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Bio

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Hello! Ola! Oi! , my name is Tarek

I am a fourth year PhD student at the Centre for Data Intensive Science at University College London (UCL). My research focus is on the application of Deep Learning algorithms for real-time classification of Astronomical transient events.

I am also a Research Software Engineering Fellow at the Software Sustainability Institute, where I help academics leverage tools and software engineering best practises for better research software : #bettersoftwarebetterscience

Besides my doctoral research, I enjoy exploring how latest techniques in statistical signal processing and probabilistic machine learning can be used for Learned Image Reconstruction and Learned Image Compression 🗜 in Embedded Systems for Embedded Machine Learning (#TinyML) 📱

Find me online @tallamjr trying to contribute to open-source whereever possible, retweeting and rambling, and providing professional insights here ,

Curriculum Vitae

Below is an overview of some of my profession and technical experience.

Education

Ph.D Applied Machine Learning [Astrophysics]
University College London (UCL)
09.2017 - present
Centre for Doctoral Training in Data Intensive Science
Supervised by: Prof. Jason McEwen (Primary Advisor), Prof. Denise Gorse
Thesis: Multivariate Time-Series Classification of Astrophysical Transients with Deep Learning
MSc Computer Science
University College London (UCL)
09.2014 - 09.2016
Department of Computer Science, Engineering
Supervised by: Prof. Jason McEwen, Prof. Denise Gorse
Project: Radio Interferometric Image Reconstruction for the SKA: A Deep Learning Approach
MSci Astrophysics
Royal Holloway, University of London (RHUL)
09.2007 - 07.2011
Department of Physics
Supervised by: Prof. Stuart Boogert
Project: Analytical Methods of Stellar Spectra: Stellar Spectroscopy

Technical Work Experience

Machine Learning Researcher [TIN Internship]
The Alan Turing Institute, London.
05.2021 - present
Conduct research into unsupervised probabilistic machine learning and scalable non-parametric inference techniques for sequential latent factor modelling. Work collaboratively to investigate data and model compression techniques for deep neural networks.
Research Software Engineering Fellow
Software Sustainability Institute (SSI)
05.2021 - present
Advocating for software engineering best practises in the research community by providing version control and testing training to academics. My SSI mission is prepare and up-skill academics for working with large scientific research code-bases, that can scale to many contributors.
Graduate Teaching Assistant
University College London (UCL)
09.2020 - 09.2021
Assisting with grading and lesson planning for SPCE038: Machine Learning with Big Data. Lead the migration of TensorFlow 1.x to TensorFlow 2.x API. Coordinated infrastructure setup for delivery of course through Jupyter Book
Graduate Teaching Assistant
London Business School (LBS)
03.2020 - 06.2020
Assisting with grading and support for: E517: Python for Finance, QDE-APP: Applied Python Program- ming and CA22: Basic Python.
Research Software Engineer [Ph.D Internship]
The Alan Turing Institute, London.
08.2019 - 03.2020
Working in collaboration with the National Air Traffic Service (NATS), reinforcement learning (RL) was used to investigate machine learning methods to support air traffic controllers. Development of a RESTful API using flask was completed to allow for integration of both open-source and proprietary simulators. This has been followed by development of RL agents using openai-gym.
Data Scientist [Ph.D Internship]
Transport for London (TfL).
01.2018 - 04.2018
Working in a team of 4 fellow PhD students, we investigated a variety of machine learning methods for train failure predication, that would be robust to highly imbalanced time-series data. With high cost implications for false-positives, we looked at algorithmic trade-offs that optimised accuracy at a low false positive rate. This work was co-supervised by academics at UCL and data scientists at TfL.

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