Staff and students

Scientists across the natural sciences and engineering increasingly rely on data-driven approaches to assist in critical decision-making during their discoveries. The search for a new scientific discovery can frequently be cast as a multi-objective optimisation problem that involves balancing many conflicting requirements within a vast, structured design space. For example, a biochemist seeking new therapeutics might aim to optimise the efficacy, synthesisability, and drug-likeness of compounds whilst minimising off-target effects and toxicity. Similarly, a clinician might optimise a treatment plan that maximises patient survival rates whilst minimising side effects and costs. Qualities like synthesisability are difficult to estimate in advance and require resource-intensive experimentations to measure, making these optimisation problems ‘black-box’ and particularly challenging. Substantial efforts have been made in developing black-box search algorithms (e.g., evolutionary methods, Bayesian optimisation), and employing recent large language models (LLMs) to optimise a wide range of black-box functions. All of them can be regarded as intelligent agents in multi-objective optimisation.

Nikolas Nikolaou received his PhD in Anatomy and Developmental Biology from University College London in 2009. As a PhD student in David Wilkinson’s group (MRC National Institute for Medical Research), he investigated cell signalling mechanisms regulating the balance between neural progenitor maintenance and differentiation in the developing nervous system. He then joined the group of Dr Martin Meyer at the Centre for Developmental Neurobiology (King’s College London) to investigate neuronal wiring in the larval zebrafish brain. He used high-content functional imaging techniques to identify and characterise the response properties of developing visual circuits. Whilst at King’s College London, Nikolas joined Prof. Corinne Houart’s group to perform genetic studies and uncover the extra-nuclear roles of splicing regulators in neuronal connectivity. He moved to the University of Bath as a Lecturer in 2020 to use structural and functional imaging techniques together with transcriptomic analyses to elucidate molecular and cellular mechanisms essential for brain connectivity and how these are affected in neurological disease conditions. He joined the University of Exeter in 2024 where he is currently a Senior Lecturer and Principal Investigator at the Living Systems Institute.

Austin’s research interest is stem cell biology and in particular pluripotent stem cells that harbour the capacity to generate all cell types of the mammalian organism. His group seek to derive universal principles underlying the establishment and progression of pluripotency in diverse mammalian embryos and to reveal network properties that enable long-term self-renewal in vitro of transient in vivo cell states. They also aim to model and dissect in vitro the developmental programme from emergent naïve pluripotency to lineage-committed progenitors. Austin’s research employs a range of approaches from computational modelling to in vivo chimaera studies.

Jordi is interested in stem cells, their role in animal regenerative processes and their evolution. Jordi’s research group uses single cell transcriptomic analysis methods to study them in a variety of stem cells and regeneration research model organisms, such as the planarian Schmidtea mediterranea. The team has developed innovative methods like ACME for dissociation and combinatorial single-cell transcriptomics and is also studying stem cells in regenerative animals such as the annelid Pristina leidyi. Ultimately, their research aims at decoding the transcriptional and epigenetic code of invertebrate pluripotent stem cells and their differentiation to understand why some animals can regenerate while we cannot.