About Me

I am currently a PhD student at the Centre for Doctral Training in Data Intensive Science at the Mullard Space Science Laboratory, UCL, supervised by Professor Jason McEwen, Professor Ofer Lahav and Dr. Denise Gorse. My PhD is in the emerging field of Astroinformatics, which is an interdisciplinary field of study involving the combination of astrophysics, astronomy, data science and machine learning.

My research focus centres around applying Machine Learning methods for Photometric Supernova classification. For a further overview of my research interests and specific research topics I am involved with, see my research page.

The tools of the trade I can't live without are git, vim, and tmux all on *NIX systems. I am a keen polyglot-programmer who lives at the command line and will find any excuse to stay there! See my blog for a more personal look into my world.

Contact Details

t dot allam dot jr at gmail dot com


UCL, University of London

PhD Astroinformatics (2017 - 2022)

Research Project:

  • Cheap Deep Learning for Photometric Supernova Classification and Beyond

Doctoral Training courses:

  • Numerical Optimisation
  • Statistical Analysis of Data
  • Research Software Engineering with Python
  • Data Intensive Astrophysics
  • Applied Machine Learning
  • Big Data and Machine Learning with Spark

UCL, University of London

MSc Computer Science, 72%

Major Project:

  • Radio Interferometric Image Reconstruction for the Square Kilometer Array:
    A Deep Learning Approach

Taught Component:

  • Introductory Programming (Java)
  • Apps Design
  • Systems Infrastructure
  • Architecture & Hardware
  • Algorithmics
  • Functional Programming
  • Databases
  • Computer Music

Royal Holloway, University of London

MSci Astrophysics, 2.1

Major Project:

  • Analytical Methods of Stellar Spectra: Stellar Spectroscopy

Research Review:

  • Dark Matter Searches


Mullard Space Science Laboratory, UCL

PhD Researcher September 2017 - Present

2017 Cohort at the STFC Centre for Doctoral Training (CDT) in Data Intensive Science (DIS) at UCL. Under the supervision of Professor Jason McEwen, Professor Ofer Lahav and Dr. Denise Gorse, my research focus is on the development of classification algorithms for effective classification of Supernova. I am very interested in all aspects of time domain astrophysics and the high energy phenomena that relate to such events.

Information Services Division, UCL

Student Training Assistant November 2015 - Present

Provided teaching support to students and staff participating in I.T training courses given by UCL Information Services. A selection of training courses include Python Programming, R, UNIX and MATLAB.


Back End Developer October 2014 - September 2015

Working in a team of 5 and as part of the ATOS IT Challenge, we were set a task to develop a product that would integrate a user with IoT technologies. Our idea was to create an internet connected intercom system that would allow a user to answer their door/sign for a package remotely with an application on their smartphone. My role was to develop effective Bash and Python scripts that would automate configuration of the product upon start up. In addition, working collectively as part of the sub-hardware team, was to integrate modern web technologies such as WebRTC with the hardware.

John Adams Institute, Dept. Physics, RHUL

Summer Research Intern. July 2010 - August 2010

The aim of this internship was to develop visual representations using Python of TM & TE modes that occur within cylindrical accelerator cavities. These were represented in 2-dimensions using Python’s Matplotlib and in 3- dimensions using Python codes embedded in Paraview.


Throughout my undergraduate and post-graduate degrees I have actively developed my programming and analytical skills which allow me to conduct effective scientific research. The languages I have most experience in are:

  • Python
  • Julia
  • Bash
  • R

I am also familiar with the following languages through self-study and implementation of basic algorithms.

  • C/C++
  • Haskell
  • Java
  • SQL
  • AWK
  • Javascript/JSON
  • PHP
  • MPI/openMP