University of Sheffield

Improving the FLAME GPU User Experience through Examples

Intern: Lewie Flemming
Degree course: BSc Computer Science
LinkedIn Profile

GPU-Accelerated Domain Adaptation for Personalised Speech Recognition of Electrolarynx Voices

Intern: Echo Garratt
Degree course: Computer Science MComp
LinkedIn Profile

My interest in programming started in a coding club when I was 8, and have been interested medical and assistive computing for the past 6 years. This made a degree in computer science an obvious choice.
I am enjoying research in healthcare computing at university, and aim to do a PhD and go into academia after I graduate. Beyond my degree, I am a keen cyclist, runner, and rugby player, and enjoy playing guitar in my band.

Why did you apply for this internship?

I researched this field during my undergraduate dissertation and found it fascinating, and am very excited to keep working on it. It will be really good experience to go alongside my studies, and set me up well for my future career plans.

What do you hope to gain in completing this project?

Completing this project, I will have real research experience beyond my dissertation, and this will set me up very well for the future. It will be good to have this research experience, working with academics, and contributing to the academic community.

MobGap-Plus: An HPC-Powered ML Pipeline for Digital Mobility: Bridging Python & R-Shiny

Intern: Artem Vakhutinskiy
Degree course: MComp Computer Science
LinkedIn Profile

I am a fourth-year student and originally chose my degree out of a genuine love for computers. My passion for academic research was sparked during my third-year dissertation, where I developed an early-stopping method for AutoML. I am currently continuing on that path by researching LLM behavioural alignment as part of my fourth-year module.

My technical interests sit at the intersection of low-level computing and machine learning. Once I graduate, I aim to specialise in data-intensive software engineering — specifically focusing on transforming cutting-edge AI/ML research into robust, production-ready pipelines.

Outside my core degree, I enjoy tackling problem-solving challenges at hackathons, and I spend time helping other students learn to code as a Java demonstrator.

Why did you apply for this internship?

I applied to tackle the challenge of translating separate research components into a production-ready tool. I want to gain more experience and refine my skills in research-to-production engineering. The combination of real-world sensor data, pipeline optimisation, and cross-language integration makes this project the exact type of research software engineering I intend to pursue in my career.

What do you hope to gain in completing this project?

I hope to gain hands-on experience building end-to-end ML pipelines. Ultimately, I want to refine my high-performance computing and software engineering skills to prepare for a career in translating complex academic research into robust, production-ready tools.

AI for Smart Farming: Detecting and Identifying Cattle from Drone Images

Intern: Eyad Ahmed Abdelaziz Abdelnabi Mohamed
Degree course: MEng Computer Science (Software Engineering)