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Dr Daniel Onah

Senior Teaching Associate for the Institute of Coding

Daniel Onah

InfoLab21

LA1 4WA

Lancaster

PhD supervision

My research is in the broad area of Natural Language Processing (NLP), Machine Learning (supervised, unsupervised and deep learning), Data Science and Educational Technology. I have supervised several students in a wide range of topics. I welcome contact from any prospective PhD students whose interest is in any topics broadly concerning:- • automated text summarization (TextRank, extractive and abstractive) • computational journalism • question and answering models • speech recognition • search engines and databases • data and text analysis • model design and so on. I'm also interested in students with topics in:- • MOOCs • e-learning pedagogical systems and tools • design science architecture. If you are keen and interested in any of these areas or related areas do contact me.

Research Interests

My research interests are in Machine Learning and Natural Language Processing in the aspects of text and medical data predictive analysis. I am interested in transforming research models and methods into software application systems with web-based GUIs and Databases. My primary research interest lies in translating/transforming Machine Learning algorithms & Mathematical models into web application search engine interfaces for text and data analysis.

Profile

Daniel Onah is a Senior Teaching Associate for the Institute of Coding in the School of Computing and Communications at Lancaster University. He is co-affiliated with the Data Science, Software Engineering and NLP groups. Previously, he was a Postdoctoral Research Software Developer at UCL Institute of Neurology. At UCL, he developed a web-based Machine Learning Gene Prediction software application for predicting and analysing over 1126 mutated genes associated to 26 subtypes of Mendelian neurological diseases. The research review the likelihood that a gene when mutated will cause neurological phenotype. He completed a PhD at the University of Warwick, Department of Computer Science in 2017. His doctoral research was focused on technology enhance learning, where he developed and facilitated a standalone e-learning MOOC platform known as eLDaMOOC (currently being used as the outreach CPD programme for teachers of computer science in the UK by his former department of Computer Science at Warwick University). His current research interests are in Natural Language Processing, Information Retrieval, Machine Learning, Deep Learning,  Bioinformatics, probabilistic predictive modeling and Data Science. He develops text analysis and classification methods to solve problems in other scientific areas such as medical science and predictive analysis for scientific research.

He is a recipient of several teaching Awards including the Best Research Paper Award for IMCL 2018. He has served on various editorial boards and he is an active reviewer of several international journals 

Current Teaching

Convenor: 2020/2021 Academic Year

  • Online Virtual Worlds MOOC - Course Lead Educator/Coordinator

2020/2021 Academic Year

  • SCC.110 Software Development
  • SCC.010 Computational Thinking
  • SCC.011 The Art of Coding

Convenor: 2019/2020 Academic Year

  • SCC.021 Scripting in Python
  • SCC.022 Making Sense of Data  co-convenor
  • SCC.030 Creative Project co-convenor

Studio Demonstration/Micro Lectures: 2019/2020 Academic Year

  • SCC.010 Computational Thinking
  • SCC.011 The Art of Coding
  • SCC.020 Creative We Applications
  • SCC.026 The Evolution of Computing
  • SCC.024 Digital Making and Crafting
  • SCC.023 Information Visualisation
  • SCC.025 Virtual Worlds

 

Supervision: 

MA Student

  • 2019-2020: Master of Arts Student - Erasmus-Programme  (Topic area: Machine Reading - Deep Learning Text Analysis, Neural Network) 

BSc Student

  • 2019 - 2020: Undergraduate  (Topic: NLP-Speech Recognition)

 

 

Research Grants

 

  • 2020- 2021: The Joy Welch Educational Charitable Trust
  • Research: Machine Learning Gene Prediction App: An innovative method to precision medicine and predictive analysis of mutated genes associated to neurological phenotype of diseases

        01/04/2020 → 31/03/2021

        Principal Investigator (PI)

 

  • 2019- 2020: The Joy Welch Educational Charitable Trust 
  • Research: Natural language processing A novel method to precision medicine and machine learning predictive analysis of genes associated to diseases.

        01/04/2019 → 31/03/2020 

        Principal Investigator (PI)

Career Details

Academic Qualifications:

  • 2017: Doctor of Philosophy in Computer Science ( University of Warwick)
  • 2011: MSc Computer Systems Engineering(Software Systems) [Distinction] (University of East London)

Current/Previous Positions:

  • 2018 - present: Senior Teaching Associate, Institute of Coding, School of Computing & Communications, Lancaster University, UK
  • 2017-2018: Post-Doctoral Research Software Developer, UCL Institute of Neurology, University College London, UK
  • 2018: Graduate Teaching Assistant,  Department of Information Studies, University College London, UK
  • 2018: Teaching Demonstrator/Assistant, Department of Computer Science & Information Systems, Birkbeck, University of London, UK
  • 2013 - 2017: Teaching Assistant, Department of Computer Science, University of Warwick, UK

Professional Role

2020: Fellow (FHEA), AdvanceHE, UK

2020: Certified External Examiner, AdvanceHE, UK

2015: Associate Fellow (AFHEA), Higher Education Academy, UK

Current Research

  • Software Testing & analysis, vulnerability and dependability
  • Automated text summarization software application model
  • Semi-supervised neural network & deep learning content, questions and answering software application model
  • Machine learning gene prediction software application model
  • Speech recognition software application model
  • Search engine & databases

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