My name is Daniel Müller-Komorowska and I am a data scientist. I started out as a neuroscientist, recording and analyzing electrical signals from neurons. I used Python with NumPy, SciPy and Pandas to analyze these neuronal time-series data. I used Matplotlib and Seaborn for data visualization. I also used dimensionality reduction and clustering with scikit-learn, scanpy and R on single cell sequencing data. Finally, I have simulated spiking neuronal networks with NEURON and artifical neural networks with PyTorch.
For recent projects I have used MATLAB for electrical circuit simulations. I also started learning Julia, where I have focused on simulating neural systems. On this website I run my Scientific Programming blog, where I write about programming and data science topics. I am currently looking for new professional data science challenges. To learn more about my work you can find my GitHub here, find my academic publications at the bottom of this page or contact me directly via firstname.lastname@example.org. You can find my resume here.
Müller-Komorowska, D., Opitz, T., Elzoheiry, S., Schweizer, M., Ambrad Giovannetti, E., & Beck, H. (2020). Nonspecific Expression in Limited Excitatory Cell Populations in Interneuron-Targeting Cre-driver Lines Can Have Large Functional Effects. Frontiers in Neural Circuits, 14(April), 1–13. https://doi.org/10.3389/fncir.2020.00016
Braganza, O., Müller-Komorowska, D., Kelly, T., & Beck, H. (2020). Quantitative properties of a feedback circuit predict frequency-dependent pattern separation. ELife, 813188. https://doi.org/10.7554/eLife.53148