Rajasekhar Anguluri

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

Research Interests: I like to know how engineering systems work at the basic level. This steers me to do research in systems theory and statistics. My current research identifies situations where data science methods can address estimation, identification, and control problems in large-scale systems (e.g., power and mechanical systems). Situations:

  • estimation and control
    • sparse state and input estimation in linear dynamical systems
    • structured system identification in network systems
    • statistical inference in engineering graphical models
    • secure networked cyber-physical 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.

Free time: I listen to audiobooks on philosophical aspects of ethics and science; or muse about the stupid things I did as a teen (12-18 years); or amuse about the dense hair I had ages ago. In the hope of becoming an efficient writer, I read many books/articles on writing (fact: It took me ten attempts to write the prior sentence). If you like to chat about research or philosophy or writing or communication, drop me a line via email.


Sep 27, 2023 I presented our work on structure learning in conservation networks at SYSCON in IIT-Bombay and EE Dept in IIT-Kanpur :sparkles:
Aug 16, 2023 I presented our work on source localization using sub-space identification methods at 2023 IEEE CCTA :sparkles:
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:

selected publications

    1. [J] IEEE TCNS
      Localization and Estimation of Unknown Forced Inputs: A Group LASSO Approach
      Rajasekhar Anguluri, O. Kosut, and L. Sankar
      IEEE Transactions on Control of Network Systems 2023
    2. [C] NeurIPS
      Learning the structure of large networked systems obeying conservation laws
      Anirudh Rayas, Rajasekhar Anguluri, and Gautam Dasarathy
      Advances in Neural Information Processing Systems 2022
    3. [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
    4. [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