Purdue Prof. Anuran Makur earns NSF CAREER award
Advancing research in information propagation across diverse domains
In today's interconnected world, the efficient transmission of information across networks is paramount to the success of various fields. Research at Purdue University is addressing a myriad of challenges in communications contributing valuable insights and solutions to a range of real-world problems.
Information propagation in large networks is a multidisciplinary field that combines information theory, statistics and computational learning theory, and graph theory to model, analyze, and optimize the spread of information in diverse contexts.
Anuran Makur, assistant professor in the Department of Computer Science and the Elmore Family School of Electrical and Computer Engineering won a National Science Foundation (NSF) CAREER award for his proposed work titled “Information Propagation over Networks.” His project explores how information spreads and dissipates in large networks, like social or communication networks, computation circuits, certain models in physics, or biological networks.
“This research tries to fundamentally understand the behavior of information flow in complex systems of inference, communication, or computation,” said Makur. He added, “Think of it as understanding how messages travel through a group of people who are talking to each other, even when there is some noise in their communication.”
Dissipation over networks
At a subcutaneous level, a common theoretical framework resides at the heart of many such problems from seemingly disparate domains. This project investigates how information originating from certain parts of a network dissipates over time as it flows through the remainder of the network. Existing analyses of this phenomenon are limited to very simple network structures and measures of information. Thus, significant development of existing ideas is necessary to model and analyze more complex problems in the aforementioned application areas.
“This research tries to fundamentally understand the behavior of information flow in complex systems of inference, communication, or computation. Think of it as understanding how messages travel through a group of people who are talking to each other, even when there is some noise in their communication.” - Assistant Professor Anuran Makur
Makur aims to create a general theory that explains how information moves through networks and dissipates over time. Right now, existing studies focus on basic network structures, but this project aims to develop more advanced ideas to model and analyze complex problems in various areas like communication, computation, statistics, and machine learning.
Emphasizing the interdisciplinary nature of this research, Makur stated, "Our work studies questions from traditionally distinct domains in a unified manner. By developing the fundamental principles governing information propagation, we can understand critical issues across all these domains."
Goals of the project
The project has two main thrusts. The first is to determine the structure of networks that allow information to spread. To achieve this, graphs where information propagation is possible will be constructed and analyzed, and general techniques to prove when such propagation is impossible will be developed.
The objective of the second thrust is to develop a foundational understanding of information contraction over graphs. Makur plans to establish new information theoretic inequalities and explore alternative methods to comprehend the contraction of information.
NSF CAREER Awards
NSF CAREER awards are the organization’s most prestigious awards given to junior faculty who embody the role of teacher-scholars through research, education and the integration of those concepts within the mission of their organizations. CAREER awards support promising and talented researchers in building a foundation for a lifetime of leadership. Receiving this award reflects this project’s merit of the NSF statutory mission and its worthiness of financial support.
Anuran Makur is an Assistant Professor in the Department of Computer Science and the Elmore Family School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA. He received his B.S. degree with highest honors from the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley (UC Berkeley), CA, USA, in 2013, and his S.M. and Sc.D. degrees from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2015 and 2019, respectively. He was a postdoctoral researcher at the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society at MIT from 2019 to 2021. His research interests include theoretical statistics and machine learning, information theory, and other areas in applied probability. He was a recipient of the Arthur M. Hopkin Award from UC Berkeley in 2013, the Jacobs Presidential Fellowship from MIT in 2013, the Ernst A. Guillemin Master's Thesis Award from MIT in 2015, the Jin Au Kong Doctoral Thesis Award from MIT in 2020, and the Thomas M. Cover Dissertation Award from the IEEE Information Theory Society in 2021.
Source: Makur earns NSF CAREER award