Enrique Pardo García
Data Scientist + ML Engineer, Data Engineer, Computer Vision, NLP Specialist, and Deep Learning
Maritime Auditory Intelligence — Datathon 2026
End-to-end maritime ML platform for anomaly detection, risk classification, temporal forecasting, and geographic clustering of maritime incidents. Six specialised models trained on real AIS data. Awarded 2nd place at ITG & UDC Datathon 2026.
Pathological Fluid Segmentation in OCT
Ablation study on U-Net architectures for retinal fluid segmentation in Optical Coherence Tomography images. Compares encoder backbones, loss functions, and data augmentation strategies with rigorous cross-validation.
Road Segmentation from Aerial Images
Deep learning pipeline for automatic road network extraction from high-resolution satellite imagery. Encoder-decoder CNN with combined loss and sliding window inference for arbitrary-resolution inputs.
Object Recognition with Pixel-Level Masks
Computer Vision pipeline for multi-class object recognition using CNNs with pixel-level segmentation masks and data augmentation. Produces dense per-pixel class maps rather than bounding boxes.
Clinical Trial Information Retrieval System
NLP-based search engine for biomedical text combining lexical retrieval with neural semantic re-ranking. Lucene indexing and BM25 for first-stage retrieval, BioBERT for passage relevance scoring.
IoT Malware Detection with PySpark
Distributed big data analytics for cybersecurity. Apache Spark Structured Streaming processes network traffic in real time, Isolation Forest detects behavioural anomalies, and gradient boosting classifies malware families.
Maritime Visual Classification at Smartports
Multi-task deep learning for smart port operations — simultaneous ship type and docking status classification from port camera feeds, with ablation studies over backbone architectures and training strategies.
Enrique Pardo García
I'm a Data Scientist & Data Engineer from Palencia, Spain, currently studying at the Universidade da Coruña. I build scalable, data-driven systems across computer vision, NLP, distributed computing, and real-time analytics — transforming raw data into intelligent, production-ready outcomes.
My work spans deep learning for computer vision, natural language processing and information retrieval, distributed data engineering, and predictive modelling — from biomedical image segmentation and satellite road extraction to IoT anomaly detection and large-scale maritime ML. Recognised with 2nd place at Datathon 2026 (ITG & UDC). See my full stack or download my CV.
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