PhD in Computational Data Science & NLP

2011 - 2015

The Open University, Knowledge Media Institue, UK

Thesis: Semantic Sentiment Analysis of Microblogs

A study that leveraged Semantic Web techniques together with Machine Learning and NLP to enhance sentiment analysis performance on social media. I designed several methods to enrich both supervised and unsupervised Machine Learning models with the conceptual and contextual semantics of words. Additionally, I developed SentiCircles, a semantic vector representation of words that uses Trigonometry and Euclidian Geometry to better assign sentiment to words based on their contextual and conceptual meanings. The scientific community has made extensive use of SentiCircles to improve both traditional and deep learning models for sentiment analysis.

The doctoral thesis was honoured with the prestigious International Semantic Web Distinguished PhD Thesis award (Japan, Kobe 2016) and was later published as a book in 2017.

B.Sc. in Computer Science & Artificial Intelligence

2003 - 2008

Damascus University, Damascus, Syria

Studied key courses including Machine Learning, Deep Neural Networks, Programming Languages, Game Theory, Applied Mathematics, and Software Engineering.

Senior Data Science Manager

2018 - Present

HSBC Group, Research & Development, Chief Compliance Office, London, UK

Lead the design and development of innovative, large-scale, and production-ready anti-financial crime systems. This is an interdisciplinary role at the intersection of quantitative analysis/ML, software development, and pipeline infrastructure engineering. I am also responsible for managing multiple cross-jurisdiction analytics teams, as well as preparing and delivering a broad range of technical briefings for a range of senior HSBC executives. Key responsibilities and achievements include:

• Managing the design, development, and delivery of HSBC's global anti-financial crime ML-based ecosystems.
• Working with extremely large, complex data sets (+30Bn records), generating insights, and identifying and modelling historical and new emerging financial crime risk behaviours and trends.
• Building an end-to-end NLP-powered ML pipeline for identifying confirmed financial crime risk typologies within a global population of the HBSC known-bad actors. The developed pipeline helped deliver auto-generated gold-standard evaluation datasets for model training and evaluation.
• Building ML-based anti-money laundering models that are ~5 times better and detect risk ~0.7 months faster than existing systems.
• Working jointly with IT and engineering teams on the integration and deployment of large-scale ML pipelines.
• Leading model performance analysis and evaluating business metrics to generate actionable insights and provide compelling visualisations for both business and technical stakeholders to support evidence-based decision-making processes within the bank.
• Managing key stakeholder relationships to determine and translate business needs into research requirements and development workflows.