Asif Malik Profile
Staff profile
I teach on postgraduate programmes, with a primary focus on Data Science and Artificial Intelligence.
- Computer Science Lead
What do you most enjoy about teaching at Teesside University London?
I value the Team Teesside culture, which is learner-centric and rewards practical, industry-embedded teaching. Being based in London at the Here East campus allows us to work closely with employers through live briefs, mentored projects and placement opportunities that translate directly into positive student outcomes. I also enjoy working closely with students and supporting their progress - from their first concepts through to becoming confident, industry-ready graduates.
How do your students benefit from your experience?
My teaching is research-informed and closely linked to employer-mentored projects, demo days and professional certifications. I also coach students on the responsible use of generative AI, including prompting, traceability and evaluation, ensuring graduates are both job-ready and ethically aware.
Drawing on my experience, I emphasise transparent grading, regular reviews and short vivas to build confidence and maintain academic integrity. I also provide regular one-to-one check-ins and tailor support to each student’s needs. This is particularly important for students adapting to the UK higher-education environment.
Tell us about your career to date
I began my academic career at the University of Greenwich, where I progressed to Assistant Professor and Course Leader for Java. During this time, I taught at both undergraduate and postgraduate levels, served on academic boards and supervised dissertations.
More recently, I worked at the Higher Colleges of Technology in the UAE as System Course Team Leader. In this role I led quality assurance and assessment, introduced learning-analytics early-alert systems, and developed employer-driven live briefs.
My scholarship spans digital twins, optimisation and analytics, and I developed an AI-Augmented Software Engineering workshop. I hold a PhD, a PGCert in Higher Education, and I am a Fellow of the Higher Education Academy (FHEA).
What industry links do you have?
I collaborate with New Africa Energies (UK) on optimisation projects using live, NDA-protected datasets. These projects feed directly into employer co-supervised student work and demo days, allowing students to apply and showcase their skills before graduation.
I also work with Tridel Technologies on software and AI briefs, internships and portfolio mentoring. In addition, I chair an Industry Advisory Board, integrate SAS certification pathways, and am preparing NVIDIA Deep Learning Institute certification opportunities to further strengthen student employability.
How has your industry experience enhanced your teaching?
My industry experience allows me to bring real-world constraints, such as NDAs and data governance, into the classroom.
Students work with the same professional toolchain used in industry, including JIRA, Git/GitHub pull requests, code reviews, testing and CI pipelines. Their work is assessed against employer-aligned criteria, ensuring that learning is current, accountable and production-grade.
What has been the highlight of your career so far?
One of the highlights of my career has been designing and embedding a cloud-hosted, AI-augmented software engineering workflow. This approach spans the full development cycle, from requirements and JIRA management to version-controlled code, automated diagrams and continuous integration with responsible-AI guardrails.
Combined with learning-analytics early-alert systems and structured study plans, this approach has delivered strong improvements in student progression, protected academic integrity and helped students produce employer-ready portfolios.
Why should students consider studying this course?
Because you will graduate work-ready. At Here East, students gain access to real-world briefs, mentoring and placement opportunities that build both confidence and a strong CV. The course provides a hands-on, industry-aligned route into Data Science and Analytics, with opportunities to build a professional portfolio and pursue optional certifications each term. Students work with live datasets and complete end-to-end projects, from problem framing and SQL data acquisition through to feature engineering, Python modelling, evaluation, dashboards and deployment. The programme uses a modern data stack, alongside training in data governance and responsible AI, all designed to enhance graduate employability.
What is the best piece of advice you have for your students?
Start small, ship often and make your work reproducible.Keep a clean record of everything, your README, data diary and experiment log, and document the story as you build. Frame the problem first, establish a simple baseline and then iterate: test, explain and compare. Use generative AI as a powerful tool, not a shortcut. Cite sources, validate results and keep evidence of your process. Finally, communicate your impact with clear visuals and a three-sentence summary that any stakeholder can understand.