Since May 2013: Postdoc with External Senior Fellow Robert Murphy
August 2006 - September 2012: PhD studies in Computer Science at Albert-Ludwigs-Univerisity, Freiburg, Germany
Oct 2000 - July 2006: Diploma Studies in Computer Science, Minor in Mathematics, at Albert-Ludwigs-Univerisity, Freiburg, Germany
- Maja Temerinac-Ott, Armaghan W. Naik, Robert F. Murphy, “Deciding when to stop: efficient experimentation to learn to predict drug-target interactions”, BMC Bioinformatics, vol. 16 (213), 2015.
- Maja Temerinac-Ott, Olaf Ronneberger, Peter Ochs, Wolfgang Driever, Thomas Brox and Hans Burkhardt, "Multiview Deblurring for 3-D Images from Light Sheet based Fluorescence Microscopy", IEEE Transactions on Image Processing, vol. 21(4), pp. 1863-1873, 2012.
- Maja Temerinac-Ott and Miodrag Temerinac, "Discrete Fourier-Invariant Signals: Design and Application", IEEE Transactions on Signal Processing, vol. 60(3), pp. 1108 - 1120, 2012.
- Maja Temerinac, Marco Reisert and Hans Burkhardt, "Invariant Features for Searching in Protein Fold Databases", International Journal on Computer Mathematics, 'Special Issue on Bioinformatics', Vol. 84, Issue 5, pp. 635-651, May 2007.
Active learning-driven perturbagen analysis
The switch from pollen development towards embryo formation provides exceptional opportunities for “green” biotechnology, and an attractive model for studying the mechanisms of cellular reprogramming, totipotency, differentiation and development. This agronomically and scientifically important question will be addressed by adopting a systems approach to study the single-celled microspore. Together with the group of Prof. Palme the cell development will be studied by systematically changing the incubation conditions. A matrix of cell structure components and conditions will be tested regarding reprogramming processes of microspores and the progression of the different development stages. The optimal microspore culture conditions will be found using the active learning approach applied and incorporated into the matrix to efficiently learn from experiments in a manner avoiding explicit testing of every possible pairing of condition and protein.