Akshay Ramachandran Office: ICCS X453 Email: aramach@cs.ubc.ca Links to: CV (as of June 2023); DBLP;

About Me

I am an Assistant Professor at the University of British Columbia in the Computer Science as of July 2025. Before this I was a Postdoctoral Researcher at ENS Lyon hosted by Professor Omar Fawzi in the QINFO group. Before that I was a Postdoctoral Researcher at Centrum Wiskunde & Informatica (CWI) and University of Amsterdam (UvA) where I was hosted by Professor Daniel Dadush in the Networks and Optimization (N&O) group as well as Professor Michael Walter who has moved to Ruhr Universitat Bochum. I'm interested in generalizations of convex analysis and convex optimization algorithms to non-Euclidean spaces with a special focus on applications to numerical linear algebra, quantum information theory, geometric invariant theory, and high dimensional statistics.

I completed my Ph.D. at the University of Waterloo's Cheriton School of Computer Science. I was very fortunate to be advised by Lap Chi Lau. Before UWaterloo, I spent four wonderful years at UC Berkeley, graduating in May 2016 with a degree in Electrical Engineering and Computer Science. At UC Berkeley, I was incredibly lucky to have the opportunity to work with Luca Trevisan and Nikhil Srivastava.

Research

Publications and Preprints (Show all)

The Paulsen Problem Revisited: Optimal Bounds via Smoothed Analysis and Scaling.
Lap Chi Lau, Akshay Ramachandran.
In Preparation.


Optimal Bounds for Tyler's M-Estimator for Elliptical Distributions.
Akshay Ramachandran, Lap Chi Lau.
In Submission.


Computational aspects of the trace norm contraction coefficient.
Idris Delsol, Omar Fawzi, Jan Kochanowski, Akshay Ramachandran.
Accepted to QIP 2026.


Strongly Polynomial Frame Scaling to High Precision.
Daniel Dadush, Akshay Ramachandran.
Manuscript.
• Presented at SODA 2024


Near optimal sample complexity for dense matrix and tensor normal models.
Cole Franks, Rafael Oliveira, Akshay Ramachandran, Michael Walter.
Manuscript.
• Accepted for Annals of Statistics.


Hidden convexity, optimization, and algorithms on rotation matrices .
Kevin Shu, Akshay Ramachandran, Alex Wang.
Manuscript.
• Published in Mathematics of Operations Research.


Scaling problems, algorithms and applications to computer science, functional analysis and statistics.
Rafael Oliveira, Akshay Ramachandran.
Survey
• In 33o Coloquio Brasileiro de Matematica


Spectral Analysis of Operator Scaling.
Tsz Chiu Kwok, Lap Chi Lau, Akshay Ramachandran.
Manuscript.
• Presented at FOCS 2019.
• In SICOMP Volume 50 (2021).


The Paulsen Problem, Continuous Operator Scaling, and Smoothed Analysis.
Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Akshay Ramachandran.
Manuscript.
• STOC 2018.


Sandpile Prediction on Trees in near linear time.
Akshay Ramachandran, Aaron Schild.
Manuscript.
• SODA 2017.



Thesis

• Geodesic Convex Analysis of Group Scaling for the Paulsen Problem and the Tensor Normal Model .
  Akshay Ramachandran.
  Ph.D. Thesis, 2021.
  • Won Mathematics Doctoral Prize (second place) in Faculty of Mathematics
  • Won Cheriton Distinguished Dissertation Award in Department of Computer Science

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Talks

• Eigenvalue Inequalities and the Subspaces that Induce them. Presented in AAC Seminar, June 2023 (Bochum, Germany).
• Operator scaling meets non-commutative optimal transport. In AIM SQuaREs Workshop, Mar 2023 (San Jose, CA, USA).
• Jordan’s Lemma and the CS Decomposition in Quantum Algorithms. Presented in QuSoft Junior Meeting, March 2023 (Amsterdam, Netherlands).
• The Brascamp-Lieb Polytope: Rank of Matrix Spaces and Combinatorial Optimization. Presented in UWaterloo A&C Seminar, Oct 2022 (Waterloo, Canada).
• Near optimal sample complexity for matrix and tensor normal models. Presented in Simons Workshop on Symmetry in Optimization, Nov 2021 (UC Berkeley, CA, USA).
• Advanced Course in Scaling problems, algorithms, and applications. Presented with Rafael Oliveira in Coloquio Brasileiro de Matematica, June 2021 (Online)
• Spectral analysis of matrix and operator scaling. Presented in FOCS 2019 (Baltimore, MD, USA).
• The Paulsen Problem, Continuous Operator Scaling, and Smoothed Analysis. Presented in FOCS 2018 Workshop on Scaling Algorithms and Applications (Paris, France).
• Ramanujan graphs from the matching polynomial. Presented in UC Berkeley Topics Course on Geometry of Polynomials, Fall 2015 (Berkeley, CA).

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Teaching

• CPSC 536C: Algorithms for Convex Optimization
• Instructional Assistant for Continuous Optimization at Mastermath Netherlands Fall 2022.
• Instructional Assistant for CS341 (Algorithms) at UWaterloo Spring 2019, Fall 2019, Winter 2020.
• Teaching Assistant for CS70 (Discrete Math and Probability) at UC Berkeley Summer 2015, Fall 2015, Spring 2016.