Talk by Prof. Carla Fabiana Chiasserini on "Recipes to Make Sustainable Machine Learning"

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Title: Recipes to Make Sustainable Machine Learning  
 
Abstract: 
The training and execution of Machine Learning (ML) models pose relevant challenges, as they often require substantial computational and energy resources. To mitigate the ML footprint on the computational resources of single network nodes, new distributed ML paradigms have emerged, as they can distribute the computational load across different network nodes, besides providing better privacy preservation. However, despite such advantages, distributed ML too may face significant hurdles, namely, (i) slower execution of the ML training or inference, and (ii) high bandwidth consumption on the links connecting the participating network nodes whenever the output of intermediate processing has to be transferred from one node contributing to the ML pipeline to another. In this talk, we discuss such issues and how they can be solved by leveraging dynamic neural networks. 

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  • IIT Delhi, Hauz Khas
  • New Delhi, Delhi
  • India 110016
  • Building: Bharti School
  • Room Number: IIA-101
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  Speakers

Prof. Carla Fabiana Chiasserini

Topic:

Talk on "Recipes to Make Sustainable Machine Learning" by Prof. Carla Fabiana Chiasserini

Title: Recipes to Make Sustainable Machine Learning  
 
Abstract: 
The training and execution of Machine Learning (ML) models pose relevant challenges, as they often require substantial computational and energy resources. To mitigate the ML footprint on the computational resources of single network nodes, new distributed ML paradigms have emerged, as they can distribute the computational load across different network nodes, besides providing better privacy preservation. However, despite such advantages, distributed ML too may face significant hurdles, namely, (i) slower execution of the ML training or inference, and (ii) high bandwidth consumption on the links connecting the participating network nodes whenever the output of intermediate processing has to be transferred from one node contributing to the ML pipeline to another. In this talk, we discuss such issues and how they can be solved by leveraging dynamic neural networks. 

Biography:

Carla Fabiana Chiasserini received her Laurea Degree (110/110 summa cum laude) in Electronic Engineering from the University of Florence in 1996, and her Ph.D. in Electronic Engineering and Telecommunications from Politecnico di Torino in 2000. Since 2000, she has been with the Electronics and Telecommunications Department at Politecnico di Torino, where she is currently Full Professor. She is also a Research Associate with the Italian National Research Council (CNR). Carla was a Visiting Researcher at UC San Diego from 1998 to 2003, and a Visiting Professor at Monash University in 2012 and 2016. She is a Fellow of the IEEE. Her research interests include 5G Networks, Mobile Edge Computing, IoT, and Connected Vehicles. She has published over 320 journal articles and referred conference papers, and has received several awards for her scientific work. Currently, she serves as Editor-in-Chief of the Computer Communications journal and as an Associate Editor of the IEEE/ACM Transactions on Networking. Carla is also a member of the Steering Committee of the IEEE Transactions on Network Science and Engineering and of the ACM MobiHoc conference, and member of the IEEE Emerging Topics Committee. She has served for several years on the Editorial Board of journals such as the IEEE Transactions on Wireless Networks and the IEEE Transactions on Mobile Computing, and she has been Co-Guest Editor of a number of journals special issues. Carla has been either the coordinator or Lead PI for may international research projects. Her teaching duties at Politecnico di Torino include graduate-level courses on wireless communications and advanced topics on wireless networks. From 2012 till 2018, she has been the Coordinator of the Master Program in Communications and Computer Engineering at Politecnico di Torino. She is Vice Rector for Alumni and Career Orientation.




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