معرفی
Dr Kaitlyn Hair is a Research Fellow at the Evidence for Policy and Practice Information Centre (EPPI Centre) within the Social Research Institute at University College London (UCL), holding a Wellcome Early Career Award. Her work focuses on automating evidence synthesis for dementia and neuroscience research to accelerate translational discovery.
Her academic credentials include:
- Doctor of Philosophy, University of Edinburgh (2017-2021)
- Bachelor of Science (Honours), University of Glasgow (2012-2016)
Hair specializes in developing computational tools for evidence synthesis, particularly in neurodegenerative diseases like Alzheimer's. Her research integrates systematic reviews, meta-analyses of preclinical models, and automation technologies to address fragmented evidence in biomedical research. She leads projects on living evidence summaries and deduplication tools like ASySD, emphasizing reproducibility and real-time data integration.
Analysis of her recent publications reveals a strong trajectory toward AI-driven evidence synthesis, with increasing focus on living systematic reviews, natural language processing for intervention extraction, and cross-disease applications in stroke and chemotherapy-induced neuropathy. Her work bridges neuroscience, informatics, and methodological innovation.
Key recognition includes:
- Wellcome Early Career Award
Hair's grant portfolio centers on her Wellcome-funded project developing automation for dementia evidence synthesis. She actively mentors early-career researchers in scholarly communication and collaborates internationally through initiatives like CAMARADES. Her advisory contributions focus on improving reporting standards (e.g., ARRIVE guidelines) and evidence-based policy.
She operates within UCL's Social Research Institute (EPPI Centre), a hub for systematic reviewing, and previously contributed to the University of Edinburgh's CAMARADES meta-research group. Current efforts target scalable AI solutions for evidence integration across neurological disorders.
