We are glad to have Ravi Raman as this weeks guest speaker. Ravi will talk about “A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations” this Friday, Nov 9th 3pm at CSL369.
Abstract: If you were to receive a trained machine learning model or simulation output that was purportedly created using computationally-expensive open source code based on data that was also open, would you trust it? If the peer you received it from was known to cut corners sometimes because of resource constraints, would you trust it? In the absence of fully re-running the computation yourself, which could be prohibitive, you might not. And without such trust, and any unquantified notion of “reputation”, collaboration would be out of the question. In this talk we will consider the problem of establishing reliable, distributed trust guarantees in large-scale computational systems, by designing a scalable blockchain framework for a peer-to-peer network, with focus on transparency, accountability, and scientific reproducibility in social good applications.
Bio: Ravi is a 5th year PhD candidate with the department of Electrical and Computer Engineering, working with Prof. Lav Varshney. Prior to this he received the B.Tech degree in Electrical Engineering and M.Tech degree in Communication Systems from the Indian Institute of Technology, Madras, India in 2009. He is a recipient of the Joan and Lalit Bahl fellowship of the Department of Electrical and Computer Engineering for the academic years 2017–2019. He is also an IBM Science for Social Good Fellow of the class of 2018. His work spans applications of information-theoretic methods for the design and analysis of unsupervised learning algorithms and scalable blockchain systems.