During the winter school Rory Smith (Monash University) ran a series of lectures on 'Learning from Data, the Bayesian Way'. The goal of these lectures was to take a Bayesian look at statistically optimal ways to detect and extract information in noisy data. This lecture series addressed a range of Bayesian related topics from inference and parameter estimation to sampling methods and hierarchical inference. Coming from a pure mathematics degree these lectures appealed to me the moment I saw them in the schedule. An early slide in the course read "18th Century Insights into 21st Century Problems". This really resonated with me. My PhD is an interesting hybrid between the application of mathematics into modern issues such as poverty and development. Often in my literature review I have come across papers from over 100 years ago and yet the mathematics not only still hold but is fundamentally routed and often unchanged in work done today. There is something beautiful about this. It's like visiting an old cathedral and admiring the strength of large pillars which have stayed standing through years of weathering and generations of visitors. 

Bayesian methodology is a statistical tool introduced by Rev.Thomas Bayes in the 18th century, yet it is vital in providing solutions to a variety of statistical issues and problems presented by researchers today. It's incredible! Bayes approach can be used to compliment a range of statistical methods and I would recommend researchers from any field look into learning some basic Bayesian statistics to see where they could be used in their work.  

Another thing which stood out to me at this event was accessibility. Big data is such a broad topic and is applied in so many fields now that scoping an event tailored to all these fields is inevitably difficult. People attending this conference ranged from pure data scientists and statisticians with intricate knowledge of a range of big data areas, to people from humanity schools looking to learn what big data is to apply it to their work. Knowing this I was pleased to see that the schedule of the events include a vast range of understanding requirements for different talks. Everyone was able to pick talks which suited their needs. Many of the talks though-out the week were however given as a follow on to previous talks in the week on the same topic. The meant that there were some big leaps within and in between talks. You could sit in an hour long talk and spend the first 20 mins feeling like you were not learning anything, then be totally lost by the last 20 mins. I think this is an inevitable part of interdisciplinary work, there will be moment when you feel things are too basic and moments of overwhelming confusion. This can really feed into imposter syndrome. What I did like about this event is that there were ample resources online provided after each talk which pointed people in the right direction both for learning the basics and extending topics to more complex levels. PhD can be very isolated, you work on a tiny specific area of what you do, and often you are the only one doing it. Sometimes you might read a paper or article which leaves you totally baffled and not even knowing where to look up the information you would need to understand what the paper was talking about. Events like this help to combat these issues of accessibility and imposter syndrome - they unite people and present an opportunity for experiences to be shared and questions to be asked.