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Dr Nina Dethlefs

Dr Nina Dethlefs

Senior Lecturer

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

This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy

Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy. Patterns, 4(1), Article 100676. https://doi.org/10.1016/j.patter.2022.100676

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

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

Lead investigator

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

Co-investigator

Project

Funder

Grant

Started

Status

Project

Secure and Safe Multi-Robot Systems

Funder

EC European Commission

Grant

£482,634.00

Started

1 January 2021

Status

Ongoing

Project

The EPSRC Centre for Doctoral Training on Offshore Wind Energy and the Environment

Funder

EPSRC Engineering & Physical Sciences Research Council

Grant

£3,840,952.00

Started

1 April 2019

Status

Ongoing

Postgraduate supervision

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

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