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Research statement
My research interests are in the development of applied decision science and data science capabilities with a focus on domains such as healthcare, legal and public services, manufacturing, and management. Most of my work is interdisciplinary and collaboratively involving both academic colleagues from different fields (e.g. Computer Science, Music, Sports, Forensics) and external partners including companies from different sectors, the government (IPA), and public (e.g. NHS Digital and hospitals) and legal services (e.g. Weightmans). I have generated research income worth a total of more than £1m attracting funding from sources such as ESRC, Cabinet Office (IPA), EPSRC, Innovate UK, HSIF, and external partners.

Below is a summary of research streams I have been pursuing since 2008, which is when I started with my PhD studies.

Non-standard challenges in experimental optimization
  • Ephemeral resource constraints (ERCs): Together with Joshua Knowles I have been working on a type of dynamic constraints that simulated the availability of resources required in the evaluation of a solution. Here, evaluations refer to physical experiment and/or a time-consuming simulation. For example, using a particular instrument setting may commit us to using this setting for a certain amount of time, or a machine may break down and thus become unavailable termporarily, or an expert may be required to operate with certain instrument settings (e.g. due to safety concerns) and she may be available on certain days of the week only. ERCs define the set of solutions evaluable at any point in time but they do not change the global optima. We introduced and formally defined different ERCs based on our own observations in collaborative experimental work, analyzed some ERCs from a theoretical point of view using Markov Chains, proposed and analyzed different static strategies and reinforcement learning-based strategies for dealing with ERCs, and augmented optimizers with on-line purchasing strategies for resources.
  • Objectives with non-uniform latencies: In experimental multiobjective optimization, it does not need to be the case that all objectives take equal amount of time to be evaluated. For instance, developing a new washing powder that is cost-efficient and performs well may require quick evaluations of the cost (summing up the ingredients) but time-consuming washing cycles at different temperatures, loads, and clothing types. I have kicked off my work on this topic with Joshua Knowles introducing non-uniform loosly and proposing and analyzing simply strategies. Julia Handl then joined us in a follow up paper where we provided a more formal introduction to non-uniform latencies, and proposed and analyzed a set of new strategies. Together with Kaisa Miettinen, Tinkle Chugh, and Vesa Ojalehto, I have now also proposed and analyzed surrogate-assisted optimization methods to deal with non-uniform latencies in objectives.
  • Changing decision variables during search: The significant demand in resources (e.g. time, money, labour) combined with time pressure to deliver results can lead to situations where an experimentalist wants to change the objects being optimized, and since restarting optimization would be too expensive and wasteful (in many cases) it is desired to do this on the fly (i.e. during the optimization). For instance, in a drug discovery problem where one aims to discover potent combinations of drugs drawn from a drug library, one way want to add, replace of remove drugs from the drug library. I have been looking at precisely this scenario together with Joshua Knowles and colleagues from the Manchester Institute of Biotechnology (MIB) including the current President and Vice-Chancellor of the University of Manchester, Dame Nany Rothwell, and the former CEO of BBSRC, Douglas Kell. Our work has resulted in an experimental/methodoligical-focussed paper, published in Nature Chemical Biology, and an optimization-focussed paper.
  • Optimization in lethal environments: In this work we looked at scenarios where the evaluation of a lethal solution (modelled here as one below a certain fitness threshold) causes the solution to be immediately removed from the population and the population size to be reduced by one. This models certain closed-loop evolution scenarios that may be encountered, for example, when evolving nano-technologies or autonomous robots that are limited in numbers, thus destroying a robot means to lose it for good. Together with Joshua Knowles we have motivated such scenarios, and proposed and analyzed methods to cope in lethal environments.
Many of the work packages discussed in this research stream have been initiated during my PhD studies and are covered in more or less detail in my PhD thesis.

Multiobjective optimization and MCDM

Surrogate-assisted optimization

Multiobjective clustering
  • Adaptive encoding for evolutionary data clustering: Together with my PhD student Cameron Shand, Julia Handl and John Keane, I have been looking at adaptive encodings for optimization approaches applied to data clustering. The motivation behind this research is to create optimization algorithms that are capable of adjusting autonomously to dataset at hand. In particular, our research investigated the adaption of a specific hyperparameter - which directly governs the encoding granularity - in the multi-objective clustering algorithm MOCK. Our proposed approach leads to a ~40% reduction of computational expense while achieving clusterint performance that is at least as good as achieved for an optimal (a priori) setting of the hyperparameter. Our paper can be downloaded here.
  • Test problem generator for clustering (HAWKS): Together with my PhD student Cameron Shand, Julia Handl and John Keane, I have been working on a new data generator -- which we called HAWKS (derived from the authors' last name initials) -- that uses an evolutionary algorithm to evolve cluster structure of a synthetic data set.We demonstrate how such an approach can be used to produce datasets of a pre-specified difficulty, to trade off different aspects of problem difficulty, and how these interventions directly translate into changes in the clustering performance of established algorithms.

Problem generators
  • Distance-based many-objective test problem generator: In an international collaboration with Jonathan Fieldsend and Tinkle Chugh (both University of Exeter, UK), and Kaisa Miettinen (University of Jyvaskyla, Finland), I have developed a feature rich distance-based many-objective visualisable test problem generator. The goal of this work was to promote the use of distance-based test problems by providing an automatic problem generator to create distance-based problems subject to a range of problem characteristics including variable density of solutions in objective space, landscape discontinuities, varying objective ranges, neutrality, and non-identical disconnected Pareto set regions.
  • Test problem generator for clustering (HAWKS): Please refer to the Multiobjective clustering section for an overview of this exciting work.

Drug development and manufacture
During my Postdoc at the Biochemical Engineering Department at UCL (2011 - 2015), I had the opportunity to work on a range of interesting problems related to the development and manufacture of drugs. I continue to work on these and other related topics if the opportunity arises. Below is an overview of my work in this research stream.
In addition to drug development and manufacture, I am working on a range of other applications: