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(Created page with "==Short Description== Abstract of the project ===Status: Available === : Looking for 1-2 Semester/Master students : Contact: Mathieu Luisier ===Prerequis...")
 
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==Short Description==
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[[File:Matteo_Spallanzani.jpg|frameless|right|128px]]
Abstract of the project
 
  
===Status: Available ===
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== Matteo Spallanzani ==
: Looking for 1-2 Semester/Master students
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I received my PhD in applied mathematics in February 2020 from the University of Modena and Reggio Emilia (UniMoRe), Italy.
: Contact: [[:User:Mluisier | Mathieu Luisier]]
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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.
===Prerequisites===
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I have been working on data science and machine learning for a while, starting as a Data Science Intern at Tetra Pak Packaging Solutions
: VLSI I
 
: VLSI II (''recommended'')
 
<!--  
 
===Status: Completed ===
 
: Fall Semester 2014 (sem13h2)
 
: Matthias Baer, Renzo Andri
 
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===Status: In Progress ===
 
: Student A, StudentB
 
: Supervision: [[:User:Mluisier | Mathieu Luisier]]
 
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===Character===
 
: 20% Theory
 
: 40% ASIC Design
 
: 40% EDA tools
 
  
===Professor===
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== Interests ==
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] --->
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I am interested in everything that can help me finding and understanding the patterns hidden in this world.
 +
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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My current research interests lie at the intersection of mathematical analysis, stochastic optimisation and parallel programming.
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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.
  
[[#top|↑ top]]
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== Contact Information ==
==Detailed Task Description==
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* '''Office''': ETZ J76.2
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* '''telephone''': (+41 44 63) 384 70
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* '''e-mail''': [mailto:spmatteo@iis.ee.ethz.ch scheremo@iis.ee.ethz.ch]
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* '''www''': [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUxODcz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Matteo Spallanzani (ETH page)]
  
===Goals===
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== Available Projects ==
===Practical Details===
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<DynamicPageList>
* '''[[Project Plan]]'''
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supresserrors = true
* '''[[Project Meetings]]'''
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category = Available
* '''[[Design Review]]'''
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category = spmatteo
* '''[[Coding Guidelines]]'''
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</DynamicPageList>
* '''[[Final Report]]'''
 
* '''[[Final Presentation]]'''
 
  
==Results==
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[[Category:Supervisors]] [[Category: Deep Learning Acceleration]] [[Category: Software]]
 
 
==Links==
 
 
 
[[#top|↑ top]]
 
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Revision as of 19:41, 9 November 2020

Matteo Spallanzani

I received my PhD in applied mathematics in February 2020 from the University of Modena and Reggio Emilia (UniMoRe), Italy. 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, starting as a Data Science Intern at Tetra Pak Packaging Solutions

Interests

I am interested in everything that can help me finding and understanding the patterns hidden in this world. 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. My current research interests lie at the intersection of mathematical analysis, stochastic optimisation and parallel programming. 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.

Contact Information

Available Projects

No pages meet these criteria.