Fabio Luciani

Fabio Luciani is a Professor in Systems Immunology and Machine Learning at the School of Medical Sciences at UNSW Sydney, with visiting fellow positions at the Garvan Institute for Medical Research and Weill Cornell College of Medicine NY, USA. His interdisciplinary research program integrates immunology, genomics, and computational approaches to develop novel immunotherapies for cancer and autoimmune diseases.

With a background spanning physics, theoretical biology, and biophysics, Luciani leads an interdisciplinary team with expertise in immunology, mathematical modeling, statistics, and bioinformatics. His research focuses on T cell responses using single-cell genomic technologies to understand immune responses in viral infections, autoimmunity, and CAR T cell therapies. He has developed landmark bioinformatic methods for combining single-cell transcriptome data with antigen receptor sequences and computational models for haplotype reconstruction.

Luciani's work demonstrates a strong emphasis on translating molecular and genomic discoveries into clinical interventions, particularly in predicting side effects and identifying solutions for unmet clinical needs in immunotherapy. His research spans multiple collaborative projects including single-cell multi-omics analysis in coeliac disease with Prof Chris Goodnow's team at the Garvan Institute, and CAR T cell therapy response studies with clinicians at Westmead Hospital and Weill Cornell.

His scientific contributions include over 140 peer-reviewed publications and successful acquisition of more than $20 million in research funding from organizations including ARC, NHMRC, JDRF, NIH, and private industry partners. He serves as Principal Investigator of the UNSW Future Institute of Cellular Genomics, which has secured $4.5 million over seven years for single-cell technologies in cellular genomics.

As an academic supervisor, Luciani currently mentors 3 PhD students, 2 honours students, and 2 postdoctoral researchers, welcoming applications from students interested in bioinformatics, statistics, data science, immunology, and genomics. His work bridges theoretical approaches with experimental immunology to advance precision medicine and transform current immunotherapies into more precise and accessible solutions.