Koohy Group: Decoding the underlying rules of T cell response by AI and Machine-Learning strategies

About the Research 

T cell responses are triggered when T cells recognize antigens presented by molecules such as MHC on the surface of target cells. This process is a pillar of our adaptive immune system. Yet, despite decades of research and groundbreaking discoveries, one central question remains unresolved: how do T cells know what to react to—and what to ignore?

This seemingly simple question lies at the heart of infection, cancer, and autoimmunity, and more broadly defines how the immune system protects us—or, at times, tragically turns against us.

Our lab seeks to address this challenge by integrating advanced AI and data science approaches. We employ state-of-the-art models, including foundational models, deep generative models, and protein language models, to decode the underlying principles of T cell antigen recognition. By doing so, we aim to harness T cell immunity to inform the design of next-generation therapeutics, such as precision medicines and immunotherapies.

Students joining our lab will have the opportunity to:

• Explore questions at the intersection of immunology, data science, and AI.
• Work with diverse data types, from single-cell sequencing to structural biology.
• Apply cutting-edge machine learning models to immunological challenges.
• Contribute to translational efforts in infection, cancer, and autoimmunity research.

Our work sits at the intersection of T cell immunology and data science. This project is therefore particularly well suited to:

• Students from theoretical or quantitative disciplines—such as Machine Learning, Data Science, Computational Biology, Mathematics/Statistics, or Physics—who are interested in applying their skills to immunology, especially T cell biology.
• Students with a strong background in T cell immunology who are motivated to develop expertise in advanced data science and machine learning approaches, including deep neural networks. 

We welcome applications from candidates with either background, provided they have a genuine interest in bridging disciplines to tackle fundamental questions in immunology.

Training Opportunities

You will have weekly one to one meeting with your supervisor. Additionally will be mentored by a senior postdoc in the lab.

Single cell transcriptomics and immune repertoire data analysis training will be available in the lab. You will also learning from the our weekly group meetings, and annual conferences and workshops.

Students will be enrolled on the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.

Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence, and impact. Students are actively encouraged to take advantage of the training opportunities available to them.

As well as the specific training detailed above, students will have access to a wide range of seminars and training opportunities through the many research institutes and centres based in Oxford.

The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.

Additional Supervisors 

1. Charlotte Deane

Publications