Fasih Haider

Background

About Fasih Haider

Dr. Fasih Haider, Senior Member of the Chartered Engineer (CEng), and Fellow of the British Computer Society (FBCS), is a researcher with a track record in scientific innovation spanning ambient assisted living technologies, digital biomarkers, affective computing, and machine learning. He has led and managed interdisciplinary projects, overseeing work packages, industry collaboration, and translational outputs. Recognised by Tech Nation UK as a leader in digital technology, Dr. Haider is at the forefront of AI-driven health monitoring.His research has been presented at leading international venues in speech and multimodal information processing and has garnered over 1,500 citations (Google Scholar: h-index: 16, i10-index: 32). He has played a pivotal role in major EU-funded projects, including INT-ACT (Intangible Cultural Heritage, Bridging the Past, Present and Future), SAAM (Supporting Active Ageing through Multimodal Coaching), and METALOGUE (Multiperspective Multimodal Dialogue: dialogue system with metacognitive abilities), contributing substantially to adaptive dialogue systems and user modeling.His portfolio includes machine learning models for automatic classification of rodent vocalisations to support behavioural phenotyping in neuroscience. With NHS clinical partners, he led research on sleep spindle detection from EEG to aid early diagnosis of sleep disorders and neurodegenerative diseases. His industrial work spans Huawei video intelligence and a UKRI-BBSRC-funded secondment on circulatory biomarkers for Alzheimer’s dementia diagnosis.At the Usher Institute, University of Edinburgh, he worked with Sony on AI pipelines for cognitive assessment and voice data to detect early decline in Alzheimer’s disease and align models with treatment goals, supporting remote cognitive monitoring and decision-making tools.Dr. Haider also contributed to the UK’s flagship EAVE II project, analysing clinical and vaccination data from Scotland and Brazil to assess natural immunity durability following COVID-19 infection. His insights informed reinfection risk and post-pandemic vaccine policy.He previously held a Research Associate role at the IDCOM Institute, contributing to the EPSRC New Horizons project on representation learning under data sparsity, aligned with his interest in generative models, low-resource learning, and explainable AI.