Teaching

Dr Lewis Quayle is Senior Lecturer in Data Science and Analytics in the Department of Computing at Sheffield Hallam University, with an interdisciplinary background in bioinformatics, data science, and cancer biology. After completing his PhD in the Department of Oncology & Metabolism at The University of Sheffield Medical School, he transitioned from laboratory-based research to computational biology, focusing on healthcare data analytics. Lewis’s professional journey has been marked by positions in both academia and industry, including a training placement in high-performance computing at the Kinghorn Centre for Clinical Genomics in Sydney, Australia, and an appointment as a full-time Cancer Bioinformatician at the University of Sheffield Bioinformatics Core Facility.

Teaching Responsibilities

As an educator, Lewis am committed to cultivating data science and analytical skills in healthcare and biomedical sciences. His current teaching responsibilities include:

• Modules:
  • Learning Systems and Data Analysis for Healthcare (MSc Big Data Analytics / MSc Healthcare Analytics and Artificial Intelligence)
  • Ethics of Healthcare Information
  • Advanced Data Management Project

Lewis integrates practical data science techniques into his teaching, ensuring students can apply theoretical knowledge to real-world problems. I also serve as an academic advisor to 42 MSc students, guiding their academic progress and research development.

University Teaching Experience

• Senior Lecturer in Data Science and Analytics (2023–Present)
  Sheffield Hallam University, Department of Computing
• Honorary Senior Lecturer in Bioinformatics (2023–2026)
  University of Sheffield, Department of Oncology & Metabolism
• Cancer Bioinformatician (2022–2023)
  Sheffield Bioinformatics Core Facility
• Postdoctoral Cancer Researcher (2017–2022)
  University of Sheffield, Oncology & Metabolism

My teaching experience spans undergraduate, MSc, and PhD levels, where I’ve covered subjects such as next-generation sequencing, bioinformatics, machine learning, and best practices in computational research.

Professional Development and Fellowships

• Fellowship of Advance HE (FHEA)
  • Awarded in recognition of my commitment to professionalism in learning and teaching in higher education, meeting the UK Professional Standards Framework (2023).
• Certified Carpentries Instructor
  • Qualified to teach foundational coding and data science skills through Data Carpentry, Library Carpentry, and Software Carpentry workshops.

Course Material Developed

I have developed and delivered a range of course materials for workshops, tutorials, and seminars, including:

• 2024-03-15: Introduction to Machine Learning in Healthcare Analytics
• 2023-11-10: RNAseq Analysis and Interpretation
• 2023-09-20: Data Ethics and Governance in Healthcare

Supervision and Mentorship

Throughout my academic career, I have actively supervised undergraduate, MSc, and PhD students in bioinformatics and data science. I have supervised research projects in areas such as:

• High-throughput sequencing analysis
• Data integration and predictive modelling in cancer research
• Transcriptomic data analysis

My students have successfully completed projects on cancer genomics, machine learning in healthcare, and computational analysis of clinical data, many of whom have gone on to pursue advanced research roles or further studies.

School Outreach and Public Engagement

As a STEM ambassador, I am passionate about promoting data science and bioinformatics in schools and the wider community. I have participated in initiatives such as:

• MRC Festival of Science
• Public Engagement Q&A sessions on topics in cancer research, data analytics, and computational biology
• Workshops and seminars on the application of bioinformatics in healthcare, in partnership with regional charities

Research-Informed Teaching

My teaching is informed by ongoing research projects in cancer genomics, high-throughput data analysis, and healthcare analytics. I am actively involved in research projects with Genomics England’s 100,000 Genomes Project and have co-investigator roles in projects focused on chemotherapy response prediction and molecular drivers of metastatic dormancy in breast cancer.

By integrating cutting-edge research with practical teaching, I aim to provide students with a comprehensive learning experience that equips them with skills directly applicable to real-world challenges in bioinformatics and data science.