Farideh Jalali

Farideh Jalali is a Health Data Scientist at the Keavney Lab (https://www.keavneylab.com/) within the Division of Cardiovascular Sciences at the University of Manchester. She specializes in applying statistical machine learning to healthcare research, focusing on multimorbidity, precision medicine, and predictive models using genetic and clinical data. Her work aims to improve clinical decision-making and patient outcomes through advanced analytics.

Education:

• BSc in Telecommunication Engineering (2008), University of Manchester
• MSc in Microelectronics (2011), Newcastle University
• PhD in Computer Science (2016), University of Manchester

Research Interests: Jalali integrates medical domain knowledge with computational methods to develop predictive models for risk assessment, disease prediction, and personalized medicine. Key areas include multimorbidity patterns, electronic health records analysis, and genetic risk modeling. She leads an MRC-funded project on precision medicine and collaborates on an ARUK initiative to predict psoriatic arthritis (PsA) in psoriasis patients.

Awards:

• 3 Minute Thesis People's Choice Winner (2015)
• MRC Skills Development Fellowship (2020)
• Overseas Tuition Fee Bursary (2012)

Grants & Collaborations: Her MRC fellowship involves collaborations with Anne Barton, Niels Peek, Thomas House, and John Bowes. She also works on PsA prediction with multidisciplinary teams in NHS and academic settings. Recent projects include analyzing multimorbidity patterns in Wales and assessing vaccine efficacy in congenital heart disease patients.

Labs & Teams: She is affiliated with the Keavney Lab and contributes to research beacons like Digital Futures and the Christabel Pankhurst Institute. Her work aligns with UN SDGs related to health and well-being.