## Mathematical Modeller, Statistics & Probability Researcher, Data Scientist and Educator

shausan.aminath@gmail.com

CV (Academic) | CV (Industry) | Github | LinkedIn

I am currently a postdoctoral researcher at CSIRO, Australia.

Previous notable positions include postdoctoral researcher at the School of Mathematics and Physics and the School of Public Health of The University of Queensland. My advisors are A/Prof Yoni Nazarathy and Dr Amalie Dyda. My research projects included: Safe Blues, AI4PAN, and ID-NET.

I have also worked as a postdoctoral research at the School of Mathematical Sciences of Queensland University Technology and associate lecturer at the Maldives National University. My advisors at QUT were Distinguished Prof Kerrie Mengersen and Prof Chris Drovandi.

I received my PhD in 2015 from The University of Queensland, in the area of probability & statistics, focused on epidemic modelling. My advisors being Prof Phil Pollett and Dr Ross McVinish. My current research focuses on epidemic modelling, digital health, disease outbreak prediction, and natural language processing. I have also worked on Bayesian inference of disease dynamics, and applied data science.

My expertise is in epidemic modelling, applied probability, Bayesian statistics, data science and mathematical education.

Application areas of my work include epidemics, control of dengue spread, and educational game development.

### Courses and Workshops

I have been engaged with teaching courses at The University of Queensland since 2010 . See full course list. The main areas of my teaching at UQ are:

Data Science and Statistical Methods, and Practice: - this set of courses provide the fundamental techniques of data science, machine learning, statistical modelling and computation. Topics include linear models, generalised linear models, regularisation, Bayesian technique and process simulation, design thinking. Computational tools include Python, R, SQL, Hive, and Tableau.

Mathematics for Data Science: - this course concern the mathematical background required for understanding data science, statistics, and machine learning algorithms. Topics include calculus, linear algebra, discrete mathematics, and probability. Mathematica is used for programming.

Mathematics for Statistics: - this collection of courses deal the mathematical background required for understanding statistical inference, probability, and data analysis. Topics covered include, likelihood theory, confidence intervals, hypothesis tests, Bayesian inference, multivariate normal distribution, and general linear model inference. The programming language MATLAB is used for computation.

Probability and Stochastic Process: - this suite of courses deal with the theory of probability and stochastic processes, the application of stochastic processes for modelling real world phenomena, and simulation methods. The programming language MATLAB is used for computation.

Statistics for Health Science: - this set of courses deal with key research principles and approaches, designs and processes in health science as well as statistical modelling. Topics include qualitative and quantitative methods of data collection and analysis, linear models and generalised linear models. Computational tools include SPSS and R.

Introduction to Statistics - this collection of courses concern with introductory statistics for data scientists, engineers, and the general sciences. The programming language R is used for computation.

Introduction to Mathematics: - this suite of courses deal with the introductory mathematics for mathematicians, and the general science.

I have initiated and co-created the BRAG-Stan Workshop at Queensland University of Technology in 2019.

### Selected Talks and Prizes

Safe Blues: Emulation of epidemics via virtual safe virus-like tokens. UQ-AI Workshop Day, 2021, Brisbane, Australia. [second prize, poster]

I have been invited to give a talk at QUT-ACEMS Node Planning Day in 2019 . See slides of the talk

Contributed talk at Bayes on the Beach Conference in 2019, showcasing the application of Bayesian statistical inference in minimising dengue severity within humans hosts using Stan probabilistic programming language. See slides of the talk

I received the ANZIAM prize for an outstanding talk presented at QANZIAM Conference in 2012. See slides of the talk

I received the UQ-SMP prize for the best Mathematics poster presented at SMP poster day in 2011. Topic of the poster: The Spread of an SIS epidemic in a network

### Computer Language Skills

Julia

R

Python

Stan

Matlab

Mathematica

Maple

Java

HTML

### Licence and Accreditations

Data Science Professional Certification, IBM, 2020. [certificate]

Machine Learning A-Z: hands on R and Python, Udemy, 2018. [certificate]

Big Data Analytics, QUT, 2016. [certificate]

### Family and Community Service

My daughter, Iva, husband, Ahmed, and myself do organise quite a bit of Maldivian community gatherings in Brisbane.