Name: #35 MLMilan: Unsupervised vehicle matching and ML for Healthcare
Start: 2022-06-16T18:45:00+02:00
End: 2022-06-16T20:15:00+02:00

Dear all,

ML Milan is back in June where we will explore two use cases, one on vehicles matching and one on pharma. The event will be held online via Zoom and the talks will be held in English.

Talk 1: “Unsupervised vehicle matching and MLOps”

Unsupervised vehicle matching based on vehicle attributes is a yet unsolved problem that can be approached by many different angles, such as high dimensional distance matching or autoencoders. In the first part of the presentation we will focus on our journey towards a human-in-the-loop solution, while in the second part we will talk about the journey of an ML model from development to production.
Clemente De Rosa
Master in Actuarial and Financial Mathematics, Phd in Financial Mathematics. About 9 years of combined Academic and Industry. At Swiss Re applies his Data Science and Actuarial expertise to the motor insurance domain.

Tobias Tekampe
PhD in experimental particle physics, applied data science techniques to data of the LHCb
experiment. After a short period as a data scientist at a startup joined Swiss Re as ML engineer where he works on varying tasks from backend development to data science.

Alessandro Gianfelici
Postgraduate degree in Financial Mathematics and Master’s degree in Theoretical Physics
from Bologna University. After more than six years in the energy industry, he joined Swiss
Re as senior data scientist in the Advanced Analytics team at the beginning of 2022.

Talk 2: “Machine Learning for Healthcare Decision-Making”

Good decision-making in hospitals can literally mean the difference between life and death. Doctors are often tasked with taking split-second decisions on whether they should try a specific procedure on a patient, especially when someone shows up in a hospital in critical conditions. Using Machine Learning (ML), and predictive analytics in particular, we can build tools that can be used by doctors to quickly make personalized predictions on adverse outcomes, answering questions such as “Should I perform this specific procedure on this specific patient?”. In this talk, we will explore a tool developed at MIT that wraps advanced, interpretable ML models with an iPhone app that doctors can use to get live predictions on children’s survival of surgery.

Eugenio Zuccarelli, CVS Health
Eugenio is a NYC-based Data Science Leader at CVS Health, a Fortune 500 company and the #1 healthcare company in the world. He is a Forbes 30 Under 30, a Fulbright Scholarship recipient and studied across MIT, Harvard and Imperial College. Eugenio’s analyses helped develop policy recommendations for The White House, especially to fight the COVID-19 pandemic. His work can be found across Forbes, Fortune, The Washington Post, Bloomberg, Financial Times as well as multiple journals and on the App Store.

Where? Join us on Thursday 16th of June on Zoom at this link: [https://zoom.us/j/96756750884?pwd=MXgvTmlNNS9KMC90UThrbGsvb3FZQT09](https://zoom.us/j/96756750884?pwd=MXgvTmlNNS9KMC90UThrbGsvb3FZQT09) . The event will start at 18:45 and will finish around 20.00

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#35 MLMilan: Unsupervised vehicle matching and ML for Healthcare

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