Julia Handl obtained a Bsc (Hons) in Computer Science from Monash University in 2001, an MSc degree in Computer Science from the University of Erlangen-Nuremberg in 2003, and a PhD in Bioinformatics from the University of Manchester in 2006. From 2007 to 2011, she held an MRC Special Training Fellowship at the University of Manchester and the University of Washington, and she is now a Senior Lecturer (Associate Professor) in the Decision and Cognitive Sciences Research Centre at the Alliance Manchester Business School. Her research includes theoretical and empirical work related to the development and use of data-mining and optimisation approaches in applications ranging from protein structure prediction over market segmentation to healthcare settings.
Julia's research focuses on the interface between data science and optimisation, with an emphasis on the design of smarter, data-driven meta-heuristic optimisers. Traditionally a data-poor discipline, the appropriate integration of available data into non-linear global optimisation techniques (specifically meta-heuristics) is an increasingly pressing problem. The problem-specific customisation of meta-heuristic optimisers is an essential step for focusing the search and achieving scalability of standard of-the-shelf techniques to large-scale industrially relevant problems. Historically, this would have been achieved through the careful manual, design of various meta-heuristic components by an optimisation expert.
The automatic integration of data offers an alternative approach and promises the opportunity to move beyond the capabilities of a human in identifying and capturing complex relationships, and integrating this information fully into the search process. Relevant data in an optimisation setting may arise in a variety of forms including e.g. historical data capturing observed co-variation between pairs of decision variables, data describing previously seen distributions of individual variables, or collections of candidate solutions for simplified or different instances of the problem.
Julia's research will deliver novel methods for data-driven optimisation, and thereby assist the Institute's agenda of driving advances in fundamental data science methodology. The development of these approaches is intrinsically linked to applications, as the nature of the data that can be exploited during optimisation will vary by problem. Understanding and fully defining generalisable classes of data features that can support data-driven optimisation, and designing suitable methodologies for each of these classes, is therefore key. The value of the resulting, general methods will be illustrated using selected applications. Applications of data-driven optimisation arise across a range of challenge areas, especially science, engineering and healthcare.