David Schnoerr


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I joined the Theoretical Systems Biology Group at the Imperial College London as a postdoc with Michael Stumpf in autumn 2017. I previously worked as a postdoc at the University of Edinburgh where I also obtained my PhD in 2016 under the supervision of Ramon Grima and Guido Sanguinetti. I received my diploma in theoretical physics from the University of Heidelberg in 2013, where I wrote my thesis on Functional Renormalization Group methods in the group of Christof Wetterich.

 
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Research interests

  • Computational systems biology and biophysics
  • Stochastic processes in biochemical reaction networks
  • Stochastic reaction-diffusion processes
  • Self-organisation
  • Metabolic whole-cell models
  • Statistical inference


Publications

* N. S. Scholes and D. Schnoerr contributed equally
† Selected for the “Highlights 2017 Collection” of Journal of Physics A
‡ B. Cseke and D. Schnoerr contributed equally


Reviewer for

  • The Journal of Chemical Physics
  • SIAM Journal on Applied Mathematics
  • Bulletin of Mathematical Biology
  • Journal of Statistical Mechanics: Theory and Experiment
  • Journal of Physics A: Mathematical and Theoretical
  • Journal of The Royal Society Interface
  • Journal of Theoretical Biology
  • PLOS One
  • Frontiers in Genetics
  • Entropy
  • AMMCS


Conference and Workshop talks

  • “Cox process representation and inference for stochastic reaction-diffusion processes”,
    Workshop on Stochastic dynamics on large networks: prediction and inference, October 2018, Max Planck Institute
    for the Physics of Complex Systems, Dresden, Germany.

  • (invited) “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”.
    Nanoscale mathematical modeling of synaptic transmis- sion and calcium dynamics, October 2018,
    Centro di Ricerca Matematica Ennio De Giorgi, Pisa.

  • (invited) “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”.
    Workshop on Multiscale modeling and simulations to bridge molecular and cellular scales, October 2018,
    Centro di Ricerca Matematica Ennio De Giorgi, Pisa.

  • “Cox process representation and inference for stochastic reaction-diffusion processes”,
    Bioms Symposium, October 2019, BioQuant, Heidelberg University, Germany.

  • “Cox process representation and inference for stochastic reaction-diffusion processes”,
    10th European Conference on Mathematical & Theoretical Biology and SMB Annual Meeting, July
    2016, Nottingham, UK.

  • “Breakdown of the chemical Langevin equation and moment closure approximations
    for stochastic chemical kinetics”, Mathematical Trends in Reaction Network Theory,
    July 2015, University of Copenhagen, Denmark.


Seminar Talks (invited)

  • “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”,
    Departmental Seminar, December 2018, Helmholtz Center Munich, Institute for Computational Biology, Munich, Germany.

  • “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”,
    Departmental Seminar, October 2018, Max Planck Institute for the Physics of Complex Systems, Dresden, Germany.

  • “Modelling the RNA life cycle in yeast under stress from RNA-protein binding data”,
    Departmental Seminar, September 2018, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.

  • “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”,
    Biophysics Seminar, September 2018, Department of Physics, University of Göttingen, Germany.

  • “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”,
    CeNoS Colloquium, May 2018, Center for Nonlinear Science, University of Münster, Germany.

  • “Modelling the RNA life cycle in yeast under stress from RNA-protein binding data”,
    BIOMS seminar, April 2018, BioQuant, Heidelberg University, Germany.

  • “Using ideas form statistics for analysing (spatio-temporal) stochastic processes”,
    Biophysics and Soft Matter Seminar, June 12, 2017, Simon Fraser University, Canada.

  • “Using ideas form statistics for analysing (spatio-temporal) stochastic processes’,
    Industrial and Applied Mathematics Seminar, April 27, 2017, University of Oxford, UK.

  • “Cox process representation and inference for stochastic reaction-diffusion processes”,
    Biomathematical Seminar, November 2016, Imperial College London, UK.

  • “Cox process representation and inference for stochastic reaction-diffusion processes,”
    Stochastic Dynamical Systems in Biology: Numerical Methods and Applications, June 2016,
    Newton Institute, University of Cambridge, UK.


Teaching

  • Tutorial on Mathematics and Physics for Biologists (2015)
  • Tutorial I on Mathematics and Physics for Biologists (2014)
  • Tutorial II on Mathematics and Physics for Biologists (2014)
  • Tutorial I on Mathematics for Natural Scientists (2012)
  • Tutorial II on Mathematics for Natural Scientists (2012)
  • Tutorial on Theoretical Physics II (2010/11)
  • Tutorial on Theoretical Physics I (2008/09)