DeWolff Consulting in data science

Services

Data science projects for industry and organizations. We develop applications and models to solve intelligence problems using state-of-the-art knowledge in the field of artificial intelligence and machine learning. Our solutions help business decision-making by extracting key information from data and predicting the likelihood of future events. This helps to minimize risks and improve efficiency. We also provide data science and artificial intelligence training for teams.

Models and applications

We specialize in creating solutions for clients who need to extract key information from their processes. With large quantities of data available in databases, we can draw conclusions, predicts events, condense information, impute missing data, find trends, etc. in order to make smart decisions and save human resources.

Using various objectives (regression, classification, clustering, etc.) and models (neural networks, Gaussian processes, auto encoders, etc.) to respond to questions such as:

When will my system fail to anticipate and prevent the failure?
How can I minimize risks when giving out loans and credits to clients?
Where is the highest likelihood of finding a mineral for mining?
Where to focus efforts to mitigate forest fires and deforestation?
How to optimize the production of agricultural products with respect to climate?

Courses

We offer introductory courses in the area of data science and machine learning to educate teams in artificial intelligence concepts. AI knowledge helps in understanding how models function and how important the role of data is.

During the course we explain a variety of subjects, such as basic machine learning concepts, how key algorithms function, what is understood with high quality data and how they are vital in training a model, and the workflow for training and using models. Furthermore, we provide basic Python skills, its libraries and fundamental statistics.

Team

Taco de Wolff
Data scientist
Master in Physics (Univerity of Groningen)
Data scientist (CMM, University of Chile)

Alejandro Cuevas
Data scientist
Master in Applied Mathematics (University of Chile)
Machine Learning engineer (NoiseGrasp)

Projects

VZOR Brain

Prediction of system failures from alerts and logs of servers and applications. Using alerts from CPU, memory, connections, etc. of critical use or failure, a classifier learns to predict if the system will break down soon and which components are responsible. We use an XGBoost classifier together with LDA to extract topics from the alert messages, SMOTE to balance the data, and LIME to interpret the results.

Multi-output Gaussian process toolkit

Development of a Python and PyTorch toolkit for regression and classification using Gaussian processes for multi-output. The library implements from loading and manipulating the data, the initialization and training of hyperparameters, to the visualization and interpretation of the model. The toolkit has a great variety of models, likelihoods, and kernels implements, and allows high-performance training on the GPU.

GitHub repository: https://github.com/GAMES-UChile/mogptk

Computationally-efficient initialisation of GPs: The generalised variogram method,
F. Tobar & E. Cazelles & T. de Wolff, TMLR (2022)
MOGPTK: The multi-output Gaussian process toolkit,
T. de Wolff & A. Cuevas & F. Tobar, Neurocomputing (2021)
Gaussian Process Imputation of Multiple Financial Series,
T. de Wolff & A. Cuevas & F. Tobar, ICASSP - IEEE (2020)

BancoEstado course

BancoEstado Together with the Center for Mathematical Modelling from the University of Chile, we have designed a machine learning course including: introduction to artificial intelligence, basic usage of scientific Python (NumPy, SciPy, Pandas, scikit-learn, PyTorch), regression, classification, optimization, clustering, support vector machines, K-nearest neighbours, cross-validation, neural networks, random forests, XGBoost, Bayesian networks and graphs. Furthermore, the course was concluded with an implementation of a cost-sale model from real bank data on credits.