Open-Source Research Software Panel Discussion

ACEMS

Presented by the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and the Australian Research Data Commons (ARDC)

Research Software . . . Why should I care? 

Are you a statistician, an applied mathematician, a computer scientist, or an engineer? 
Does your work involve producing new computational methods? 
Are you making these new contributions available as software? 
You might be saying “Yes” to the first two questions, but think “Why should I care about research software?” Well. . . an obvious reason is because it is a pathway to impact. Or you might be wanting to make research software but have some doubts. 

In this ACEMS panel discussion we will chat about open-source software development and help promote the idea of researchers producing software. We will have a wide-ranging discussion on how it benefits researchers and others, effect on career development, research impact via software, what can be done to increase uptake and usability and getting recognition for this work. 


This panel discussion will be about 45 minutes, followed by a Q&amp;A session.NOTE - click this <a href="https://monash.zoom.us/j/86797514938?pwd=MHdkRHhrY2NRZEV4c1dtWkdLZ2hhZz09" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">link</a> to join the webinar on the day (Password: 116026).

<h3>About the Speakers</h3>Dr Tom Honeyman is a Software Program Manager at the Australian Research Data Commons (ARDC), a transformational initiative that enables the Australian research community and industry access to nationally significant, leading edge data intensive eInfrastructure, platforms, skills and collections of high-quality data. In his role as Software Program Manager, he is looking to open up a new area for the ARDC in working to make research software a first class research output.Professor Dianne Cook is an ACEMS Associate Investigator and Professor of Business Analytics in the Department of Econometrics and Business Statistics at Monash. She is a Fellow of the American Statistical Association, and Ordinary Member of the R Foundation (elected). Her research is primarily in data visualisation, visualising high-dimensional spaces using tours with projection pursuit, and bridging the gap between statistical inference and exploratory data analysis. She has developed visualisations for virtual environments, used eye-trackers for assessing visual perception. The applications include bioinformatics, ecology and sport.Dr Sevvandi Kandanaarachchi is an ACEMS Associate Investigator and an interdisciplinary researcher working in data science and analytics. She uses mathematics, statistics, and machine learning for her research. Her research interests include anomaly and event detection with applications in complex data, and algorithm evaluation. She likes working on real world problems, especially ones that are motivated by industry. Professor Dan Simpson is the Director of Analytics Engagement and a Professor in the Department of Econometrics and Business Statistics at Monash University, and an ACEMS Associate Investigator. His research interests include spatial and spatiotemporal models with computationally efficient Gaussian random fields (Gaussian processes); methods for building and assessing Bayesian models, including prior specification, model criticism, and model checking; computational statistics, including approximate inference methods (such as INLA), Markov chain Monte Carlo methods, and methods for assessing the quality of approximations to posterior distributions; and applications of statistics in environmental science (specifically modelling air quality) and global health.Katie Buchhorn (MC) is an ACEMS PhD candidate at Queensland University of Technology, working in the field of applied Bayesian statistics. The context she is working in is environmental, specifically, water quality in river networks (and how we collect samples using sensors). The research topic involves Bayesian adaptive design, which is a framework, for optimising over where, when and how many of these sensors to place around the river network such that information retrieval is maximised.

Open-Source Research Software Panel Discussion

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Open-Source Research Software Panel Discussion

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