Genevieve Buckley

The Women in Machine Learning Program Blog Series Part 4 – Genevieve Buckley

We are publishing a blog series on the brilliant women we had involved in the Women in Machine Learning program last year.  

Whoever you are, and whatever your background we are sure you will find their thoughts on Machine Learning, inclusion and their stories inspirational and inspiring.  

Genevieve Buckley

As a data analyst with Monash University, Genevieve brings data science to biomedical research using her skills and background in physics, mathematics and communication. Genevieve sees machine learning as an increasingly important tool in a data scientist’s kit.

She is also very active in the community, presenting at the PyLadies meetup, as well as co-organising the 2019 Advanced Scientific Programming in Python summer school, a week long course that develops programming and tech skills for scientific applications.

What get’s you excited about Machine Learning, and what makes you want to learn more?

What’s exciting to me is the potential to unearth interesting relationships within data that just wouldn’t be possible with more classic statistical approaches. And harnessing that for predictive power is also pretty awesome.
 

What diversity initiatives would you like to see in the MLAI industry?

That’s a big question, and I’m not sure any one thing is going to be the answer.
I think I’d like to see more funding for programs that build skills or contribute to career development. I’d certainly like to see funding for this workshop to continue, I wouldn’t have been able to be there otherwise.
 

What was your favourite part of the Women in Machine Learning program, or what did you get most out of?

My favourite part was definitely the auto-encoder project. It was very relevant for me, in particular in terms of data compression. Plus, we got to make these very weird looking emojis as you can see below. 
(Image: Genevieve Buckley)

What is next for you on your Machine Learning journey?

I’ve been working on an application recently that uses unsupervised learning for microscopy, and would like to extend it. This kind of tool is especially useful for health research, identifying new candidates for drug development.
 
I’m particularly interested in weakly supervised or unsupervised learning, things that allow us to cope with the messiness of the real world.

 

What advice do you have for anyone from an underrepresented group in entering a career in tech?

I’ve got a lot out of meetup groups specifically for underrepresented groups, it’s been a really good way of finding community for me. If you’re looking for somewhere to start, I can recommend both PyLadies and the Women in Machine Learning and Data Science groups in Melbourne (come say hi!).
 
 

Do you or your company what to support the Women in Machine Learning  program in 2019? Register your interest here. 

We will be opening applications for the Women in Machine Learning  program later in 2019. Register your details here to be amongst the first to apply.

Interested in a career in Machine Learning? We are hiring, check out our current openings here.  

Did you enjoy reading Genevieve’s post? Check out Part 2 with Ayesha and Part 3 with Ashley.

Share this post

Leave a Reply

Your email address will not be published. Required fields are marked *