I am a ML scientist with 6y+ experience and a passion about ML frameworks development. I enjoy finding simple, interpretable solutions to problems and coding them in a modular way. I believe in FOSS and I look forward to making contributions.
- I have been working on a number of tabular and time series tasks. At the moment, I make use of smart grid data to take on tasks such as anomaly detection, classification, and forecasting.
- I am enthusiastic about the philosophy and simplicity of the scikit-learn API. I have developped a number of packages extending and/or integrating with it.
- In the past couple years, I have been excited about uncertainty quantification and especially conformal prediction!
- I have a diverse background spanning engineering, applied math, physics, and high-performance computing.
- During my PhD, I studied turbulence models and evaluated them numerically using metaLBM, a C++ simulation package running on GPU clusters using MPI, OpenMP, and CUDA.
- During my postdoc at EPFL, I created giotto-tda, an open-source Topological Data Analysis library for feature engineering and unsupervised learning extending scikit-learn.