Professor Nophar Geifman

My interests lie in data sciences within healthcare and medicine; extending the use of artificial intelligence and big-data analytics to improve patient-centric predictions, treatment and outcomes, while also enhancing the open sharing of biomedical data. I have developed and employed a wide range of AI methods and applications, from text mining to machine learning, with the overarching goal of producing translational research with real-world impact. 

My research centres on patient stratification, and biomarker discovery from large, diverse clinical and ‘omics’ datasets; applying informatics techniques, AI and machine learning for discovery in various areas of medicine, particularly where conventional research methods have over-simplified the inherent complexity of disease and care.

I completed my postdoctoral training with Professor Atul J. Butte at Stanford University and the Institute for Computational Health Sciences at the University of California, San Francisco, and hold a PhD and Master’s degree in Medical Sciences (Biomedical Informatics) and an undergraduate degree in Biology. I have extensive working knowledge of clinical trial data analysis, application of machine learning approaches, analysis of ‘omics’ datasets, bioinformatics techniques, mining of medical records, and big data integration; as well as knowledgebase modelling and construction. I am a Fellow of the Higher Education Academy and have a strong interest in new methods to teach AI and informatics.