Gowtham Muniraju, PhD

Student Name: Gowtham Muniraju, PhD 

Email: [email protected]

Biography

Gowtham Muniraju received his PhD at Arizona State University, co-advised by Dr. Andreas Spanias and Dr. Cihan Tepedelenlioglu. He completed B.E. degree in electronics and communications engineering from Visvesvaraya Technological University, India, in 2016. His research interests include distributed computation in wireless sensor networks, distributed optimization, computer vision and deep learning. In summer 2018, he interned at Lawrence Livermore National Laboratory, where he worked on developing optimal sampling techniques for improved hyperparameter optimization. In recent years, his research involves statistical parameter estimation and clustering in distributed wireless sensor networks.
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Background and Research Interests:

“I received my PhD from the Department of Electrical Engineering, Arizona State University, advised by Dr Andreas Spanias and Dr Cihan Tepedelenlioglu. I completed by Bachelors of Engineering Degree from Visvesvaraya Technological University, India in July 2016, soon after, I joined Arizona State University to pursue my Higher Studies. As an Undergraduate Student, I did an Internship at BSNL, India for 1.5 years and Completed my Undergraduate thesis with the assistance of DEBEL Lab, DRDO, India.

My Research Interests are in the Domains of Wireless Communication and Machine learning; Massive MIMO, Wireless Sensor Networks, Neural Nets, Reinforcement Learning. Currently, I am Working as a Graduate Research Assistant for SenSIP ASU, Under the project: CPS: Synergy: Image Modelling and Machine Learning Algorithms for Utility-Scale Solar Panel Monitoring. My research for this project is based on implementing Distributed Consensus Concept to estimate Parameters, Detect Faults in Solar PV grids. “

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Research:

A distributed wireless sensor network (WSN) is a network of large number of low-cost, multi-functional sensors with power, bandwidth, and memory constraints, operating in remote environments with sensing and communication capabilities. WSNs is a source for large amount of un-labelled data, and due to the inherent communication and resource constraints, developing a distributed algorithm to perform statistical parameter estimation and data analysis is necessary. In this regard, we have devised a distributed spectral clustering algorithm to group the sensors based on location attributes. We also developed a novel distributed max consensus algorithm robust to additive noise in the network. The work on estimating the spectral radius of the network in a distributed way is in progress. Algorithms we develop can be easily integrated in the existing sensor networks and will have a broader impact on the large scale distributed networks.

Research Picture:

List of Publications

  1. G. Muniraju, S. Rao, S. Katoch, A. Spanias, C. Tepedelenlioglu, P. Turaga, M. K. Banavar, and D. Srinivasan, “A cyberphysical photovoltaic array monitoring and control system,” International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), vol. 5, no. 3, pp. 33–56, 2017.
  2. X. Zhang, M. K. Banavar, C. Tepedelenlioglu, A. Spanias and G. Muniraju. “Location Estimation and Detection in Wireless Sensor Networks in the Presence of Fading.” In Physical communication journal, Elsevier, 2018.
  3. G. Muniraju, C. Tepedelenlioglu and A. Spanias, ” Analysis Max Consensus in the presence of additive noise.” (To be submitted)

Conferences:

  1. G. Muniraju, S. Zhang, C. Tepedelenlioglu, M. K. Banavar, A. Spanias, C. Vargas-Rosales, and R. Villalpando-Hernandez, “Location based distributed spectral clustering for wireless sensor networks,” in IEEE Sensor Signal Processing for Defence Conference (SSPD), 2017.
  2. G. Muniraju, C. Tepedelenlioglu, A. Spanias, S. Zhang, and M. K. Banavar, “Max consensus in the presence of additive noise,” in IEEE Asilomar Conference on Signals Systems and Computers, 2018.
  3. S. Katoch, G. Muniraju, S. Rao, A. Spanias, P. Turaga, C. Tepedelenlioglu, M. Banavar, and D. Srinivasan. “Shading prediction, fault detection, and consensus estimation for solar array control.” In 2018 IEEE Industrial Cyber-Physical Systems (ICPS), pp.217-222. IEEE, 2018.

Acknowledgements: 

I acknowledge the SenSIP Center of Electrical Engineering for the excellent advisory and support.