## An Interview with Me (Alex Antic) – The Analytics Show

It was great to be featured on The Analytics Show podcast. I really enjoyed my chat with Jason Tan, in which we discussed my somewhat unusual career trajectory (which resembles a Gradient Descent!), my current research and teaching focus, and the new Software Innovation Institute that I’m helping set up at the The Australian National…

## So you’ve landed your first Data Science role, now what?

How exciting, after all that study, spending umpteen hours watching MOOC videos, and praying at the Andrew Ng statue you have in your room, you’ve finally landed your first illustrious role as a sexy Data Scientist, congratulations! Hmmm… now what? To get you started on your journey, I’ve compiled some wisdom that I often share…

## Why you should do all your Machine Learning/Deep Learning in Pascal

I know what you’re thinking, “not ANOTHER post telling me which programming language to use!“ Don’t worry, I plan to discuss the opposite actually i.e. why you shouldn’t be so concerned about which ‘tool’ you’re using, and in addition, I’ll offer three pieces of essential advice. There’s a great deal of obsession throughout the Data…

## Why every Data Scientist should know how to Shoot an Azimuth!

Here is a lecture that I recently presented on the topic of Link Prediction in Networks as part of a Network Science course

## Exponentially Logarithmic – Lifting the Lid on some Bloom Filter Derivations

In one of my previous posts on Bloom Filters, I stated the expressions for both the False Positive rate, ie $$p \approx \left( 1 – e^{\frac{-kn}{m}} \right)^{k}$$ and the optimal number of hash functions, ie $$k = \frac{m}{n} \ln 2$$ In this post I will detail the derivations of both expressions. Derivation of the False…

## Bridging the Graph

I’ve been lucky enough to have worked on many interesting and challenging projects throughout my career, especially in the space of detecting “bad” people. As one can imagine, much focus is placed on detecting persons of interest in the government sector. After all, you can’t ensure national security, for instance, without being able to detect…

## In Bloom – Nirvana?

I recently discussed how Confidential Computing allows us to analyse and use data without actually seeing it. A key component of Confidential Computing is Privacy-Preserving Record Linkage (PPRL), and one of the most widely used techniques within PPRL is the Bloom Filter (BF), which is the focus of this post. The aim is to give an overview of…

## How to Detect, Deter and Disrupt Crime & Fraud using Confidential Computing

Blame it on misguided youth, blame it on greed, or blame it on just plain ignorance, but I have a confession to make: I spent the early part of my career working for a major investment bank. To be honest, it was a great opportunity as a young Data Scientist to learn how to apply…

## Interview with Felipe Flores (Founder of Data Futurology)

Felipe Flores is the founder and podcast host of Data Futurology, a podcast targeted at helping Data Scientists become successful leaders. As the former Head of Data Science at ANZ bank, and a former consultant, he shares some incredible insights, and offers valuable advice, to Data Scientists at any stage of their career. You recently…