I am a data scientist at a London-based tech startup. My main focus is in forecasting cargo movements and energy market behaviours around the world. This process involves a fair amount of time-series modelling, satellite image analysis and more general clustering problems.I was previously a postdoc at the University of Illinois and the University of St Andrews where I also completed my PhD research in observational astrophysics. My main research goals are broadly concerned with understanding the formation, dynamics and kinematics of Active Galactic Nuclei (AGN). I have developed a Monte Carlo Markov Chain (MCMC) code to fit the time-series flux variations observed in AGN. This code, CREAM (Continuum REverberation AGN MCMC), uses bayesian inference to fit the Fourier time-series that drives the flux variations. It also infers the accretion disc and emission line time-lag response functions and returns the posterior probability distribution for all these parameters. The response function maps the accretion disc tilt-angle and radial temperature profile and allows us to probe the accretion disc structure for objects far too remote and compact for standard observations to be effective. Please follow the following Links to mt CV, thesis, and publications. CREAM has recently been upgraded to a Python module (PyceCREAM) and can be installed from the terminal by typing pip install pycecream.
When not lost in space, I enjoy modelling a variety of data sets using various machine learning techniques including K-means clustering, multi-layer neural networks, random forrest classifiers and regressors and various other techniques from probability theory. The projects apply these techniques to a wide variety of datasets with applications anywhere from classifying underwater sonar signals as mines, to modelling variables affecting house prices in the US, to analysing shopping habits on Black Friday. The links below take you to my GitHub folder which contains the individual projects and codes (mainly python based). The 'projects' folder contains a short summary of the analysis process and main results of each project.
Super massive black holes (SMBH) lie at the centers of almost all galaxies. These sometimes eat orbiting clouds of gas that wander too close and become Active Galactic Nuclei (AGN). The accreting material forms into a disc structure around the SMBH in a similar way to X-ray binary stellar mass black holes in our own galaxy. These discs are too remote and compact to be resolved directly and much about their structure and dynamics remains unknown.
My research goals are broadly...
- To understand the structure and dynamics of the accretion disc and surrounding broad-line-clouds.
- Use this to help discover how the black hole is able to draw in huge amounts of material (around a solar mass each year) from its host galaxy.
- What causes this to happen in some galaxies but not others?
- Use AGN as cosmological distance measures (standard candles) to improve our knowledge of cosmology (See Watson et al, 2010; Hoenig et al, 2017).
PyceCREAM can installed using ''pip install pycecream'' and the source code can be found by following the link to the github repository below. Have fun :)