I'm Isabela, a physicist, data scientist and computer programmer.
M.Sc. in quantum information at the University of São Paulo and M.B.A in Data Science and Analytics at the University of São Paulo. I did an exchange year at the Uppsala University.
My main interests are quantum and classical information theories.
I build and develop data analysis (and exploratory analysis) in Python and R to identify patterns. I study artificial neural network architectures to improve the models' accuracy. I love to simulate and interpret nature through equations and codes.
- Programming Languages: Python, R, SQL, Mathematica, Qiskit.
Robustness of transversal quantum gates.
I study robust decision-making in machine learning algorithms using Bayesian Inference in this project. Model 1 : Bayesian inference: Clinical Trials - predicting treatment outcomes using Bayesian inference.
World Energy Outlook 2023 Free Dataset Includes world aggregated data for all three modeled scenarios (STEPS, APS, NZE) and selected data for key regions and countries for 2030, 2035, 2040, and 2050, as well as historical data (2010, 2021, 2022). We apply clustering and data cleaning to get some insights from the data.
Credit Card Fraud detection with neural networks(anomaly detection) and machine learning techniques (random forest classifier)
Enhancing the system security (classical and quantum) with neural networks
Cleaning and filling data using decision tree and k-nn techniques.
Dynamics of time-local non-Markovian master equations using Qutip Monte Carlo solver.
Deep Learning concepts and techniques: Regularization, Epochs, Batch, Hyperparameters, Cross-validation, Optimizers, etc.
Python scripts using the Visualization Toolkit (VTK) and Topology ToolKit (TTK) libraries. Tasks: visualize and explore topological features of a 3D volume and 2D scalar field datasets. 1. Probability density for the 3d electron position in a hydrogen atom and 2. 2D scalar field.
Scripts in R. Logistic Models. In this project, we explore theoretical foundations, Model specification and canonical connection functions, Binary and multinomial logistic models, Estimation of parameters, etc.
OLS. R and Python. In this project, we study fundamental concepts of Supervised ML models, such as Regression Analysis: Coefficient of Model Adjustment (R²), Parameters Estimation, Statistical Significance, etc.
Unsupervised Machine Learning Techniques (R and Python): CLUSTERING, FACTOR ANALYSIS AND CORRESPONDENCE ANALYSIS.
Course: Introduction to scientific computation - Development of a container and parallel job using OpenMP and MPI. Problem: matrix multiplication
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent