AI

Web Skills and Technologies

Recent outputs

View more outputs

Conference Proceeding

Transparency of execution using epigenetic networks

Dethlefs, N., & Turner, A. (2017). Transparency of execution using epigenetic networks. In C. Knibbe, G. Beslon, D. Parsons, D. Misevic, J. Rouzaud-Cornabas, N. Bredeche, …H. Soula (Eds.), Proceedings of the ECAL 2017 (404-411). https://doi.org/10.7551/ecal_a_068

Journal Article

Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future

Chatterjee, J., & Dethlefs, N. (2021). Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renewable & sustainable energy reviews, 144, https://doi.org/10.1016/j.rser.2021.111051

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Schoene, A. M., Turner, A. P., De Mel, G., & Dethlefs, N. (in press). Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes. IEEE Transactions on Affective Computing, https://doi.org/10.1109/TAFFC.2021.3057105

A divide-and-conquer approach to neural natural language generation from structured data

Dethlefs, N., Schoene, A., & Cuayáhuitl, H. (2021). A divide-and-conquer approach to neural natural language generation from structured data. Neurocomputing, 433, 300-309. https://doi.org/10.1016/j.neucom.2020.12.083

Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines

Chatterjee, J., & Dethlefs, N. (2020). Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind energy, 23(8), 1693-1710. https://doi.org/10.1002/we.2510

Project

Funder

Grant

Started

Status

Project

Deep Text Generation from a Knowledge Graph

Funder

Diffbot

Grant

£21,119.00

Started

1 October 2017

Status

Complete