Dr Nina Dethlefs

Dr Nina Dethlefs

Senior Lecturer, Director of Research

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.

For projects, detailed publications and my team, please see: https://bda-hull.github.io/

Data Analysis and Visualisation

Applied AI

Understanding AI

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, Article 111051. 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

Research interests

Artificial Intelligence, Natural language processing, Interactive Systems, Environmental modelling / Sustainable AI, Offshore wind

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

Postgraduate supervision

Natural language processing, interactive systems, AI, environmental data analytics