Kaixo! I'm Aritz, a statistician at DCB (Diabetes Center Berne).
My background is in physics and statistics, and I enjoy exploring state-of-the-art statistical- & data science methods. My favorite programming languages are R and Python.
If you keep scrolling down, you will stumble upon some of my projects, as well as more information regarding my background and skills.
The Standard Model of particle physics, while being able to make accurate predictions, has been proved to fail to explain various phenomena, such as astronomical dark matter observations. In this project, a machine learning application is implemented with the goal of studying dark matter candidates. Images from Charge Coupled Devices (CCDs) in different experiments located underground are used to test different deep learning algorithms. A U-Net model is trained with Python's open-source library Keras. The model performs multi-class image segmentation in order to detect dark matter particle signals among background noise.
Original title: Application of deep learning techniques to images collected with Charge Coupled Devices to search for Dark Matter.
More informationImage recognition implementation with Keras. A CNN is built and trained with the CIFAR-10 dataset. Two models are trained: one without data-augmentation (77.25% accuracy) and the other with data-augmentation (78.04% accuracy).
More informationAn implementation of "A Simple Random Walk Model for Predicting Track and Field World Records"
A prediction model is built for the men's 100m world record, and human performance limits are estimated using Monte Carlo simulations.
The goal of this study is to fit an ARIMA model to forecast the EUR/CHF currency exchange rate. Different models are obtained through two trend removal methods: a linear model, and differencing.
More informationA Generalized Linear Mixed Model (GLMM) is fitted in order to study the correlated observations in longitudinal data of patients suffering from schizophrenia. The model parameter estimates reveal that ’male’ patients evolve less favorably than ’female’ patients, while age does not have a significant effect on the evolution of prevalence of thought disorders.
More informationThe goal of this study is to determine, in an automated manner, whether new applicants present good or bad credit risk. 9 different machine learning models are trained to classify applicants based on their credit rating.
More informationA collection of games where agents are trained with Reinforcement Learning.
More informationSmall projects that I work on during my free time.
More informationDiabetes Center Berne (DCB)
Université de Neuchâtel (UniNe)
CHEQUE - Der intelligente Cloudspeicher
Universität Zürich (UZH)
Universidad de Cantabria (UC)
Swiss Epidemiology Winter School 2023
Native
Native
Native proficiency: CPE Cambridge C2
Intermediate: EOIDNA B1