Projects   

Our cohort of students and professionals undertaking short projects
supported by the project are listed below, followed by a selection of
reflective case studies.

Completed Projects Summary

2024/2025 Cohort: Selected Case studies

Name: Tejal Ravikumar Yekkula 

Course: Masters in Artificial Intelligence  

Partner: The University of Manchester 

Title: AI support for statistical disclosure checks and synthetic data 

Biography: I completed my undergraduate degree in AI and Machine Learning at BNM Institute of Technology, co-authoring “Face Recognition using MTCNN, Inception-ResNet with Ensemble Approach, which proposed a deep learning model combining advanced face detection with ensemble classificationMy final-year project, SurgiLearnVR, a VR medical training platform, won the Best Project Award.  

My journey includes internships with Oracle Financial Services Software and PwC, applying AI, machine learning, and cloud technologies to improve systems. These experiences sharpened my technical expertise while giving valuable insights into how AI can enhance efficiency and trust in both healthcare and financial systems. 

I am passionate about mentorship and outreach, supporting diversity in tech initiatives, and accessibility in AI. I envision a future where AI is not only powerful but also ethical, inclusive, and accessible, and am committed to contributing to that vision. 

Project Summary: My project focused on creating an AI agent that can generate synthetic healthcare data, motivated by the challenge of accessing real patient data for research, which is often restricted due to privacy concerns. By producing realistic but entirely fictional medical records, this offers researchers a safe way to test ideas, build models, and explore healthcare questions without exposing sensitive information. 

I combined advanced AI language models with smart data design so the agent could respond to simple prompts like “generate 50 asthma patients with their prescribed medications.” The generated datasets were then carefully compared with real ones to ensure they looked and behaved realistically while still protecting privacy. I found that AI is especially effective at capturing important clinical data e.g. age, gender, and conditions, though some hospital-related details were less precise. 

The research demonstrated that AI-driven synthetic data is a powerful tool for healthcare innovation, offering a balance between usefulness and privacy. It opens the door for safer, faster research and highlights how AI can be applied responsibly in sensitive fields like medicine. 

Reflections on SDE Skillshub Project Support: The supervision and the support from the Skills Hub team created an environment that was both academically rigorous and practically insightful fostering independence and growth. My supervisor provided in-depth technical mentorship and strategic direction, while the wider SkillsHub team complemented this with expertise in governance, context, and reflective practice. Their combined guidance provided relevance to real-world applications and implications such as ethics, IG, and applicability. Supervision struck a balance between academic rigor and approachable mentorship and was consistently clear, constructive, intellectually stimulating, and encouraging.  

Regular feedback sessions addressed challenges early and maintained progress. More structured checkpoints around the later evaluation phases and guidance on benchmarking against similar synthetic data initiatives would have further contextualized the work. 

Working within this supportive framework was enriching and enabled me to develop a stronger understanding of how technical innovation must align with wider factors. The process helped me build new skills in communicating complex ideas across technical and non-technical settings and greater confidence in applying my knowledge in practical, interdisciplinary contexts. These insights will be invaluable for future technical roles within SDEs. 

 

  

Name: Nouar Nagem 

Course: MSc Artificial Intelligence  

Partner: The University of Manchester 

Project Title: AI support for statistical disclosure checks and synthetic data 

Biography: I recently completed both a BSc and an MSc in Artificial Intelligence, building strong skills in programming, machine learning, and data engineering along the way. I enjoy learning about technology and exploring how AI can be applied in different fields, especially healthcare. Outside my studies, I developed skills in data analysis, project coordination, and stakeholder collaboration, strengthening both my technical and organisational abilities. 

Project summary: I developed an AI agent that understands natural language queries and generates synthetic healthcare data. It is designed to be capable of handling queries from uploaded datasets; supporting medical synonyms and abbreviations using Pinecone with OpenAI embeddings and NHS SNOMED terms; and of producing fully fictional, privacy-preserving patient records with the OpenAI API. 

Reflections on SDE Skillshub Project Support: the supervision form the SDE Team Development Hub was supportive, with weekly meetings providing guidance while allowing independent work. This project gave me hands-on experience developing a complex AI agent, interpreting natural language queries, and generating structured synthetic data. It strengthened my problem-solving skills, improved how I communicate complex ideas, and increased my confidence in pursuing a career in technical development of IT infrastructure. 

 

 Name: Emaan Hajara 

Course: MSc Health Data Science 

Institution: The University of Manchester 

Project Title: Designing a secure architecture that blends on-premises and cloud based computation infrastructure for use in SDE 

Biography: I studied for my undergraduate degree in Biological Sciences (Biotechnology with Enterprise) at the University of LeedsI found that along the way, I realised I wanted to move into a more technical direction. This led me to pursue an MSc in Health Data Science at the University of Manchester. Here, I was introduced to the principles and approach of cloud engineering through my dissertation. That project revealed just how exciting and impactful technical infrastructure can be. I am now eager to explore roles that combine these skills and have a real-world impact in cloud infrastructure, DevOps, and data-driven research 

Project Summary: For my dissertation, I built a secure hybrid cloud architecture from scratch, linking on-premises resources with Azure to create a scalable, automated, and compliant environment for sensitive data. Having started with no prior experience in cloud engineering, I successfully designed and tested a reproducible blueprint demonstrating that hybrid models can be secure and cost-effective, and that it’s possible to modernise infrastructure without compromising on governance requirements. 

Reflections on SDE Skillshub Project Support: My supervisors were invaluable in helping me make this leap into technical development. They guided me through unfamiliar tools like Terraform, GitHub Actions, and Visual Studio Code, helping me work through challenges, rather than just giving instructions, which in turn made me much more confident in applying the skills myself. With regular meetings and feedback, I was not only able to keep on track but was also presented with multiple opportunities to improve, whether that was my skills or the dissertation itself. Their input undoubtedly strengthened the outcome of the project. 

With hands-on expertise in hybrid cloud design, CI/CD pipelines and compliance frameworks, I now feel much more confident and ready for roles in DevOps and cloud engineering and am more excited than ever to get started and apply these skills in real-world settings.