Nathan Hughes

Nathan Hughes

Research Associate in HCI for Clinical AI

University of York

Biography

Nathan is a Research Associate at the University of York. His work focuses on designing human-centred explainable AI in healthcare. He has experience with both qualitative and quantitative methods, applying them within a multitude of HCI settings (including games, autonomous vehicles, and air traffic control).

Download my CV.

Interests
  • Human Computer Interaction
  • Decision Making
  • Player Experience
  • Factor Analysis
Education
  • PhD in Computer Science

    University of York

  • Master of Psychology (MPsych), 2018

    University of York

Skills

Cognitive Psychology

Decision-Making | Goal Pursuit | Motivation

Data Analysis

Quantitative | Qualitative | Mixed Methods

RStudio

Analysis & Visualisation

Questionnaires

Design | Distribution | Analysis

Illustration

Freelance Digital Artist

Creative Writing

Fantasy Novel Writer

Experience

 
 
 
 
 
Research Trainee
University of York
May 2022 – Feb 2023 York

Research project in collaboration with the National Air Traffic Service (NATS) on how to use Wizard of Oz techniques to prototype automated decision-making tools in Air Traffic Control. Responsibilities include:

  • Interviewing Users & Stakeholders
  • Designing & Running Workshops
  • Analysing Mixed Methods Data
 
 
 
 
 
Research Trainee
University of York
Jan 2021 – May 2021 York

Research project in collaboration with the National Air Traffic Service (NATS) on how to safely introduce automated decision-making tools to Air Traffic Control. Responsibilities include:

  • Policy & Literature Search
  • Designing & Running Workshops
  • Methodology Design
 
 
 
 
 
Video Transcriber
University of York
Oct 2020 – Dec 2020 York
Transcribed online lectures for multiple Computer Science modules.
 
 
 
 
 
Student Technician
University of York
Sep 2017 – Oct 2018 York

Researched the impact of autonomous vehicles on driving behaviour, using Virtual Reality (VR) techonology. Responsibilities included:

  • Running over 100 participants with up to 4 simultaneous participants
  • Exploratory data analysis
  • Resolving coding errors

Recent Publications

Contact