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Dr Nina Dethlefs

Dr Nina Dethlefs

Senior Lecturer, Director of Research

Faculty and Department

  • Faculty of Science and Engineering
  • School of Computer Science

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

Journal Article

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Chatterjee, J., & Dethlefs, N. (2022). Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines. IEEE Access, 10, 84710-84737. https://doi.org/10.1109/ACCESS.2022.3197167

Facilitating a smoother transition to renewable energy with AI

Chatterjee, J., & Dethlefs, N. (2022). Facilitating a smoother transition to renewable energy with AI. Patterns, 3(6), Article 100528. https://doi.org/10.1016/j.patter.2022.100528

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

Working Paper

XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines

Chatterjee, J., & Dethlefs, N. (2021). XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines

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

Project

FF2021 -1049 - Physics -informed machine learning for rapid fatigue assessments in offshore wind farms

Funder

EPSRC Engineering & Physical Sciences Research Council

Grant

£96,941.00

Started

1 June 2021

Status

Complete

Postgraduate supervision

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

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