Belal Zaky

London, United Kingdom

Dubai, United Arab Emirates

Python · propensity-score matching · self-controlled case series · bootstrap CI

• Emulated a new-user, active-comparator study and validated the estimators against a sealed true effect: the crude comparison understated the harm by 46% (OR 1.19), while matching (2.05) and a self-controlled series (2.14) both recovered the truth (2.20).
• A simulation study by design — the only setting where you can score a causal method against a known answer. Same protocol queued for real clinical data (MIMIC-IV, credentialing in progress).

Python · scikit-learn · scispaCy · SNOMED CT · pytest · GitHub Actions · Docker

• Three-stage pipeline: triage a medical abstract for a possible adverse drug event (F1 0.38 → 0.79), extract the drug and effect, then code the effect to SNOMED CT against a 461,000-term vocabulary.
• Built as a system, not a notebook — 32 unit tests, CI on every push, a Dockerfile and a CLI. Chose precision over coverage in coding: a wrong code corrupts a safety signal, a blank one does not.

Python · pandas · SQL · openFDA API · PRR / ROR / chi-squared / Evans criteria

• Built a disproportionality signal-detection pipeline over 20M+ adverse-event reports; validated on the known warfarin–haemorrhage signal (PRR ≈ 5), with negative controls to screen out confounders.
• Across companion projects (a live dashboard and a SQL reproduction), showed how notoriety and sampling distort conclusions — a heartburn drug’s 2020–21 report spike was litigation, not clinical harm.

R (survival) · Kaplan–Meier · Cox PH · Python · scikit-learn · PubMedBERT

• Benchmarked four models for detecting adverse drug events in free text: the domain-tuned transformer won, a keyword model beat a general-purpose one, and a zero-shot LLM came last — domain fit beats model size.
• Modelled time-to-hypertension in 1,100+ patients: diabetics progressed ~2.4× faster, and a seemingly null age effect proved time-varying.

Supported customer service and sales operations in a busy retail pharmacy environment, Dubai.

Coordinated client–team communication and documentation, built digital filing systems, and supported business development and social media.

Ran order fulfilment, customer communication and social-media campaigns, supporting product launches with the founders.

Represent coursemates on the Staff–Student Liaison Committee; consolidated scattered module feedback into one case that reached the department and changed the module.

Selected for King's flagship leadership programme — employer-led challenges.

Regular prepared and impromptu speaking; earned “Speech of the Meeting”.

Awarded Delegate of the Conference — research, debate and negotiation.