Our researchers

Geach conducts ground-breaking research into the evolution of galaxies (Geach et al. 2014, Nature, 516, 68) and has pioneered the use of unsupervised machine learning in astrophysics (Geach 2012, MNRAS, 419, 2633).

Jim is an astrophysicist specialising in galaxy evolution and observational cosmology. With a degree in physics from Imperial College London and a PhD in astronomy from Durham University, Jim has over a decade of research experience at the forefront of his field, holding a Banting Fellowship at McGill University in Montreal and now a Royal Society University Research Fellowship at the University of Hertfordshire, where he is a Professor of Astrophysics. Much of Jim's research involves searching for weak signals in giant data sets, and this requires the development of efficient and intelligent algorithms to make those discoveries.

Steuber is an expert in computational neuroscience (Rothman et al. 2009, Nature 457, 1015) and president of the Organization for Computational Neurosciences.

Volker is Head of the Biocomputation Research Group at the University of Hertfordshire. Research in the Biocomputation Research Group involves the development of computational models to study biological systems, and the application of biologically-inspired machine learning algorithms for the analysis of real-world data. Before joining the university he worked as postdoc at UCL and the University of Antwerp. He received his PhD from the University of Edinburgh and has degrees in artificial intelligence from Edinburgh and in neuroscience from the ETH Zürich. Volker’s current and previous funding includes grants from the Human Frontier Science Program Organization, the German DFG, the BBSRC and the German National Merit Foundation.

Kadir has pioneered the development of new spike sorting algorithms for electrophysiological experiments which are used by over 300 laboratories worldwide (Rossant, Kadir, et al. 2016 Nature Neuroscience, 19, 634).

Shabnam is a mathematician with a history of interdisciplinary research in computational neuroscience, theoretical physics, pure mathematics and software engineering. Shabnam studied mathematics at Trinity College, Cambridge followed by a DPhil at the Mathematical Institute, Oxford. During her DPhil and subsequent postdoctoral research fellowships at the Fields Institute, Toronto, and the Institute fuer Algebraische Geometrie at Leibniz Universitaet, Hannover, she applied mathematical ideas inspired by string theory to topics in geometry and topology. In particular, she was inspired by the fruitfulness of using computational methods to inspire new and unexpected mathematical conjectures. This led her to start looking at problems in neuroscience during postdoctoral positions at UCL, Imperial, Rutgers and NYU. Her key achievements so far have been in developing machine learning algorithms for the processing and analysis of very large datasets by experimental neuroscientists. She is now a senior lecturer at the University of Hertfordshire and is building new techniques of topological data analysis for use on neuroscientific data, ranging from the auditory system, to vision and olfaction.

Schmuker has pioneered brain-inspired data science (Schmuker et al. 2014 PNAS, 111) and leads a work-package “Neuromorphic Applications” in the EU flagship research initiative “Human Brain Project”.

Michael is a computer scientist dedicated to translating algorithms and processes from biology and the brain into solutions for data science and machine learning. His highly diverse background includes a degree in Biology and a PhD in Chemical Informatics, and over ten years of research experience in Neuroscience, Computer Science, and Chemistry. He has extensive experience in machine learning and pattern recognition in a wide range of large datasets, ranging from drug discovery and brain imaging to gas sensing in turbulent environments. Before joining the University of Hertfordshire in 2016, Michael held a Marie Curie Fellowship at the University of Sussex. He received funding from the EU, the German DFG and BMBF, Volkswagen Foundation and EPSRC. His recent research uses computational principles observed in the sense of smell to implement brain-inspired pattern recognition on next-generation computing hardware, so-called neuromorphic devices.

Kaviraj is the UK point of contact for the ‘Galaxies’ science working group of the revolutionary Large Synoptic Survey Telescope (LSST).

Sugata works on galaxy formation and evolution. He holds an undergraduate degree in Physics from Imperial College London and a PhD in Astrophysics from Oxford University and is currently a Reader in Astrophysics at the University of Hertfordshire. Prior to this position he held a Leverhulme Early-Career Fellowship at Oxford,  a Junior Research Fellowship at Worcester College Oxford, an 1851 Royal Commission Fellowship at University College London and an Imperial College Research Fellowship. He was awarded the Royal Astronomical Society’s (RAS) Winton Capital Award (for best early-career astronomer) and the RAS Group Achievement Award for his involvement with the Galaxy Zoo citizen-science project. He currently leads the Galaxies Science Collaboration of the Large Synoptic Survey Telescope and is a core member of the Horizon-AGN cosmological simulation project.

Jones is an expert in brown dwarfs and exoplanets, and a key member of the team that discovered Proxima b, the nearest exoplanet to Earth (Anglada-Escudé et al. 2016, Nature, 536, 437).

Hugh began his research career as a PhD student based at the Royal Observatory of Edinburgh and partly at Space Telescope Science Institute in Baltimore. He was a European Commission research fellow in Tokyo then moved to Liverpool and since 2004 the University of Hertfordshire. He has had close involvement in the discovery and characterisation of a significant fraction of the nearby brown dwarfs and extrasolar planets including the discovery of planet's orbiting the Sun's nearest neighbours, Proxima Centauri and Barnard’s star. His research is based on finding weak signals in a wide variety of datasets which he applies to a variety of other problems including cloud classification and astronomical site testing.