Rob Romijnders
As a final-year PhD student at the University of Amsterdam, Rob Romijnders specializes in privacy-aware machine learning. Two research projects have been awarded oral talks by academic conferences such as AAAI and ICLR. Having worked at a startup and two AI companies, Rob is excited to be back at PyData and make academic topics applicable to the PyData community!
Sessions
With AI becoming more common, there’s a growing need for privacy in our data processing algorithms. Differential Privacy (DP) is a popular way to quantify privacy loss and has been adopted in many applications. Examples include the Android keyboard learning about user typing, Apple’s system to collect statistics on emoticons and website usage, and the US government releasing Census population statistics. We’ll discuss in this talk an intuitive and tangible definition of differential privacy and how the above examples implement DP. In an IPython notebook, we'll demonstrate the effects of Differential Privacy on a small-scale data science problem. Additionally, I’ll refer to Python repos for doing differential privacy at scale, including for deep learning.