We are delighted to announce a new pilot training scheme led by our newly-appointed JGI Research Data Science Advocates. This is a new way to take part in training in a low-stress, collaborative and supportive environment, and at the same time form a community of data scientists in your area.
The pilot will run JGI training events over a whole week in Schools, supported by a local Data Science Advocate. They will run sessions to support a cohort to undertake the training together, over the course of a week. The formal training takes only around 2-3 hours to complete, but it is anticipated that this format will allow deeper learning and more useful application to research.
To take part in the pilot (which is aimed at relatively inexperienced coders within a discipline), please email to jgi-training@bristol.ac.uk. If your school doesn’t have a volunteer, you would be welcomed at a research-adjacent community. Bios for our Advocates are below and even if you don’t need this particular training, they would love to include you in an ongoing data science community, so please get in touch.
Ruolin Wu

I am a PhD student of paleobiology diving into the mysteries of evolutionary history. Armed with code, fossils, and molecular data, I craft stories about topological and temporal pattern of animals and plants. Outside of academia, I like climbing, handcrafts, succulents and ferns of any kind.
Zhiyuan Xu

I am a 1st year PhD student focusing on data science and artificial intelligence, with a particular focus on large language models and their applications. My background includes experience in machine learning, data-driven research, and interdisciplinary collaboration to address complex problems.
Bryony Clifton

I’m a PhD student in Biochemistry, studying the molecular details underpinning neurotransmission. My project focuses on identifying the biological role for an uncharacterised intramembrane protease found in the human brain. During my PhD, I have become aware of the importance of developing tools to present complex datasets in a clear and informative way. I am excited to begin my role with the JGI where I can support others to build these skills too.
Catherine Upex

I’m Catherine and I’m a first year PhD student based in the medical school. I’m using data science and AI to understand the shape and movement patterns of the heart over different disease states. I’m also currently working on a mini-project using AI protein folding tools, like AlphaFold, and computer simulations to uncover interactions between synthetic cannabinoids and the hERG potassium channel and its relation to arrythmia risk.
Kaan Deniz

Aerospace Engineer who has intensive industrial experience in numerical modelling with a MSc degree from the University of Bristol/ Aerospace Engineering. Current PhD student in Aerospace Engineering at the University of Bristol. Research focus is numerical modelling of composite manufacturing processes.
Boy Li

I study how to synergize domain-specific knowledge with data-driven deep learning models to extract information from remote sensing imagery.
Vaishnudebi Dutta

I am an Engineering Mathematics PhD student working on model and data-driven design of combination therapies for non-small cell lung cancer. Beyond my research, I serve as the School of Engineering Mathematics and Technology (SEMT) PhD Student Representative, advocating for and supporting the academic community. I also hold a key position as the PhD Representative for the Bristol Cancer Research Network where I get the opportunity to share research updates to Clinicians, and others in the network. Additionally, I manage the network’s official X (formerly Twitter) presence, helping to disseminate research developments and maintain engagement with the broader scientific community.
Zhengzhe Peng

I am a PhD student with a diverse background in computer science, business, and over a year of IT work experience. My research applies advanced data science methods, with a focus on AI, to explore real-world challenges. I am dedicated to expanding my knowledge in these fields and eager to help others who are new to data science, working together to advance and explore new possibilities in this ever-evolving domain.
Winfred Gatua

Winfred Gatua is a PhD Fellow at the University of Bristol, specializing in Molecular Genetics and Life Course Epidemiology. Her research focuses on the triangulation of evidence between Mendelian randomization and randomized controlled trials for complex diseases. She holds an MSc in Bioinformatics, a Postgraduate Diploma in Health Research Methods, and a BSc in Biomedical Science and Technology. Transitioning from wet lab biomedical sciences to dry lab bioinformatics, Winfred is a self-taught coder passionate about open science, automation, and reproducible research in genetics. Beyond research, Winfred is dedicated to capacity building, particularly in increasing computational and data literacy among non-computer science researchers. Since 2021, she has been a volunteer instructor with The Carpentries, securing funding, hosting and instructing carpentries lessons that equip researchers with essential skills in data analysis, open science, reproducible research and best practices in scientific computing in different institutions across the globe.