Darya Yarparvar

The problem: clinical codelists are built by hand —slow, and easy to get wrong. The fix: an agentic RAG assistant. It routes the question, reasons in a loop, and searches PCD + SNOMED CT by keyword and meaning before an LLM drafts the codes —each with full traceability and auditability. Why it works: a clinician still checks every code, so it’s fast but stays safe.

The problem: research code rarely runs anywhere but the author’s laptop. The solution: ship the repurposing pipeline as an installable, CI-checked package. How: it ranks existing drugs for a disease. Why it matters: reproducible and deployable. Beyond a small k, recall@k is bounded not by search depth but by how completely each disease’s known drugs map onto its top associated targets.

The problem: far too many gym reviews to read by hand. The solution: let the models read them. How: BERTopic and LDA group the themes, while BERT emotion analysis scores the feeling. Why it matters: the noise becomes a ranked to-do list —broken, overcrowded equipment first, then staff and membership gripes.

The problem: flag students at risk of dropping out early enough to help —on very imbalanced data. The solution: stage the model. How: neural networks handle the weak early signals (stages 1 & 2), then XGBoost takes over once grades arrive (stage 3). Why staged: early means cheap, proactive help; later is surer but can be too late (PR-AUC 0.67 → 0.75 → 0.87).

The problem: catch a failing ship engine early, without drowning in false alarms. The solution: make three detectors agree. How: IQR, One-Class SVM and Isolation Forest each flag odd readings. Why it works: the ones caught by several methods are the real ones —just 1.09% (two methods), 0.58% (all three) —a short, trustworthy watchlist.

The problem: treating every customer the same wastes marketing. The solution: group them by behaviour. How: K-means and hierarchical clustering (with PCA / t-SNE) on recency, frequency, value and age. Why it matters: three clear segments emerge, each getting its own play to lift retention and profit.

The problem: forecast weekly book demand, spikes and all. The solution: SARIMA first, then an LSTM on top. How: SARIMA learns the season and trend; the LSTM mops up what’s left. Why the result matters: SARIMA alone is the most reliable (the data is mostly seasonal), the hybrid adds only a little —and neither can predict spikes driven by events outside the data.

A Sankey diagram to help you win —and stay one step ahead— in a game of Scrabble!