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(Interests)
(Interests)
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I am interested in everything that can help me finding and understanding the patterns hidden in our world.
 
I am interested in everything that can help me finding and understanding the patterns hidden in our world.
Since I found out that boosting statistical models with parallel computers can be pretty efficient at this task, I have made it my job to understand how machine learning systems work, how to combine them and how to apply them to real-world problems.
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Since I found out that boosting statistical models with parallel computers can be pretty efficient at this task, I have made it my job to understand how machine learning systems work and how to apply them to real-world problems.
 
My research interests lie at the intersection of mathematical analysis, stochastic optimisation, algorithms, and parallel programming.
 
My research interests lie at the intersection of mathematical analysis, stochastic optimisation, algorithms, and parallel programming.
  

Revision as of 17:38, 19 June 2021

Matteo sp.JPG

Matteo Spallanzani

I received my PhD in applied mathematics in February 2020 from the University of Modena and Reggio Emilia (UniMoRe), Italy, with a thesis on "A framework for the analysis of machine learning systems". While at UniMoRe, I have contributed to create the machine learning division of the High-Performance Real-Time (HiPeRT) laboratory led by professor Marko Bertogna. I have been working on data science and machine learning for a while, fields in which I have also had some industrial experiences either as an intern or a scientific consultant (Tetra Pak, Maserati, Advertima).

Interests

I am interested in everything that can help me finding and understanding the patterns hidden in our world. Since I found out that boosting statistical models with parallel computers can be pretty efficient at this task, I have made it my job to understand how machine learning systems work and how to apply them to real-world problems. My research interests lie at the intersection of mathematical analysis, stochastic optimisation, algorithms, and parallel programming.

At IIS, I am working on quantized neural networks (QNNs), applying neural architecture search (NAS) to improve their function approximation properties, developing new algorithms to improve their learning process, and crafting tools to deploy them on resource-constrained computing platforms.

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