Rate My Professor Natasha Fernandes

Fernandes' research centers on the mathematical foundations of data privacy, specializing in differential privacy, metric differential privacy, quantitative information flow techniques derived from information theory, and privacy-preserving mechanisms for natural language processing and machine learning. Her notable publications include 'Generalised differential privacy for text document processing' (2019), 'Explaining ε in local differential privacy through the lens of quantitative information flow' (2024), 'Universal optimality and robust utility bounds for metric differential privacy' (2023), 'Locality sensitive hashing with extended differential privacy' (2021), and 'How to develop an intuition for risk... and other invisible phenomena' (2022). She has received the John Makepeace Bennett Award for the best Computing PhD dissertation in Australia and New Zealand (2022), the Faculty of Science and Engineering Excellence in HDR Award (2021), Vice-Chancellor’s Commendation for PhD Dissertation (2021), HDR Rising Star Award (2021), Cyber Security MRes Computing Prize for Academic Excellence (2017), and University Medal for Computing (2018). Fernandes teaches units such as Offensive Security, Secure Applications Development, and Digital Forensics, and affiliates with the Data Horizons Research Centre, Future Communications Research Centre, and FinTech and Banking Research Centre. Her work develops mathematical principles and software tools to analyze and enhance privacy in secure systems.