My main research interests are Machine Learning, Data Mining and Data Science. I’m interested in predictive analytics with a special focus in cost-sensitive/utility-based predicitive analytics, imbalanced domains learning, anomaly detection, fraud detection and rare extreme values forecasting.

Utility-based Predictive Analytics

    * Utility-based learning problems
    * Cost-sensitive learning problems
    * Performance Evaluation

Imbalanced domains

    * Strategies for dealing with imbalanced domains
    * Imbalanced Regression
    * Imbalanced Time Series
    * Imbalanced Data Streams
    * Performance Evaluation on imbalanced domains

Rare Events Mining

    * Outlier detection
    * Anomaly detection
    * Fraud detection
    * Rare extreme values forecasting

Real-world Applications

The problems that I’ve been addressing in the last years have many important real-world applications. Examples of such applications are:

Cybersecurity

	* Malware detection
	* Intrusion Detection
	* Network Traffic Anomaly Detection
	* Misuse/Signature Detection

Health Care Problems

	* Rare disease detection
	* Cancer Prediction
	* Diabetes Prediction
	* Forecasting/anticipating heart diseases

Spatio-temporal data

	* Fores fires forecasting
	* Monitor distribution and health of aquatic species
	* Recommend locations for exploration of mineral resources

Failure and fraud detection

	* Failure detection in sensors data
	* Anticipating equipments interventions
	* Fraud detection applications such as:
		* credit card transactions
		* insurance claims
		* email phishing

Ecological/Meteorological data

	* prediction of abnormal values in ecological indicators
	* anticipation of critical phenomena related with air or water quality
	* forecasting weather extreme events such as:
		* floods
		* heavy snowfall
		* black ice
		* heat waves