Michele Volpi
Lead Data Scientist
Academia
(Alumni)
Michele received a Ph.D. in Environmental Sciences from the University of Lausanne (Switzerland) in 2013. He was then a visiting postdoc in the CALVIN group, Institute of Perception, Action and Behaviour of the School of Informatics at the University of Edinburgh, Scotland (2014-2016). He then joined the Multimodal Remote Sensing and the Geocomputation groups at the Geography department of the University of Zurich, Switzerland (2016-2017). His main research activities were at the interface of computer vision, machine and deep learning for the extraction of information from aerial photos, satellite optical images and geospatial data in general.
Projects
CLIMIS4AVAL
In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment
deepLNAfrica
In Progress
Deep statistical learning-based image analysis for measurement of socioeconomic development in sub-Saharan Africa using high-resolution satellite images, and geo-referenced household survey data
Energy, Climate & Environment
ArcticNAP
In Progress
Exploring the natural aerosol baseline for improved model predictions of Arctic climate change
Energy, Climate & Environment
BioDetect
In Progress
Deep Learning for Biodiversity Detection and Classification
Energy, Climate & Environment
DEAPSnow
In Progress
Improving snow avalanche forecasting by data-driven automated predictions
Energy, Climate & Environment
MSEI
In Progress
Molecular structure elucidation by integrating different data mining strategies
Big Science Data
ACE-DATA
Completed
Delivering Added-value To Antarctica
Energy, Climate & Environment
Publications
Mentioned in
September 23, 2022
What you see is what you classify: black box attributions
What you see is what you classify: black box attributions
The lack of transparency of black-box models is a fundamental problem in modern Artificial Intelligence and Machine Learning. This work focuses on how to unbox deep learning models for image classification problems.
October 28, 2021
DEAPSnow | Supporting avalanche forecasting in the Swiss Alps using machine learning
DEAPSnow | Supporting avalanche forecasting in the Swiss Alps using machine learning
The creation of avalanche bulletins is still a largely expert-driven and manual task. DEAPSnow aims to explore the feasibility of using data-driven models to support the process of avalanche danger forecast.
Case Studies
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