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The Women in Machine Learning Program Blog Series Part 3 – Ashley Anderson

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.  

Ashley’s career has progressed from Molecular Cell Biology to Bioinformatics, with a significant detour teaching English in Japan for 3 ½ years. She is currently studying a Master of Bioinformatics while working as a wet lab assistant at a cancer research laboratory. Ashley is also very active with Pyladies Melbourne, both as an attendee and presenter.

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

I’m excited about the vast array of applications that machine learning has. It seems like the only limit is your imagination. Currently in biology (and bioinformatics, for that matter) we’re generating data faster than we can analyse it. There is a lot of great work that is being done in the biology/bioinformatics sphere, but I think that using machine learning techniques to dig through the data we have might help uncover patterns that we never would have found otherwise. MLAI could help us generate new hypotheses, and potentially help find causes or treatments for disease, and improve medical diagnostics. Some groups have already started using these techniques, and I want to be a part of that research, too.

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

I think WiML is a great example of a diversity initiative in the MLAI industry. Attending an inclusive workshop made it easier to get started, because I knew I was going to be amongst a supportive group of people who would work together to push everyone up, rather than compete with them. Likewise, I think that having similarly targeted meetup groups and networking events would encourage more people from underrepresented groups to step into, or stay in, the MLAI industry.

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

I thought the entire program was great. If I had to choose one part that I gained the most from it would be working in a team to develop a neural network from scratch. We were really thrown in the deep end, but with the support of the Women in Machine Learning mentors and a great team to work with, we were able to build a VGG image classifier. Working out the network structures and code as a group helped cement the content we learned the previous day, and I really enjoyed the challenge.

What is next for you on your Machine Learning journey?

Coming from a biology background, I didn’t learn many of the computational or mathematical skills involved in MLAI during my degree or my career. I’d like to develop some strong MLAI skills, so I’m currently working on building my knowledge from the ground up. I’m trying to learn some foundational linear algebra and calculus, and I’ve also started taking some online courses in machine learning. I have a few ideas on how I would like to use MLAI in my research, and I’m currently working on building the skills I need to gain new insights into my data.

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

I think that’s a difficult question to answer because different underrepresented groups face vastly different obstacles. In general I’d say that if you want to do something, try it. Even if you’re not sure whether or not you’re capable, or good enough. Learn what you can, ask questions when you have them, and get involved in tech-related groups outside of work. I’ve found that most people I’ve encountered have been welcoming and supportive, and I’ve learned far more than I thought I ever would.

Tell us a little about Bioinformatics. What is it and what makes you fascinated by it?

Bioinformatics is a field that uses statistical and computational tools to solve biological problems. The problems that bioinformatics tries to solve are extremely broad, so it’s difficult give a succinct definition. I think that the most widely known applications for bioinformatics are the development of computational tools that analyse an individual person’s genetic code. Bioinformaticians can then analyse those codes across whole populations to identify genes associated with disease (or other traits). Bioinformatics also contributes to so much more. Much like MLAI, bioinformatics has a vast array of applications, and the potential to make a huge impact on the lives of many people. Part of why I love it is that bioinformatitions and biologists work together to achieve a common goal, and that goal is always to make a positive contribution to the world.

Do you or your company what to support theWomen 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.  

Have you read Ayesha’s post in part 2? You can do so here

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