Prof Emma Hart

Biography	Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems. She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 where she leads a group in Nature-Inspired Intelligent Systems, specialising in optimisation and learning algorithms applied in domains that range from combinatorial optimisation to robotics. Her work mainly involves development of algorithms inspired by biological evolution to discover novel solutions to challenging problems. She was appointed as Editor-in-Chief of Evolutionary Computation (MIT Press) in 2017. She has been invited to give keynotes at major international conferences including CLAIO 2020, IEEE CEC 2019, EURO 2016 and UKCI 2015 and was General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She is an elected member of the Executive Board of the ACM SIG on Evolutionary Computation. More broadly, she invited member of the UK Operations Research Society Research Panel, and in Scotland, co-leads the Artificial Intelligence theme within SICSA. She was appointed as a panel member for REF2021 (UoA11 Computer Science). In 2020 she was appointed to the Steering Committee that developed Scotland's AI Strategy published in 2021 . She has a sustained track record of obtaining funding from the EU, EPSRC and of engaging with industry via KTP projects and consultancy, and participates enthusiastically in public-engagement activity, e.g Pint of Science. Her work in evolutionary robotics has attracted significant media attention, e.g. in New Scientist, the Guardian, Telegraph and the Conversation. In 2021, she gave a TED Talk on Evolutionary Robotics, available online
Research Interests	Within the general field of Artificial Intelligence, my interests mainly lie in the area of Biologically Inspired Computing, in particular in Evolutionary Computing. I undertake research in two main areas: optimisation, and automated design and learning Within Optimisation, I am particularly interested in optimisation system which are able to continually learn over time to improve their own methods, for example improving their own code as they gain experience, and generate new algorithms for solving predicted future instances. This research combines technique in automated algorithm design and generation with state-of-the-art machine-learning methods. Much of my research has drawn on the hyper-heuristic paradigm as a practical method of solving optimisation problems encountered in the real world, e.g packing, scheduling and routing. Recently, I have been investigating ensemble methods for tackling these problems, where I'm interested in the trade-off between diversity and quality in forming an ensemble, and related questions about how to measure algorithm diversity. Much of my current work focus on evolutionary and learning applied to robotics. I am currently PI on a large multi-institutional project that aims to produce an autonomous facility for fabricating robots on demand, designed by evolution, that mixes 3d-printing with the state-of-the-art in evolutionary and machine-learning methods to evolve the morphology and controllers of robots. More generally, I am interested in how artificial evolutionary mechanisms can be adapted to co-evolve the body and brain of a robot, and particularly the relationship between evolutionary processes acting on populations and learning processes that act on individuals. I’m also interested in the role that diversity plays in such systems, and how it can be harnessed to enable systems to adapt to changing environments.

Biography

Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems.

She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 where she leads a group in Nature-Inspired Intelligent Systems, specialising in optimisation and learning algorithms applied in domains that range from combinatorial optimisation to robotics. Her work mainly involves development of algorithms inspired by biological evolution to discover novel solutions to challenging problems.

She was appointed as Editor-in-Chief of Evolutionary Computation (MIT Press) in 2017. She has been invited to give keynotes at major international conferences including CLAIO 2020, IEEE CEC 2019, EURO 2016 and UKCI 2015 and was General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She is an elected member of the Executive Board of the ACM SIG on Evolutionary Computation.

More broadly, she invited member of the UK Operations Research Society Research Panel, and in Scotland, co-leads the Artificial Intelligence theme within SICSA. She was appointed as a panel member for REF2021 (UoA11 Computer Science). In 2020 she was appointed to the Steering Committee that developed Scotland's AI Strategy published in 2021 . She has a sustained track record of obtaining funding from the EU, EPSRC and of engaging with industry via KTP projects and consultancy, and participates enthusiastically in public-engagement activity, e.g Pint of Science.

Her work in evolutionary robotics has attracted significant media attention, e.g. in New Scientist, the Guardian, Telegraph and the Conversation. In 2021, she gave a TED Talk on Evolutionary Robotics, available online

Research Interests

Within the general field of Artificial Intelligence, my interests mainly lie in the area of Biologically Inspired Computing, in particular in Evolutionary Computing. I undertake research in two main areas: optimisation, and automated design and learning

Within Optimisation, I am particularly interested in optimisation system which are able to continually learn over time to improve their own methods, for example improving their own code as they gain experience, and generate new algorithms for solving predicted future instances. This research combines technique in automated algorithm design and generation with state-of-the-art machine-learning methods. Much of my research has drawn on the hyper-heuristic paradigm as
a practical method of solving optimisation problems encountered in the real world, e.g packing, scheduling and routing. Recently, I have been investigating ensemble methods for tackling these problems, where I'm interested in the trade-off between diversity and quality in forming an ensemble, and related questions about how to measure algorithm diversity.

Much of my current work focus on evolutionary and learning applied to robotics. I am currently PI on a large multi-institutional project that aims to produce an autonomous facility for fabricating robots on demand, designed by evolution, that mixes 3d-printing with the state-of-the-art in evolutionary and machine-learning methods to evolve the morphology and controllers of robots. More generally, I am interested in how artificial evolutionary mechanisms can be adapted to co-evolve the body and brain of a robot, and particularly the relationship between evolutionary processes acting on populations and learning processes that act on individuals.
I’m also interested in the role that diversity plays in such systems, and how it can be harnessed to enable systems to adapt to changing environments.