I am a Conjoint Research Fellow in Speech Pathology between the University of Queensland and Princess Alexandra Hospital in Brisbane, Australia. My background is as a speech pathologist and speech scientist. For the longest time, I have been fascinated with how we so effortlessly transform thoughts and ideas into words and sounds and can be instantaneously understood by those around us. For many experiencing neurological changes, however, their ability to communicate can change over night and what once was an automatic task becomes significantly challenging.
At the University of Queensland, I am building a research program that (1) investigates the brain mechanisms underlying acquired motor speech disorders, and (2) develops and evaluates evidence-based speech therapy options for those living with neurological disorders. I conduct both basic science and clinical research using a broad range of methods (e.g., acoustic, neuroimaging, computational modelling), and I am committed to conducting rigorous, transparent, and accessible science.
At Princess Alexandra Hospital, I facilitate research in the Speech Pathology department, specifically mentoring speech pathologists in building research capacity and establishing a stronger evidence base for their clinical practise. This work focuses on knowledge translation and implementation science; making real-world change in the healthcare setting.
PhD in Speech-Language Pathology, 2018
University of Toronto
BSc in Speech and Language Therapy, 2011
University College Cork

Purpose: Reproducibility is a core principle of science and access to a study’s data is essential to reproduce its findings. However, data sharing is uncommon in the field of Communication Sciences and Disorders (CSD), often due to concerns related to privacy and disclosure risks. Synthetic data offers a potential solution to this barrier by generating artificial datasets that do not represent real individuals yet retain statistical properties and relationships from the original data. This study aimed to explore the feasibility and preliminary utility of synthetic data to promote transparency and reproducibility in the field of CSD.
Method: Ten open datasets were obtained from previously published research within the American Speech-Language-Hearing Association ‘Big Nine’ domains (articulation, cognition, communication, fluency, hearing, language, social communication, voice and resonance, and swallowing) across a range of study outcomes and designs. Synthetic datasets were generated with the synthpop R package. General utility was assessed visually and with the standardized ratio of the propensity mean squared error (S_pMSE). Specific utility assessed whether inferential relationships from the original data were preserved in the synthetic dataset by comparing model fit indices, coefficients, and p-values.
Results: All synthetic datasets showed strong general utility, maintaining univariate and bivariate distributions. Six of nine synthetic datasets that used inferential statistics showed strong specific utility, maintaining inferential relationships from the original analysis. Specific utility was low in three datasets with hierarchical structures.
Conclusion: Findings suggest that synthetic data can effectively maintain statistical properties and relationships across a wide range of non-hierarchical data commonly seen in the field of CSD. Other approaches for hierarchical data need to be explored in future work. Researchers who use synthetic data should assess its utility in preserving their results for their own data and use-case.
To date, I have taught two courses to clinical MSc. students of Speech-Language Pathology:
I have also guest lectured in a number of Speech, Language, and Hearing Sciences programs — from undergraduate to Ph.D. level: