Rajasekhar Anguluri

Postdoc @ Arizona State University. Brews simple math from engineered systems

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rangulur[at]asu[dot]edu(he/him/his)

Academic CV (08/2022)

Google Scholar

I’m interested in how engineering systems work at the deepest levels, which steers me to do research in systems theory. My current research identifies situations where estimation, identification, and control problems in large-scale systems (e.g., power and mechanical systems) can be solved using data science methods with theoretical guarantees. Situations:

  • estimation and control
    • sparse state and input estimation in dynamical systems
    • controllability of coarse dynamical systems
    • structured system identification
    • structure learning in graphical models
    • attack design and detection in network systems
  • applications to power systems
    • parameter estimation in low-inertia systems
    • topology learning in distribution grids
    • oscillations and cyber-attack detection/localization/mitigation in bulk systems

Academic roots: I’m Postdoc-ing in the School of ECEE at Arizona State University, where I work with Lalitha Sankar, Oliver Kosut, and Gautam Dasarathy. I received a PhD in Mechanical Engineering from University of California, Riverside. My thesis (security of stochastic dynamical systems) supervisor was Fabio Pasqualetti. At UC Riverside, I took a bunch of courses in Statistics and earned an MS degree, but I remember only 51.32% of the course material.

In my free time, I either listen to audiobooks on philosophical aspects of ethics and science or remember my early adolescent memories or think about the dense hair I had ages ago. If you like to chat, drop me a line via email.

news

Oct 20, 2022 Research Grant. Mistletoe Research Fellowship. Momental Foundation, $10,000, June 2022 - June 2023 :sparkles:
Oct 9, 2022 Our paper on inertia and damping estimation in low-inertia power systems has been accepted at NAPS 2022 :sparkles:
Sep 14, 2022 Our paper on structure learning in high-dimensional networks has been accepted at NeurIPS 2022:sparkles:
Jul 17, 2022 I presented a poster at the PES General Meeting about Complex-valued LASSO for Forced Oscillation Localization :sparkles:
Dec 15, 2021 I presented our work on Distribution Networks Topology Estimation under Hidden Nodes at the IEEE CDC :sparkles:

selected publications

  1. [J] NeurIPS
    Learning the Structure of Large Networked Systems Obeying Conservation Laws
    A. Rayas, Rajasekhar Anguluri, and G. Dasarathy
    2022 (accepted at NeurIPS)
    arXiv PDF Code
  2. [J] IEEE TCNS
    Localization and Estimation of Unknown Forced Inputs: A Group LASSO Approach
    Rajasekhar Anguluri, L. Sankar, and O. Kosut
    2022 (accepted at IEEE Transactions on Control of Network Systems)
    arXiv PDF
  1. [J] IEEE L-CSS
    Grid Topology Identification With Hidden Nodes via Structured Norm Minimization
    Rajasekhar Anguluri, G. Dasarathy, O. Kosut, and L. Sankar
    IEEE Control Systems Letters 2021
    Bib PDF Website
  2. [J] IEEE TAC
    Centralized versus decentralized detection of attacks in stochastic interconnected systems
    Rajasekhar Anguluri, V. Katewa, and F. Pasqualetti
    IEEE Transactions on Automatic Control 2019
    arXiv Bib PDF Website