Nemania Borovits, MSc is a PhD Candidate in Data engineering an AI for Privacy. His research centers around implementing the Privacy by Design framework within Big Data architectures, privacy-preserving machine and deep learning and privacy risk assessment for AI solutions. Additionally, he has made contributions to the Infrastructure as Code domain by using machine and deep learning for defect prediction.
Nemania’s research effectively trains models while safeguarding privacy-sensitive data in an effort to allay society concerns about the transparency and fairness of AI systems. Furthermore, it directly impacts the business world. His work, which he is conducting in conjunction with KPN, aims to develop AI models that preserve and safeguard privacy.
JADS also participates in several (international) projects, which contribute to grand societal challenges like health, food security, smart transport and secure societies. Together with companies, government, NGO’s and other knowledge institutions, JADS works on solutions by using data.