Julia Handl

Research Group(s)

• Data Science Institute

I am a Professor in Decision Sciences in Alliance Manchester Business School (AMBS) and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). Prior to my appointment in AMBS I held an MRC Special Training Fellowship in Bioinformatics in Manchester's Faculty of Life Sciences at the University of Manchester, spending six months as a visiting researcher at the University of Washington, Seattle (2007-2011).

I hold a PhD degree from the Manchester Interdisciplinary Biocentre at the University of Manchester (2006), a Masters degree in Computer Science from the University of Erlangen-Nuremberg (2003), and an Honours degree in Computer Science from Monash University, Melbourne (2001). My Masters thesis was completed at the Artificial Intelligence Research Laboratory (IRIDIA) of the Université Libre de Bruxelles. My studies abroad were funded through scholarships of the German Academic Exchange Service (DAAD) and the Gottlieb-Daimler and Karl-Benz Foundation.

I have, as sole or joint PI, won research funding > £1m, and held a range of leadership roles within my research community and at the University of Manchester, most recently (2018-2021) acting as Associate Head of Division for the Management Sciences and Marketing division (Teaching), and (since 2021) as the Faculty of Humanities' e-research lead.

I have experience working as a researcher in all three of Manchester's Faculties, and of engaging in knowledge exchange and consultancy with industry partners from a variety of domains. A computer scientist at heart, I am a passionate advocate of interdisciplinarity in research, and the potential of abstraction and mathematical and computational techniques to transcend traditional discipline boundaries.

My core methodological expertise lies at the interface between optimization and machine learning. I am best known for my work on unsupervised learning, and the transfer of concepts and techniques from multi-criteria optimization, evolutionary computation and multi-criteria decision making (MCDM) to cluster analysis, in particular.