I am a passionate programmer, data scientist, and Astrophysicist with a background in signal processing based at the University of Toronto. I have an interdisciplinary background, including an MSc in Condensed Matter Physics, a PhD in Astrophysics, and a Diploma in Data Science.
My experience in software development and data science started with my PhD at University College London. I worked with a team of research software engineers to develop imaging software in the field of Radio Astronomy. I was given the opportunity to design new image reconstruction algorithms in Radio Astronomy and Data Science that exploit the distributed nature of computing clusters using MPI and C++. Software engineering techniques were key to making this project successful, including test driven development and continuous integration.
Working as an Astrophysicist has provided me with years of experience in C++ and Python in the context of software engineering. As a post doctoral researcher at the University of Toronto I still make use of these skills in the development of new signal processing algorithms. Furthermore, I was exposed to different areas of science and signal processing, including medical imaging, cosmology, remote sensing, and radio instrumentation.
Even outside of the office, I am passionate about learning more about Data Science and Machine Learning Engineering in areas such as Natural Language Processing and Computer Vision. This lead to me gaining a Diploma in Data Science at BrainStation, to compliment my current technical expertise with training for Machine Learning in industry. This includes training in a range of technologies including but not limited to SQL, Scikit-Learn, Pandas, Tensorflow, PyTorch, Hadoop, and Spark. As a project I trained multiple artificial neural network architectures with Tensorflow to extract roads and buildings in satellite imagery.
I have published researched in multiple areas of Physics and Astronomy by leveraging my technical and scientific skills in mathematics, programming, and data science. I am committed to taking my proven problem-solving and learning ability to engineer and interpret real-world Data Science and Machine Learning solutions.