Hello world !!!
Welcome to my personal blog where I love to share what I’m learning and researching through my PhD and also as a Biostatistician (at University of Sydney).
I always love to improve the world wide quality of living through data. But you may wonder how? Simple answer is, If we want to know what’s really happening around us, we have to learn statistics.
Here I will share my learning and the experience which I’m gathering along my journey. I really hope this will also help you in your data related endeavors.
Well then this is my learning curve !!!
Let’s start digging.
Linkedin Snap shot of who am I
Researchgate Profile of what I’m researching
I’m a statistician specialized in biostatistics and data science.
I’m currently working as a biostatistician at National Health Medical Research Council (NHMRC) Clinical Trial Center, University of Sydney.
I have completed my bachelor’s degree in Statistics at University of Colombo, Sri Lanka. After a year of working as a data analyst/scientist in Sri Lanka, I was offered a full scholarship from Department of Health Science and Biostatistics at Swinburne University of Technology Australia to follow a Ph.D. in Statistics (STA90001) in the scope of Health Science (DR-HTHSCI), under the supervision of Prof. Denny Meyer and Dr. Madawa W. Jayawardana.
My Ph.D. project was named as “Innovative Statistical Methods to Model and Evaluate Physical Activity Programs Engagement”, which comprises statistical and machine learning models to model mHealth physical activity programs. This Ph.D. project was done in collaboration with a US based world leading wellbeing program which is known as “Virgin Pulse Global Challenge”. The findings of the Ph.D. were well published in leading academic journals and international conferences.
During my Ph.D., I have consulted over 1000 students in advanced statistical modelling, time series analysis, machine learning, business forecasting and predictive analytics at three ranking universities namely Monash University, Melbourne University (MBS) and Swinburne University of Technology, with an overall rating of 4.7 out of 5.
I have worked with Department of Transport, Victoria and Peter MacCallum Cancer Research Centre, Melbourne on various studies to solve real world problems. The findings of these studies have been published in leading academic journals.
I enjoy myself working on real world health problems using data and statistics.
Download my resumé.
Ph.D. in Statistics, 2021
Swinburne University of Technology
B.Sc. in Statistics, 2016
University of Colombo
Apply and develop statistical methods to problems arising from clinical and biological research.
Perform statistical analysis of clinical trials and associated data (meta-analyses, data linkage and genome wide association studies) using various statistical packages.
Collaborating with clinical trials research to improve health care practice.
Investigating risk factors associated with suicide in adolescents and young adults (AYA) with cancer Responsibilities include:
Funded by a VicRoads tender, within the Centre for Mental Health, which sits in the Health and Biostatistics department under Swinburne Research. This project has established the predictive value of offence data as a proxy measure of future crash risk and identified the important characteristics, license and offence histories of drivers with high future fatal and serious injury crash risk Responsibilities include:
This study will be focused on the physical activity module of a workplace health and exercise program in which the participants track their day to day physical activity using step counts. The current system of flagging step entries uses an arbitrary value in order to identify fraudulent step entries. This cutoff is not a personalized value although the physical activity of participants is likely to vary depending on various psychosocial, demographic, weather, and climatic factors. Moreover, accepting and rejecting flagged step entries based on past performance and reasons provided by participants tends to be subjective. Furthermore, once a flagged step entry is rejected or accepted, these findings are not being taken into consideration in order to identify the genuineness of participants. It is expected that the proposed framework will overcome most of these issues.
According to recent statistics from the World Health Organization, 23% of people aged 18 years and over are not sufficiently physically active. At the same time online health, wellbeing and physical activity programs have become more popular. However, these online programs lack structured statistical and machine learning frameworks for enhancing engagement through program personalisation. This thesis fills this gap by developing a framework for monitoring and predicting future online engagement, including personalised achievable goals for daily step counts and systems for personalised participant intervention by administrators. It identifies new opportunities for online physical activity programs for producing better participant outcomes
For achieving successful results among employees at high risk of poor health outcomes remains a significant challenge for interventions. It is hoped that program developers can use this information to create effective interventions particularly for more sedentary employees.
The delivery of videoconferencing psychotherapy has been found to be an efficacious, acceptable and feasible treatment modality for individual therapy.
According to the global World Health Organization 2017 statistics, more than 80% of the world’s adolescents are insufficiently physically active. In response to this problem, physical activity programs have become popular, with step counts commonly used to measure program performance. Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance. This study reviews the statistical methods that are used to model and evaluate physical activity programs, using step counts.
Predicting drivers with fatal and serious injury crash involvement using their offence history