Geoffrey Chinot

I am currently a Postdoc Researcher at ETH Zurich with expertise in Statistics and Data-Science. After six years in Academia, I am looking for new challenges in the industry. I would love to bring my expertise and enthusiasm to tackle new problems.
My research interests are quite broad: 

• understanding of deep-learning
• robust statistics
• sequence modelling 
• boosting methods
• causality
• random optimisation
• optimal transport
• graphical models
• fairness in machine learning

In the last few months I have been preparing my transition from academia to industry. In particular I have been learning a lot about:

• PyTorch/Tensorflow
• XGboost/ LightGBM / Catboost
• SQL 
• More advanced used of Python 

I have spent the last 3 years as a postdoc at ETH, publishing in top journals. I have been working on different theoretical problems,  attempting to explain the success of deep learning and boosting methods. The ideas are based on the interplay between over-parametrisation, regularisation and optimisation. 

Moreover, I have been working lately on many applied problems to prepare my transition to industry. 

Having a theoretical background is an advantage. I am used to learn complex notions. Consequently, I learn fast and adapt quickly. 

I am a PhD in machine learning and statistics with 3 years of postdoc. It allows me to understand any kind of subject in this field. My strong theoretical background is useful to deeply understand the state of the art algorithms and how they are implemented in practice.