In the early part of the 21st century, exciting work is being conducted by Microsoft Research at the intersection of computing science and the natural sciences. Whether it is groundbreaking research resulting in the WorldWide Telescope, the integration of computing and biology, novel computational tools and methods to predict and mitigate rapid changes occurring in the earth’s life-support systems, or the representations, analysis, and visualization of digitized geographic information, Microsoft Research is committed to providing computational support to unravel the mysteries of the universe.
- Distribution Modeller: Environmental Modelling at the Speed of ThoughtDistribution Modeller (temporary name only!) is CEES' end-to-end browser tool that lets the researcher to rapidly import data, supplement that data with environmental info from FetchClimate, specify an arbitrary model by point and click or in code, parameterize the model against the data using Filzbach, make and visualize predictions with a full propagation of parameter uncertainty – then package and share everytihng, in a way that is inspectable, repeatable, and modifiable.
- ViralSearchIdentifying and Visualizing Viral Content
- SNAP Sequence AlignerSNAP is a new sequence aligner that is 10-100x faster and simultaneously more accurate than existing tools like BWA, Bowtie2 and SOAP2. It runs on commodity x86 processors, and supports a rich error model that lets it cheaply match reads with more differences from the reference than other tools. SNAP was developed by a team from the UC Berkeley AMP Lab, Microsoft, and UCSF. Binaries are available at http://github.com/downloads/amplab/snap/
- 2020 ScienceThe University College London and University of Oxford have recently received funding from the EPSRC Cross-Disciplinary Interfaces Programme (2020 Science: Mathematical and Computational Modelling of Complex Natural Systems) to collaborate with Microsoft Research Cambridge on a programme of research that will involve up to 17 post-doctoral Research Associates over a five year period.
Zhipeng Gui, Chaowei Yang, Jizhe Xia, Jing Li, Abdelmounaam Rezgui, Min Sun, Yan Xu, and Daniel Fay, A visualization-enhanced graphical user interface for geospatial resource discovery, in Annals of GIS, vol. 0, no. 0, pp. 1-13, Taylor & Francis, 23 April 2013
Cory Merow, John A. Silander, and Matthew J. Smith, A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter, in Ecography, Wiley, April 2013
Maurizio Sajeva, Claudio Augugliaro, Matthew Smith, and Elisabetta Oddo, Regulating Internet Trade in CITES Species, in Conservation Biology, Wiley, 8 February 2013
Mindy M. Syfert, Matthew J. Smith, and David A. Coomes, The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models , in PLOS One, vol. 8, no. 2, PLoS, February 2013
M. J. Smith, D. W. Purves, M. C. Vanderwel, V. Lyutsarev, and S. Emmott, The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties, in Biogeosciences, vol. 10, pp. 583-606, European Geosciences Union, 29 January 2013