Marco Trincavelli

Marco Trincavelli received both his BSc degree (2003) and his MSc degree (2006) in computer engineering from the Politecnico di Milano, Milan, Italy. He additionally received an MSc degree in electrical engineering and computer science from the Lund Tekniska Högskola, Lund, Sweden, in 2006. From 2010–2013, after receiving his Ph.D. in Computer Science from Örebro University in Sweden (2010), he served as a postdoc at the Center for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.

From 2014-2018, he worked at Scania CV AB, designing the perception system of autonomous trucks. He then moved to Raysearch Laboratories AB, where he worked on applying machine learning to radiation therapy planning, and now works as Principal Data Scientist at H&M Group GBC AB, where he drives the research initiatives in artificial intelligence. His research interests include artificial intelligence, machine learning, mathematical optimization, and robotics.


Zero To Hero Tutorial on a Deep Learning Classification Task
Georgios Deligiorgis, Marco Trincavelli, David Andersson

Live Stream:

This workshop will demonstrate a zero-to-hero tutorial on how to solve a classification task using deep learning. The tutorial kicks off demonstrating a simple classification task on synthetic data, first in low and then in high dimension. Then, a harder classification task based on FashinMNIST, a famous dataset containing images of clothes, will be tackled. Apart from solving the classification task itself, we will show how to generate and analyze embedding vectors that can be used to solve other downstream tasks, different from the original classification problem on which the model was trained. Finally, we are going to face a more advanced type of classification problem, namely, predicting links on a graph using Graph Neural Networks. Link prediction will be demonstrated on an open source dataset that contains information about collaborations among authors of scientific papers. The target of this workshop is to show how we can use Python to solve the the aforementioned tasks, taking into account both the data science aspects and the engineering and project lifecycle related ones. In particular, the python packages that we are going to cover in the workshop are PyTorch, PyTorch-Lightning, Deep Graph Library.

Data Science, AI, and Machine Learning