We look into Generative Adversarial Network (GAN), its prevalent variants and applications
in a number of sectors. GANs combine two neural networks that compete against one …

Developing machine learning models can be seen as a process similar to the one
established for traditional software development. A key difference between the two lies in the …

In this work, we examine the classification accuracy and robustness of a state-of-the-art semi-
supervised learning (SSL) algorithm applied to the morphological classification of radio …

We present Ease. ML, a lifecycle management system for machine learning (ML). Unlike
many existing works, which focus on improving individual steps during the lifecycle of ML …

Tighter constraints on measurements of primordial non-Gaussianity (PNG) will allow the
differentiation of inflationary scenarios. The cosmic microwave background bispectrum–the …

We present Ease. ML, a lifecycle management system for machine learning (ML). Unlike
many existing works, which focus on improving individual steps during the lifecycle of ML …

Along with the nearing completion of the square kilometer array (SKA), comes an increasing
demand for accurate and reliable automated solutions to extract valuable information from …

Image-to-image translation with Deep Learning neural networks, particularly with Generative
Adversarial Networks (GANs), is one of the most powerful methods for simulating …

With the beginning of a new gravitational-wave era in astronomy and astrophysics, the
problem of identification of gamma-ray bursts optical counterparts is becoming more …

My PhD journey started when I was invited to South Africa by my supervisor, Oleg Smirnov,
to help him create packages for the tools used in aperture synthesis. While working at SKA …