Bio

I was born and raised in India. I spent most of this time in Bangalore. Before joining Stanford, I obtained a Master’s from the Electrical and Computer Engineering Department of Purdue University. At Purdue, I was advised by Prof. David Gleich.

My brother Siddarth Vasudevan is a PhD student at ETH Zürich.

Honors & Awards

  • ACM Graduate TA Award, Department of Computer Science, Purdue University (2015)

Professional Affiliations and Activities

  • Member, SIAM (2015 - Present)
  • Member, IEEE (2010 - Present)

Education & Certifications

  • MSc, ECE Department, Purdue University (2016)

Service, Volunteer and Community Work

  • Weeks Of Welcome (WOW) volunteer, Purdue University

    Stuff and hand welcome bags during International Students & Scholars (ISS) orientation; Gatekeeper.

    Location

    International Students and Scholars (ISS) - Purdue University, West Lafayette, Indiana, 47906

Personal Interests

I enjoy reading. You can find me on Goodreads.

Contact

  • devan@stanford.edu
    University - Student Department: Institute for Computational and Mathematical Engineering (ICME) Position: Graduate

Additional Info

  • Mail Code: 4042

Links

Current Research and Scholarly Interests

Master's Thesis

Title: Best rank-1 approximations without orthogonal invariance for the 1-norm.

Abstract: Data measured in the real-world is often composed of both a true signal, such as an image or experimental response, and a perturbation, such as noise or weak secondary effects. Low-rank matrix approximation is one commonly used technique to extract the true signal from the data. Given a matrix representation of the data, this method seeks the nearest low-rank matrix where the distance is measured using a matrix norm.

The classic Eckart-Young-Mirsky theorem tells us how to use the Singular Value Decomposition (SVD) to compute a best low-rank approximation of a matrix for any orthogonally invariant norm. This leaves as an open question how to compute a best low-rank approximation for norms that are not orthogonally invariant, like the 1-norm.

In this thesis, we present how to calculate the best rank-1 approximations for 2-by-n and n-by-2 matrices in the 1-norm. We consider both the operator induced 1-norm (maximum column 1-norm) and the Frobenius 1-norm (sum of absolute values over the matrix). We present some thoughts on how to extend the arguments to larger matrices.

Work Experience

  • Graduate Teaching Assistant, Department of Computer Science, Purdue University (8/1/2013 - 5/1/2016)

    Location

    West Lafayette, IN 47907, United States