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== Matteo Spallanzani ==
 
== Matteo Spallanzani ==
I received my PhD in applied mathematics in February 2020 from the University of Modena and Reggio Emilia (UniMoRe), Italy.
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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.
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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".
I have been working on data science and machine learning for a while, starting as a Data Science Intern at Tetra Pak Packaging Solutions
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I have been working on data science and machine learning for five years, both in academia and industry (Tetra Pak, Maserati, Advertima).
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== Interests ==
 
== Interests ==
I am interested in everything that can help me finding and understanding the patterns hidden in this world.
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Since I found out that boosting statistical models with parallel computers can be pretty efficient at this, I have made it my job to understand how machine learning systems work, hot to combine them and how to apply them to real-world problems.
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I am interested in everything that can help me finding and understanding the patterns hidden in our world.
My current research interests lie at the intersection of mathematical analysis, stochastic optimisation and parallel programming.
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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.
In particular, I am now working on quantized neural networks, applying neural architecture search (NAS) algorithms to improve their topology and approximation proerties, and studying new algorithms to improve their learning process.
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My research interests lie at the intersection of mathematical analysis, stochastic optimisation, algorithms, and parallel programming.
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At IIS, I am working on quantised neural networks (QNNs): applying data science techniques to know how good networks look like, developing new algorithms to improve their learning process, and crafting tools to deploy them on resource-constrained computing platforms.
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== Contact Information ==
 
== Contact Information ==
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* '''Office''': ETZ J76.2
 
* '''Office''': ETZ J76.2
 
* '''telephone''': (+41 44 63) 384 70
 
* '''telephone''': (+41 44 63) 384 70
 
* '''e-mail''': [mailto:spmatteo@iis.ee.ethz.ch spmatteo@iis.ee.ethz.ch]
 
* '''e-mail''': [mailto:spmatteo@iis.ee.ethz.ch spmatteo@iis.ee.ethz.ch]
 
* '''www''': [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUxODcz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Matteo Spallanzani (ETH page)]
 
* '''www''': [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUxODcz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Matteo Spallanzani (ETH page)]
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== Available Projects ==
 
== Available Projects ==
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[[Category:Supervisors]] [[Category: Deep Learning Acceleration]] [[Category: Software]]
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[[Category:Deep Learning Acceleration]]

Latest revision as of 20:56, 24 September 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". I have been working on data science and machine learning for five years, both in academia and industry (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 quantised neural networks (QNNs): applying data science techniques to know how good networks look like, developing new algorithms to improve their learning process, and crafting tools to deploy them on resource-constrained computing platforms.


Contact Information


Available Projects

No pages meet these criteria.