Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)
Phone: +49 351 210-2683
Bevan Cheeseman is a PhD student in the MOSAIC Group since July 2013. He is a New Zealand citizen and was born in Auckland, New Zealand, in 1987.
From 2005 until 2008, Bevan studied a conjoint degree (BSc/BCom) in Applied Mathematics and Finance at University of Auckland (New Zealand). Further at Auckland, in 2009 Bevan completed a BCom(Hons) in Finance with first class honours and was fully funded by a University of Auckland Honours Scholarship. His Honours research focused on econometric modelling of commodity futures markets using vector error correction models.
In 2010, Bevan joined investment bank Macquarie Group in Sydney, as an analyst in Debt Markets Research working on pricing mortgage and asset backed securities. Later in the year, he undertook a quantitative trading internship at Liquid Capital working on auto-regressive and discrete Hidden Markov Models. In early 2011, he worked as an independent contractor for Craigs Investment Partners (Auckland) on accessing and analysing New Zealand wholesale electricity market and hydrology information.
From 2011 to 2012, Bevan studied a MSc in Applied Mathematics at the University of Melbourne (Australia) and graduated with distinction. Bevan was supervised by Professor Kerry Landman and Professor Barry Hughes and was a member of the Mathematical Biology research group. His Master research focused on single-cell motility, developing a discrete modelling framework including extracellular matrix interactions. During these studies Bevan was supported by a Faculty of Science National Scholarship and received the M.L. Urquhart Graduate Prize (2012) for best overall performance in mathematics.
Following graduation in December 2012, Bevan continued as a research assistant supervised by Professor Kerry Landman in the Mathematical Biology research group. Collaborating with Dr. Don Newgreen from MCRI Melbourne, Bevan worked on modelling and analysis of the developing enteric nervous system. This research focused on understanding the stochastic competition for space and its impact on cell lineage tracings using stochastic cellular automata models.
In the MOSAIC Group, Bevan is working on spatially adaptive particle representations of images and numerical simulation results. This is going to enable large-scale and efficient experiments on 4D cell lineage tracing in developing embryos. He intends to use this data along with stochastic, mesh-free particle models and statistical analysis to understand cell decision processes in embryogenesis.
A Word from Bevan...
I am developing computational algorithms for both image processing and simulation that adapt dynamically to information through space and time. The MOSAIC group's goal is to understand how cells form tissues through image-based systems biology. This requires that we are able to extract information from, and then simulate, real biology systems. However, a key feature of biological systems is their large range of spatial and temporal scales. This dynamism creates a significant challenge for traditional methods. The challenge arises because the smallest space and time scales in the system dictate the structure, and complexity, of these methods. My work addresses this issue by providing a common framework for both image processing and simulation that adapts inherently to the dynamic space and time scales of the system.