Nik joined the team from Durham University where she was a PhD student focusing upon a multidisciplinary study combining the fields of Machine Learning (ML) and Brain-Computer Interfaces (BCI), covering areas including signal recognition, object detection and adversarial learning. Prior to that, Nik worked in the aviation and home-automation industries, as well as getting a Masters from South Korea.


  • Mouse MApp / School of Natural and Environmental Sciences

    • Image processing and Image Analysis
    • Comparing image classification algorithm based on body condition score
    • Integrating the model and weight with web apps for end-user
  • Integrated Multi-Modal Tissue State Mapping of Triple Negative Breast Cancer Progression / School of Computing

    • Understanding the different modalities for cancer imaging (IMC, LA-ICP-MS and Spatial Transcriptomics)
  • Auto Generation of Optimal Deep Learning Networks / School of Computing

    • Creating the framework for Neural Architecture Search pipeline
    • Testing the framework with different image dataloader, image transform and creating mock NAS for the testing
    • Getting Involved in the CVPR NAS workshop
  • TRACK / School of Computing

    • Data annotation and data analysis
    • Developing frameworks for person detection in public transportation images using machine learning
    • Investigating solutions for automated social distancing measures


PhD Computer Science / Durham University


Developing a real-time BCI application to tackle fundamental constraints in bio-signals decoding.


  • Programming:
    • Python
    • Matlab
    • C/C++
    • VB.net
  • Software:
    • Unix Shell
    • LaTeX
    • GitHub Workflows
    • Arduino
    • SQL
    • Labview
    • PcVue
  • Topics:
    • Data Analysis
    • Signal Processing
    • Machine Learning
    • Data Generation
    • Brain Computer Interface
    • GPU Computing


  • I enjoy:
    • Walking
    • Cycling
    • Badminton
    • Painting
    • Video games/board games