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January 1970 - September 2019
January 1970 - September 2019
Department of Bioinformatics – George August University – Göttingen
January 2010 - January 2010
• Implemented a novel machine learning algorithm (Hidden Markov Model) to find new patterns in Human genome in Java. The model was based on conditional random fields.
January 2011 - January 2014
Thesis project: My dissertation work examined the impact of recombination (shuffle crossovers) on adaptation (optimization), using deterministic and stochastic mathematical models. * Recombination on HIV fitness landscape (RecombinationHIV): Successfully established a novel stochastic evolutionary algorithm in Java to simulate the evolution of HIV genomic sequences over time. The aim was to evaluate the impact of shuffle crossovers on the rate of optimization on a realistic fitness landscape. * Evolution of natural competence (ClonalComplex): Developed two novel mathematical and computer simulation frameworks in Java and Mathematica. The models were based on continuous- time Kolmogorov forward equations. The models effectively simulate complex dynamics of large populations. * Evolution of recombination on rugged fitness landscapes (RecombinationRuggedLandscape): Designed and implemented models in Java and Mathematica to address an important question in the evolutionary algorithms, i.e. the impact of shuffle cross- overs on the rate of optimum finding on complex fitness landscapes. The framework allowed to confirm that, irrespective of the fitness landscape structure, shuffle crossover accelerates optimization.
Wellcome Trust Sanger Institute
January 2014 - January 2018
* Machine learning approaches to predicting antibiotic resistance (PanPred): Developed a generic machine learning framework in Python to accurately predict antibiotic resistance from genetic and epidemiological input features. The model invoked logistic regression, random decision forests, gradient boosted decision trees and deep learning techniques. Gradient boosted decision trees consistently outperformed alternative models. The best model attained an overall accuracy of 0.92. * Data analysis of large-scale genomic data: Employed a wide range of statistical models, e.g. regression and Bayesian models, in R and Python to analyze 7 large-scale genomic datasets. * Prepared reports, visualized and presented results at meetings and conferences.
Center for Computational Biology
January 2018 - September 2019
* Established and led a growing team of data analysts, specialized in big genomic data analysis. * Developed and optimized a machine learning framework, based on ensemble machine learning models, to accurately predict bacterial growth from genomic data. * Designed and delivered courses on machine learning, data analysis and bioinformatics at the University of Birmingham.
As needed - open to ofers < 24hr response time
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