Julia Neri (she/her)

School of Information, Journalism and Communication

University Teacher in Data Science

julia.neri@sheffield.ac.uk

The Wave

Full contact details

Julia Neri
School of Information, Journalism and Communication
The Wave
2 Whitham Road
Sheffield
S10 2AH

Profile

I am a University Teacher in Data Science at the University of Sheffield, where I teach across undergraduate and postgraduate programmes.

My academic background is interdisciplinary, combining the humanities, information science and computer science.

I hold a BA and MA in Ancient Literature and Archaeology from Sapienza University of Rome and an MSc in Digital Library Management from the University of Sheffield, where my dissertation received the annual prize for the best dissertation in Digital Libraries.

I am currently completing a doctorate in Computer Science on the organisational aspects of exploratory software testing in Agile environments.

My research and teaching interests include responsible data science, research methods, software testing, digital technologies, and the social, ethical and organisational contexts in which data-driven systems are developed and used.

Research interests

My research interests include exploratory software testing in Agile environments, particularly the organisational conditions that influence testing practices.

I am also interested in responsible data science, AI-assisted decision-making, data visualisation, digital libraries, and the ethical and societal implications of data-driven technologies.

I am a member of the AI cluster research group (IJC) and of the AI x Software Engineering & Testing (ASET) group (School of Computer Science).

Teaching interests: My teaching interests centre on responsible and ethical data science, research methods, software testing, data literacy, information systems, and the social and organisational contexts of technology. I am particularly interested in inclusive, participatory and interdisciplinary teaching approaches that help students critically evaluate the assumptions and consequences of data-driven systems.