Málaga, Andalusia, Spain
Data Scientist with 8+ years of experience in building end-to-end data science and machine learning products in the healthcare field. Proficient at analyzing and extracting insights from sensor data and using it to build ML models. Experienced working in cross-functional teams and agile environments. Passionate about developing devices and systems that improve the health, well-being and fitness of people.
Helping members reverse and prevent chronic metabolic diseases (particularly diabetes): • Used LLMs to improve coach workflows • Implemented an automated chat message routing system using LLMs
Helping members reverse and prevent chronic metabolic diseases (particularly diabetes): • Using LLMs to improve coach workflows • Developing and improving (time-series) models to predict health biomarkers from sensor data • Developing recommendation systems (using hybrid recommenders, Neural Nets)
Predicting patient behavior to guide interventions and improve health outcomes: • Trained regression and binary classification models on data from Electronic Medical Records (EMR) and activity records (e.g. steps, heart rate, user engagement, etc.) to analyze user behavior • Analyzed a dataset of Electronic Health Records (EHRs) of over 100k colorectal cancer patients to predict the duration of chemotherapy, identifying the main factors that lead to patient drop off Creating reports to assist internal and external stakeholders: • Retrospectively analyzed and forecasted the pill consumption of cancer patients to guide digital pill production and what pharmaceuticals to digitize next • Implemented code to automatically send daily patient reports to commercial partners at scale and set it up for 8 partner hospitals
Visiting Researcher at CAMLAB (Prof. Camarillo) Applying machine learning to predict the reproductive potential of human eggs for egg cryopreservation: • Prototyped an electromechanical measurement device to identify mechanical properties of human eggs • Conducted a clinical trial with over 80 patients at In-Vitro-Fertilization (IVF) clinics in China and Taiwan • Trained binary classifiers (Random forest, SVM, Logistic Regression) that predicted egg reproductive potential with 40% higher accuracy than state-of-the-art methods (morphological assessment) • Implemented computer vision algorithms to automate cell orientation and measurement analysis Detecting successful fertilization of human eggs using deep learning: • Implemented a convolutional neural network to distinguish successful fertilized eggs from abnormal eggs Applying machine learning to detect concussions in Mixed Martial Arts (MMA): • Conducted experiments and analyzed experimental data from a mouthguard instrumented with IMUs • Applied logistic regression to the data to detect blows to the head
Research Associate at the Professorship of Sports Equipment and Materials (Prof. Senner): QuadRad Project: • Prototyped measurement devices and conducted experiments to analyze human-machine interaction while cycling • Analyzed experimental data using inferential statistics and regression testing • Collaborated with industrial and academic partners to create four-wheeled electric bicycle prototypes • Supervised theses (B.Sc. & M.Sc.), student team projects and exchange students • Wrote proposals for funding projects Supervisor of the Sports Technology Project of the Human Factors Engineering Master's program: • Supervised student teams, helped them building test equipment and taught them how to plan and conduct experiments Manager of the Test and Development Center: • Conducted structural durability tests of bicycle components made from different materials (CFRP, aluminum, bamboo) to ensure compliance with legal regulations (ISO)