Rate My Professor Fabio Luciani

Professor Fabio Luciani is a Professor in Systems Immunology and Machine Learning at the School of Medical Sciences in the Faculty of Medicine & Health at the University of New South Wales (UNSW Sydney). He earned a Master's degree in Physics from the University of Bologna, Italy, in 2000, and a PhD in Theoretical Biology from Humboldt University, Berlin, Germany, in 2006. Luciani completed postdoctoral fellowships at UNSW, including in Biophysics in 2008 and Theoretical Biology in 2009, followed by positions in Bioinformatics and Immunology. He serves as a visiting fellow at the Garvan Institute for Medical Research and at Weill Cornell College of Medicine, New York, USA. As Principal Investigator of the UNSW Future Institute of Cellular Genomics, he oversees initiatives investing $4.5 million over seven years in single-cell technologies.

Luciani's research adopts a systems immunology approach to dissect T cell responses, developing immunotherapies for cancer and autoimmune diseases. His interdisciplinary work combines experimental immunology with computational tools, including single-cell genomics, multi-omics, machine learning, and bioinformatics. Focus areas encompass antigen-specific T cells in viral infections like influenza and hepatitis C, immunopathogenesis of coeliac disease, CAR T cell therapies for blood disorders and solid tumors, and immune dynamics in ageing. He has produced over 140 peer-reviewed publications, featuring landmark contributions such as the VDJdb database of T-cell receptor sequences (2018) and its extension (2020), high-throughput single-cell sequencing methods (2019), and models of viral evolution in early hepatitis C infection (2011). Recent works address CAR T cell differentiation, rogue T cells in refractory coeliac disease (2025), and transcriptomic signatures in CD8 T memory stem cells (2026). Luciani has attracted over $20 million in funding from NHMRC, ARC, JDRF, NIH, and industry partners including BD Bioscience, supporting projects like immuno-genomics mapping of T cells and AI-driven epitope identification. His innovations include bioinformatic pipelines for integrating single-cell transcriptomes with antigen receptors, haplotype reconstruction from deep sequencing, and statistical models for mutating genomes, influencing public health strategies for viral control.