The first thing I ever published that attracted any real attention was just after I finished my PhD. I started writing the puzzle page for Actuaries Magazine as a way of filling my suddenly empty weekends. I didn’t expect it to lead to anything. Yet, in the years that followed, people would walk up to me at conferences and want to shake my hand because I was “the puzzle girl”. Once someone even offered me a job because of it. Prof Rob Hyndman, one of the world’s leading applied statisticians,...
6 days ago • 1 min read
Even at the best of times, the world is an uncertain place. Right now, we are far from the best of times. In periods of high uncertainty, people naturally seek out anything that might reduce that uncertainty - even just a little. Politicians understand this instinctively, which is why we've seen so many world leaders addressing their people in recent weeks, with varying degrees of success. The greater the uncertainty, the greater the value of any reduction in it. This principle doesn't stop...
9 days ago • 1 min read
When I started my career, data science didn’t exist as a field. I trained as an actuary and statistician and those were the tools I relied on in my earliest roles. Then, around 10 years ago, I started hearing about the wonders of machine learning and became worried that my traditional training was no longer enough. So, despite already having a PhD in Statistics, I went back and completed a Masters in Machine Learning. Then came the AI wave – ChatGPT, large language models, generative AI – and...
13 days ago • 1 min read
The most valuable lessons I’ve learned in my data science career weren’t learned in a classroom. They came from conversations with people who’d already figured things out the hard way. My podcast has been a more valuable learning tool for me than all of my university degrees combined. Over 100 episodes, I’ve had the chance to speak one-on-one with some of the sharpest minds in the industry - CEOs, best-selling authors and leading researchers - on everything from cutting-edge AI to what it...
16 days ago • 2 min read
In 2015, I fell in love with a job I would never have. I’d just attended a conference where people were talking about machine learning and data science as the way of the future. I returned to the office eager to learn more and started down the data science rabbit hole - where I stumbled across an article about the recently established NYC Mayor’s Office for Data Analytics. They were using data science to locate illegal cooking oil dumping in the city’s sewers. To coordinate emergency services...
20 days ago • 1 min read
The first time I ever presented my work in public was at a finance symposium when I was 27. I was close to submitting my PhD thesis and my supervisor offered me the opportunity as a supporting speaker to a renowned international mathematical finance researcher. I was the final speaker of the day. By the time I took the podium, almost everyone had gone home. Fewer than 10 people remained in the room. But the researcher was still there. Afterwards, I headed to the airport, and ran into him in...
about 1 month ago • 1 min read
Biased machine learning models don’t just produce poor predictions. They damage reputations, derail projects, and in high-stakes fields like healthcare, they can potentially cause real harm. Yet most data scientists don’t check for bias until it’s too late - missing the opportunity to address it at its source. Serg Masis, author of Interpretable Machine Learning with Python, puts it bluntly: “Models magnify bias just simply by the way they are. It’s like when you make a caricature of someone...
about 1 month ago • 1 min read
“Cheating with artificial intelligence is now rampant at universities.” “University is no longer a test of your intellect. It’s a test of how well you can instruct Chat GPT.” “AI Is giving students top grades for zero intellectual work.” These are quotes from a recent article in The Australian Weekend Magazine, which argues that students are now turning to AI en masse to automate learning, and graduating with perfect grades but limited knowledge. The phenomenon has been observed across...
about 1 month ago • 1 min read
"Because the algorithm said so” isn’t good enough anymore. When your machine learning model makes a decision that affects someone’s medical treatment, financial security, or legal rights, stakeholders need to understand why. I first encountered interpretable machine learning working in insurance - though I didn’t realise it at the time. The insurer I worked for used machine learning models as part of its premium calculation process. There was an unwritten rule that any models we deployed had...
about 1 month ago • 1 min read