David Ascher

Lab Head

Douglas Pires

Baker Institute and School of Computing and Information Systems

Azadeh Alavi

Research Officer

Application of machine learning to clinical data.

Thanh-Binh Nguyen

Research Officer

Current research interests are computational biology, particularly, the integrative study of proteomics, protein structures, and their functions. Based on available structural information of proteins, I would like to develop bioinformatics tools to predict the function properties of uncharacterized proteins. I also would like to spend my experience on virtual screening to identify binders of enzymes that are related to particular diseases.

Moshe Olshansky

Research Officer

Statistical analysis of 'omic data

Raghad Al-Jarf

PhD Candidate

Project: Using machine learning to improve our understanding, and personalizing treatment of cancer.

Malancha Karmakar

PhD Candidate

Project: Integrating structural and epidemiological modelling to identify which Tuberculosis resistance mutations are likely to arise in a population.

Bruna Moreira

PhD Candidate

Yoochan Myung

PhD Candidate

Project: Using graph-based signatures to guide antibody engineering and epitope identification.

Stephanie Portelli

PhD Candidate

Project: The structural characterisation of drug resistance in infectious and non-infectious diseases.

Carlos Rodrigues

PhD Candidate

Project: Characterising the molecular properties of protein-protein interaction interfaces in order to understand the role of disease mutations and better guide development of PPI modulators.

Michael Silk

PhD Candidate

Project: Using population genetic diversity to characterise the mutational tolerance of a given gene, to identify pathogenic variants and structurally and functionally important regions of the protein.

Joao Velloso

PhD Candidate

Project: Computational guided GPCR engineering and drug development.

Joicymara Xavier

PhD Candidate

Project: Investigation of the interaction between multiple mutations on protein stability and function.

Mengyuan Shen


Predicting the pathogenicity and patient outcomes of BRCA1/2 mutations.

Parth Trehan


Using machine and deep learning to process medical imaging data. Building new tools for improved automated analysis and diagnosis from medical images.

Ted Airey


Structural characterisation of ALS disease mutations to predict disease progression.

Elston D'Souza


Identification and characterisation of proteins under different selective pressures between ethnic populations.

Noa Levi

Undergraduate Researcher

Sophia Muller-Dott

Undergraduate Researcher

Lucy Barr

Research Visitor

Zachary O'Brien


Analysis of ICU data

Marialena Michanetzi

Marialena did her Masters of Bioinformatics project with us in 2017-2018, looking at the structural characterisation of Mycobacterium tuberculosis streptomycin resistance variants in gidB.

Mi-Chi Lee

Mi-Chi did her Masters of IT with us in 2019, combining machine learning with phenotypic screening to improve drug development.

Amanda Albanaz

Amanda did her undergraduate and Masters of Bioinformatics with us 2016-2018 looking at the structural characterisation of ALS disease mutations to predict disease progression. She is now pursuing her PhD at the University of Ostrava, Czech Republic.

Aaron Barnard

Aaron did his undergraduate project with us in 2018 looking at mapping population variation in malaria. He is now studying medicine at Melbourne University.

Vittoria Cicaloni

Vittoria joined us in 2019 to work on the structural analysis of genetic disease causing variants. She is now completing her PhD research programme at the University of Siena.

Anna Visibelli

Anna joined us in 2019 to work on an automatic modelling pipeline. She is now completing her PhD research programme at the University of Siena.

Anjali Garg

Anjalia completed her summer internship in the group in 2019. She developed a method to predict antimicrobial peptide activity. She is now looking forward to starting her PhD in the US.

Hardik Parate

Hardik worked with the group in 2019 to develop a method to map the drugability of protein surfaces.