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Key Skills

Python (Pandas, Numpy, astropy)
Machine Learning (scikit-learn, TensorFlow, Keras, OpenCV)
Data Visualization (Matplotlib, seaborn)
Git, LaTex, SQL, IDL

Work Experience

Researcher
University of Milan - Bicocca, Italy (May 2020 - April 2023)

• During this position, I was responsible for developing a sequence classification model using random forest to identify and model features in galaxy spectra. This model was successful at identifying up to 90\% of features, comparable to human classification, but was able to complete the work in hours rather than months.

• I also completed one of the first statistically significant studies connecting gas and galaxies in the distant Universe by analysing datasets containing thousands of galaxies using correlation, regression and clustering methods.

Postdoctoral Researcher
Durham University, UK (May 2018 - April 2020)

• As the scientific lead of the MAGG survey, I managed the data science pipeline to process, clean and distribute large datasets of astronomical data.

• Utilising this data, I explored individual cases in detail to identify low signal-to-noise features and showed that the nature of gas around galaxies is much more complex and diverse than previously thought.

PhD Researcher
University of Hertfordshire, UK (September 2013 - September 2017)

• While a PhD candidate, I focused on exploiting and combining very large datasets from multiple major telescopes. I used data mining techniques to clean and extract information in order to study the distribution of star formation and make inferences about the nature of galaxies and how they evolve.

• I showed that mergers between two comparable galaxies, while dramatic, are rare and have little impact on the overall evolution of our Universe.