Logotype design
I created the logos of the packages presented above and the others listed below using both Matplotlib and Illustrator. I have used these tools to create logos for various projects and workshops listed below, and I enjoy the process of designing custom graphics and visualizations.
This logo represents the Math + x symposium on inverse problems and deep learning in space exploration (2019). It symbolizes a Ptolemy solar system, with the central white half-circle representing a star, the second radial area showing an orbiting moon, and the last radial area showing an exoplanet illuminated by the star. The bottom spiderweb illustrates space-time deformations caused by the star, and the lower part shows an abstract representation of a neural network.
This logo represents the Math + x symposium on inverse problems and deep learning, mitigating natural hazards (2020). It depicts a volcano erupting and releasing a neural network, with a LSTM model being applied underneath to symbolize the research that was conducted to anticipate volcanic eruptions using deep learning and seismic source analysis. The logo also includes illustrations of a tornado and tsunamis to emphasize the focus on various types of natural hazards.
This logo represents the Math + x symposium on matter under extreme conditions in solar system giant planets and exoplanets, inverse problems and deep learning (2022). This logo depicts Saturn, with an atom symbol at its center symbolizing matter in the center of giant planets like metallic hydrogen. The constellation around the planet illustrates a neural network used to understand and model the behavior of such a system.
The covseisnet logo represents an array of seismic stations, with one station symbolizing a virtual source in the field of passive seismic interferometry. The image is enclosed in brackets to symbolize the matrix framework of the Python package.
This logo represents a beamforming or backprojection process, as depicted by the probability density function. The 3D grid illustrates that the package is capable of working with data in three dimensions. The logo was designed for a package that performs beamforming on seismic data features.
This logo is a modified Morlet wavelet. It represents the wavelet transform at the core of a scattering network and has the ability to classify and group similar signals through analysis. The color gradient in the logo represents the ability to identify and distinguish between different types of signals.
This logo is based on the original JAX logo, with the addition of the word "SYM" at the beginning, modifying the name to "SYMJAX".