Anirudh Rayas

PhD@ASU, ECEE.

prof_pic.jpg

Goldwater Centre 425

650 E. Tyler Mall

Tempe, AZ, 85281

Ph +1 (925)-922-7942

:wave: Welcome to my website!

I’m a PhD student in the School of Electrical, Computer, and Energy Engineering at Arizona State University, where I’m fortunate to be advised by Prof. Gautam Dasarathy. Before this, I earned my B.Tech degree in Electronics and Communication Engineering from PES University, Bangalore, where I completed my undergraduate thesis under the guidance of Prof. Sanjeev Gurugopinath.

My research goal is to develop novel theory and statistically principled methodologies for learning and understanding fundamental network structures in complex interactive systems. A key focus is characterizing statistical complexity, computational efficiency, estimation accuracy, and their trade-offs under realistic network constraints and data collection processes. My research interests lie at the intersection of high-dimensional statistics, graphical models, network theory, and optimization.

In my spare time, I like to read about the history of mathematics and mathematicians, I also enjoy hiking, swimming, playing chess (albeit poorly), and watching animated movies.

I am on the postdoctoral job market. Please feel free to reach out if you have an opening!.

news

Dec 04, 2024 Our paper on Learning Networks from Wide-Sense Stationary Stochastic Processes is out on arXiv. :tada:
Jul 20, 2024 Our paper on The Sample Complexity of Differential Analysis for Networks that Obey Conservation Laws has been accepted at ASILOMAR, 2024. :tada:
May 22, 2024 Our paper on Orthogonality and graph divergence losses promote disentanglement in generative models has been accepted at Frontiers in Computer Science, 2024. :tada:
May 21, 2024 Grateful for a wonderful summer internship at Los Alamos National Laboratory. Huge thanks to Dr. Deepjyoti Deka for hosting me!
Feb 15, 2023 Our paper on Differential Analysis for Networks Obeying Conservation Laws has been accepted at ICASSP, 2023. :tada:
Sep 20, 2022 Got accepted to Google’s CS Research Mentorship Program. :tada:
Sep 14, 2022 Our paper on Learning High Dimensional Networks Obeying Conservation Laws has been accepted at NeurIPS, 2022. :tada:

selected publications

  1. [J] NeurIPS
    Learning the Structure of Large Networked Systems Obeying Conservation Laws
    Anirudh Rayas, Rajasekhar Anguluri, and Gautam Dasarathy
    NeurIPS, 2022
    arXiv PDF Code
  2. [C] ICASSP
    Differential Analysis for Networks Obeying Conservation Laws
    Anirudh Rayas, Rajasekhar Anguluri, Jiajun Cheng, and Gautam Dasarathy
    ICASSP, 2023
    arXiv PDF
  3. [J] Frontiers
    Orthogonality and graph divergence losses promote disentanglement in generative models
    Ankita Shukla, Rishi Dadhich, Rajhans Singh, Anirudh Rayas, Pouria Saidi, and 3 more authors
    Frontiers in Computer Science, 2024
    PDF
  4. [P] Preprint
    Learning Networks from Wide-Sense Stationary Stochastic Processes
    Anirudh Rayas, Jiajun Cheng, Rajasekhar Anguluri, Deepjyoti Deka, and Gautam Dasarathy
    arXiv preprint arXiv:2412.03768, 2024
    arXiv Code
  5. [P] Preprint
    Structure Learning in Gaussian Graphical Models from Glauber Dynamics
    Vignesh Tirukkonda, Anirudh Rayas, and Gautam Dasarathy
    arXiv preprint arXiv:2412.18594, 2024
    arXiv