Seeing how AI can directly contribute to sustainable agriculture, improving food security while also addressing practical needs in the field, has give

What does your typical day look like as a PhD student at Sheffield?

As a PhD student in the Pervasive Computing Group at the University of Sheffield, my responsibilities revolve around conducting original research on the explainability of deep learning. This role requires continuous engagement with the academic literature to identify research gaps, formulate hypotheses, and develop novel solutions that address both theoretical and applied challenges.

Alongside my doctoral work, I am engaged in collaborating with interdisciplinary teams as a research assistant on an applied AI project in agriculture, where I develop and optimise pest detection models for supporting farm management. The outcomes of this project have been adopted by agri-technology companies, which has led to the establishment of international academic-industrial cooperation. In addition, I occasionally support the supervision and teaching of MSc students, as a graduate teaching assistant, helping them align their projects with broader research goals.

What's your career story been like since you graduated?

Since completing my MSc in Computer Science at the University of Sheffield, I began my journey at China Unicom, as a cloud computing engineer. During my two years there, I worked on large-scale cloud computing systems, which deepened my understanding of real-world application challenges in computer technologies. This industry experience inspired me to return to academia with a focus on research that bridges practical needs and theoretical innovation. I subsequently re-joined the University of Sheffield as a PhD student in the Pervasive Computing Group, where I now focus on explainability in deep learning, aiming to develop transparent and trustworthy AI systems for real-world domains. In parallel, I have taken on the role of a research assistant and graduate teaching assistant, involving designing experiments, developing models, collaborating with interdisciplinary teams, and supporting students, which have complemented and enriched my doctoral training.

What is the most rewarding aspect of your work so far?

The most rewarding aspect of my current role is the opportunity to work on interdisciplinary projects that apply deep learning to real-world challenges in agriculture. One of my most fulfilling experiences has been developing object detection models that support pest detection in agriculture. Seeing how AI can directly contribute to sustainable agriculture, improving food security while also addressing practical needs in the field, has given my research a strong sense of purpose.

In addition to the technical contributions, I have had the opportunity to publish several academic conference and journal papers as part of this work, which has allowed me to share insights with both the AI and agricultural research communities. Furthermore, I was actively involved in project application and management as a co-Principal Investigator (co-PI), which culminated in securing additional funding to extend the scope of our research. This experience has deepened my academic development.

Thinking back to your degree, how did it prepare you for your current role?

Reflecting on my MSc in Computer Science at the University of Sheffield, I can confidently say it laid a strong foundation for my current role as a PhD student. The programme not only equipped me with core technical skills in coding and machine learning, but also fostered a mindset of critical thinking and problem-solving that I now rely on every day in research.

Importantly, the course emphasised research-oriented learning, through group work, independent projects, and my MSc dissertation, which prepared me for the open-ended nature of academic inquiry. I learned how to evaluate existing literature, formulate research questions, and communicate technical work clearly, skills that are essential to my daily work as a PhD student.

Importantly, the course emphasised research-oriented learning, through group work, independent projects, and my MSc dissertation, which prepared me for the open-ended nature of academic inquiry.

Zhipeng Yuan

MSc Advanced Computer Science alumnus, current PhD student at Sheffield

What advice would you give to younger students interested in studying engineering at Sheffield?

My advice to younger students considering engineering at Sheffield is to fully embrace the opportunities both inside and outside the classroom. The University of Sheffield offers not just excellent academic training, but also a vibrant and supportive research environment where you can explore your interests and grow as an independent thinker.

I encourage students to take advantage of the many hands-on projects, research opportunities, and student societies. Whether it’s joining a coding team, attending public lectures, or working on a summer research internship, these experiences will help you discover what excites you most and give you practical skills beyond the curriculum. Don’t hesitate to ask questions, seek feedback, and build relationships with your lecturers and peers. These connections can be incredibly valuable for both your academic and personal development.

Finally, stay curious and open-minded. Engineering and computer science are constantly evolving fields. What you start studying might lead you to unexpected, exciting directions.