I am a Reader in Machine Learning in the Institute for Adaptive and Neural Computation at the School of Informatics, University of Edinburgh. My interests focus on probabilistic modelling of biological systems, with particular emphasis on inference in dynamical systems. For more details of my research interests, including live projects and possible PhD projects, please see the research projects page. I'm also a potential supervisor on the newly funded EPSRC Centre for Doctoral Training in Data Science, see Data Science Ph.D. programme.
Dan Benveniste, Hans-Joachim Sonntag, Guido Sanguinetti and Duncan Sproul, Transcription factor binding predicts histone modifications in human cell lines, Proc. Natl Acad. Sci. USA (PNAS), 111(37), 13367-13372, 2014, journal link. An accompanying webpage containing instructions to replicate the research can be downloaded here
David Schnoerr, Guido Sanguinetti and Ramon Grima, Validity conditions for moment closure approximations in stochastic chemical kinetics,J. Chem. Phys. 141 , 084103, 2014 journal link.
David Schnoerr, Guido Sanguinetti and Ramon Grima, The Complex Chemical Langevin Equation, Journal of Chemical Physics, 141, 024103, 2014.journal link
Ezio Bartocci, Luca Bortolussi and Guido Sanguinetti, Data-driven Statistical Learning of Temporal Logic Properties, FORMATS 2014
Luca Bortolussi and Guido Sanguinetti, A statistical approach for computing reachability of non-linear and stochastic dynamical systems, QEST 2014
Anastasis Georgoulas, Jane Hillston and Guido Sanguinetti, Probabilistic Programming Process Algebra, QEST 2014
Gabriele Schweikert, Botond Cseke, Thomas Clouaire, Adrian Bird and Guido Sanguinetti, MMDiff: quantitative testing for shape changes in ChIP-Seq data sets, BMC Genomics 14:826, 2013, link
Botond Cseke, Manfred Opper and Guido Sanguinetti, Approximate Inference in Latent Diffusion Processes from Continuous Time Observations, to appear at NIPS 2013 camera-ready version and supplementary material
Luca Bortolussi and Guido Sanguinetti, Learning and designing stochastic processes from logical constraints, in Quantitative Evaluation of Systems (QEST) 13 pdf
Andrea Ocone, Andrew J. Millar and Guido Sanguinetti, Hybrid Regulatory Models: a statistically tractable approach to model regulatory network dynamics, Bioinformatics 29(7) (2013) journal link
Tom Mayo joined us as a PhD student from the Neuroinformatics DTC, working on epigenetic mechanisms in neuronal cells.
Van Anh Huynh-Thu recently joined us to work on my ERC project from the University of Liege, where she worked on network reconstruction with Pierre Geurts and Louis Wehenkel.
David Schnoerr joined us and Ramon Grima's lab as a PhD student, after a theoretical physics degree in Heidelberg on renormalisation group techniques.Andrea Ocone submitted his thesis and is off to work in the Helmholtz Zentrum in Munich as a post doc with Fabian Theis.