IE Seminar: Leveraging Blockchain as a Decentralized Computational Resource
This event is in the past.
Leveraging Blockchain as a Decentralized Computational Resource
November 19th, 11AM-12PM ET | Zoom
Seminar by Dr. Paritosh Ramanan, Georgia Institute of Technology
Abstract: Blockchains have received a lot of attention in the past decade owing to the rise of Bitcoin and other cryptocurrencies. In this talk, we focus our attention on the blockchain as a versatile computational resource with a few interesting features enabling the creation of decentralized applications (dApps). We will first provide an overview of fundamental concepts like distributed ledgers and consensus protocols that are intrinsic to blockchains. This would be followed by a discussion on how these concepts have been cobbled together to yield a computational platform for orchestrating dApps through the means of Smart Contracts. We will discuss the immediate benefits that accompany dApps as also the computational challenges that need to be addressed in order to achieve scalability. Finally, we present two of our case studies pertaining to cyber threat detection in large-scale power systems and orchestration of aggregator-free Federated Learning. We demonstrate the advantages of a blockchain-driven framework in each and also highlight the significant reduction in the computational cost of our proposed techniques in each case study.
Bio: Dr. Paritosh Ramanan is a Postdoctoral Fellow with the Georgia Institute of Technology in Atlanta, Georgia. He got his PhD in Computational Science and Engineering from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology in Atlanta, Georgia in 2020. Prior to his PhD he earned a Masters in Computer Science from Georgia State University in Atlanta, Georgia in 2015 and obtained his Bachelors in Information Systems from Birla Institute of Technology and Science (BITS) Pilani, Goa Campus in 2013. His research focuses on developing decentralized algorithms for the improved computational performance of large-scale optimization problems through the use of parallel and distributed computing paradigms.
For publication citation and impacts, see
Google Scholar https://scholar.google.com/citations?user=O9vCKwUAAAAJ&hl=en