Uses
Technologies and tools I work with daily — organised from broad domains down to the specific libraries and frameworks behind each project.
Deep Learning & Computer Vision
Most of my research projects live in the deep learning and computer vision space: U-Net-based semantic segmentation of medical images (retinal fluid in OCT) and aerial roads, pixel-level object recognition, and multi-task ship classification via transfer learning from ImageNet-pretrained backbones. I work mainly with PyTorch for building and training networks — custom loops, mixed-precision, early stopping, and TorchScript export for inference. For standard backbone architectures I use torchvision (ResNet18, ResNet34) and segmentation-models-pytorch to rapidly ablate encoder/decoder combinations without boilerplate.
Image processing relies on OpenCV for preprocessing, sliding-window tiling, and morphological post-processing, and on Rasterio for reading geospatial GeoTIFF imagery with correct CRS handling. Augmentation pipelines (random crops, elastic transforms, colour jitter, Gaussian blur) are built with torchvision transforms and albumentations-style compositions.
Classical ML & Statistical Modelling
Not every problem needs a neural network. For tabular and time-series data I default to gradient boosting and ensemble methods, reaching for deep learning only when the data is high-dimensional or unstructured.
- Ensemble methods — Random Forest and XGBoost / LightGBM for classification and regression. Used in the maritime datathon (97.6% anomaly detection, 92.6% risk classification) and IoT malware pipeline (97.8% accuracy). Hyperparameter search via GridSearchCV and early stopping.
- Unsupervised & anomaly detection — Isolation Forest for IoT traffic anomaly scoring; HDBSCAN for geographic clustering of maritime incidents (handles arbitrary cluster shapes and noise, unlike K-Means).
- Time-series forecasting — Prophet for additive decomposition with seasonality components and trend changepoints, applied to temporal incident prediction in the maritime platform.
- Evaluation & validation — cross-validation pipelines, stratified splits, ROC/AUC, F1, precision, recall, and regression metrics (R²) via scikit-learn.
NLP & Information Retrieval
My NLP work centres on information retrieval over biomedical text — building search engines that combine classical lexical ranking with neural semantic understanding.
- Lexical retrieval — Apache Lucene (Java) for inverted index construction, custom biomedical text analysers, and BM25 ranking. Fast first-stage retrieval before any neural re-ranking.
- Neural re-ranking — BioBERT (via HuggingFace Transformers) fine-tuned as a cross-encoder for passage relevance scoring over clinical trial documents.
- Dense retrieval — document embeddings indexed with FAISS (IVF index) for approximate nearest-neighbour search; combined with lexical scores via Reciprocal Rank Fusion.
- Evaluation — TREC framework (MAP, NDCG@10, P@10); hybrid pipeline improved MAP from 0.31 to 0.42 over BM25 alone.
Data Engineering & Distributed Systems
When data doesn't fit in memory or needs to be processed in real time, I move to distributed systems. My go-to stack is Apache Spark via PySpark — for both batch feature engineering (IoT traffic, AIS maritime data) and real-time Structured Streaming pipelines. MLlib handles large-scale model training when keeping data in the cluster.
On the storage side: PostgreSQL for relational workloads (ETL pipelines, data quality frameworks, Row-Level Security for multi-tenant access — built during my internship at ENXENIO); MongoDB for heterogeneous document records; and Elasticsearch for full-text search at scale. Data wrangling in Python leans on pandas and NumPy before any distributed processing.
Web Development & Full-Stack
Not everything I build is a research pipeline — UptimeAI is a production SaaS I designed and shipped end-to-end. Its frontend and API layer run on Next.js 14 (App Router, Server Actions) with TypeScript and Tailwind CSS v4, deployed on Vercel.
Supabase provides managed PostgreSQL, authentication, and realtime subscriptions, with Row-Level Security enforcing per-tenant data isolation. Inngest schedules and retries the background cron jobs that poll monitored endpoints, Stripe handles subscription billing and the customer portal, and Resend, Twilio, Slack, and Telegram integrations deliver real-time alerts across email, SMS, and chat.
BI & Visualisation
Communicating results is as important as producing them. For enterprise BI I use Power BI (DAX, incremental refresh, row-level security) — the main tool during my internship at ENXENIO for financial reporting dashboards. For the Datathon 2026 maritime platform I built an interactive Next.js dashboard with geographic hotspot maps, incident timelines, and P0-P4 severity prioritisation.
For research and project reporting I use Matplotlib for training curves, confusion matrices, ROC plots, and segmentation overlays; Seaborn for distribution plots and correlation heatmaps during EDA; and Plotly for interactive geographic and time-series charts.
Languages & Dev Environment
Python is my primary language for everything ML, data, and scripting. Java for Apache Lucene IR work. R for statistical analysis and reporting (ggplot2, knitr). SQL for analytical queries, window functions, and ETL orchestration. TypeScript/JavaScript for the Next.js maritime dashboard and the UptimeAI SaaS platform.
Day-to-day: VS Code for Python, TS/JS (Pylance, Jupyter, Ruff, Docker extensions); IntelliJ IDEA for Java (Maven, debugger); Jupyter Lab for EDA and experiment notebooks; Docker for reproducible environments; Git with conventional commits; Postman for API endpoint testing.
Full stack at a glance
| Languages | Python · Java · R · SQL · TypeScript · JavaScript |
|---|---|
| Deep Learning | PyTorch · torchvision · segmentation-models-pytorch · U-Net · ResNet18/34 · TorchScript |
| Computer Vision | OpenCV · Rasterio · albumentations · sliding-window inference |
| Classical ML | scikit-learn · XGBoost · LightGBM · Prophet · HDBSCAN · Isolation Forest |
| NLP & Search | HuggingFace Transformers · BioBERT · Apache Lucene · BM25 · FAISS · RRF |
| Distributed / Streaming | PySpark · Apache Spark · Structured Streaming · MLlib |
| Databases | PostgreSQL · Supabase · MongoDB · Elasticsearch |
| Data wrangling | pandas · NumPy · ETL pipelines · data quality frameworks |
| Web / Full-Stack | Next.js 14 · TypeScript · Tailwind CSS · Supabase · Inngest · Stripe |
| BI & Visualisation | Power BI · Next.js · Matplotlib · Seaborn · Plotly |
| Dev tooling | Docker · Git · VS Code · IntelliJ IDEA · Jupyter Lab · Postman |
| OS | Windows 11 · Linux (Ubuntu / Debian) |
Curriculum Vitae
Full details on education, experience, and projects. CV_PardoGarcia_Enrique_EN.pdf