Shamaila Iram

Shamaila Iram serves as a Research Fellow in the School of the Built Environment at the University of Salford, applying machine learning and artificial intelligence to healthcare challenges including early detection of neurodegenerative diseases and preterm birth prediction through bio-signal analysis.

Her academic background includes:

• Ph.D. in Computer Science from Liverpool John Moores University (2014)
• MSc in Computing and Information Systems with distinction from Liverpool John Moores University (2010)
• BSc (Hons) in Computer Science

Dr. Iram's research centers on Machine Learning, Artificial Intelligence, and Big Data Analysis within healthcare contexts, with significant contributions to EEG-based Alzheimer's diagnosis and EHG signal processing for obstetric monitoring. Her work bridges biomedical engineering and health informatics through innovative signal processing techniques.

Publication analysis reveals consistent focus on machine learning applications for biomedical signal interpretation, particularly in neurodegenerative disorders and pregnancy monitoring. Her research demonstrates cross-domain versatility through contributions to e-Health systems and critical infrastructure security.

Scientific Awards: No awards or fellowships were documented in the source materials.

Advising and Grants: The provided information contains no details regarding graduate students supervised or research grants administered by Dr. Iram.

THINKlab at the University of Salford serves as Dr. Iram's research base, connecting her work to broader initiatives in resilient and smart cities through projects including MOBILISE, RenoZEB, and TRANSCEND, where machine learning techniques are applied to built environment challenges.