Research in our group focuses on biological systems, with an interdisciplinary approach based on the interplay of mathematics computer science and biology, but the strategy we use to approach them is the same. Data analysis leads to the study and development of computer models and mathematics help describe and predict the dynamics of the phenomena of interest. The mathematical methods we use to study these models are diverse and range from the theory of dynamical systems to partial differential equations or probability theory.
The modelling team has participated in the organization of numerous scientifc conferences and the editing of two special issues of the per-reviewed journal Networks and Homogenous Media [1,2].
The modelling team is strongly dedicated to education, notably through innovation and training by/for research. The team received "High Performance Computing for Education" Microsoft prize in 2008 and has been a "Cuda Teaching Center" since 2011. The team is also involved in creative approaches for the diffusion science combining arts and sciences (Festival Sciences sur Seine 2008).
 Henri Berestycki, Danielle Hilhorst, Frank Merle, Masayasu Mimura and Khashayar Pakdaman (special issue editors) Networks and heterogenous media, special issue vol 7 (4) December 2012.
 Henri Berestycki, Danielle Hilhorst, Frank Merle, Masayasu Mimura and Khashayar Pakdaman (special issue editors) Networks and heterogenous media, special issue vol 8 (1) January 2013.
Sélection of Publications
Guevara H. E.
Breakdown of the Finite-Time and -Population Scalings of the Large-Deviation Function in the Large-Size Limit of a Contact
Journal of Statistical Mechanics: Theory and Experiment, (2018),
Garcia del Molino L.C., Pakdaman K, Touboul J., Wainrib G.
The real Ginibre ensemble with k = O(n) real eigenvalues.
Journal of Statistical Physics. 18 fev. 2016.
Le Cunff Y., Baudisch, A. Pakdaman K.
Evolution of Aging: Individual life History trade-offs and population Heterogeneity account for mortality patterns across species
Journal of Evolutionary Biology. 2014 Aug;27(8):1706-20 Jun 13.
Pakdaman K., Perthame B., and Salort D.
Relaxation and self-sustained oscillations in the time Elapsed neuron Network Model.
SIAM Journal on Applied Mathematics 2013, 73(3), 1260–1279.
Pakdaman K., Thieullen, M., Wainrib G.
Asymptotic expansion and central limit theorem for multiscale piecewise deterministic Markov processes.
Stochastic Processes and their Applications, 2012, 122 (6): 2292-2318.
Grotta-Ragazzo C., Malta C. P. and Pakdaman K.,
Metastable Periodic Patterns in Singularly Perturbed Delayed Equations.
Journal of Dynamics and Differential Equations, 2010, 22(2): 203-252