Deeksha Adil

<first_name>@cs.toronto.edu

University of Toronto

Biography

I am a Ph.D. student in the Theory Group at the University of Toronto, where I am supervised by Prof. Sushant Sachdeva. My current research interests include the Design of Fast Algorithms, particularly for optimization problems.

Prior to this, I completed my BS-MS in mathematics from the Indian Institute of Science Education and Research, Pune in 2017. For my MS thesis I visited the Institute of Mathematical Sciences, Chennai and the University of Bergen, Norway where I was supervised by Prof. Saket Saurabh. We were working on the parameterized complexity of matching problems.


BS-MS, Mathematics Indian Institute of Science Education and Research, Pune 2012 - 2017	Research Intern International Centre for Theoretical Sciences-TIFR, Bengaluru Summer 2014 and Summer 2015	Master's Thesis Project Institute for Mathematical Sciences, Chennai May 2016 - March 2017	Visiting Student University of Bergen, Norway Sep 2016 - Nov 2016	Visiting Student Institute for Advanced Study, Princeton Fall 2019

Publications

Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization

Deeksha Adil, Brian Bullins, Sushant Sachdeva

Under Review

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Almost-linear-time Weighted ℓp-norm Solvers in Slightly Dense Graphs via Sparsification

Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva

ICALP 2021

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Faster p-norm minimizing flows, via smoothed q-norm problems

Deeksha Adil, Sushant Sachdeva

SODA 2020

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Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

Deeksha Adil, Richard Peng, Sushant Sachdeva

NeurIPS 2019

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@article{AdilPS19,author = {Deeksha Adil and Richard Peng and Sushant Sachdeva},
title     = {Fast, Provably convergent {IRLS} Algorithm for p-norm Linear Regression},
journal   = {CoRR},
volume    = {abs/1907.07167},
year= {2019},
archivePrefix = {arXiv},
eprint    = {1907.07167},
timestamp = {Tue, 23 Jul 2019 10:54:22 +0200},
biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1907-07167},
bibsource = {dblp computer science bibliography, https://dblp.org} }

Iterative Refinement for lp-norm Regression

Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva

SODA 2019

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@inproceedings{adil2019iterative,title={Iterative Refinement for lp-norm Regression},
author={Adil, Deeksha and Kyng, Rasmus and Peng, Richard and Sachdeva, Sushant},
booktitle={Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms},
pages={1405–1424},
year={2019},
organization={SIAM}}

Parameterized algorithms for stable matching with ties and incomplete lists

Deeksha Adil, Sushmita Gupta, Sanjukta Roy, Saket Saurabh, Meirav Zehavi

Theoretical Computer Science

Details BibTeX

@article{AdilGRSZ18, author    = {Deeksha Adil and Sushmita Gupta and Sanjukta Roy and Saket Saurabh and Meirav Zehavi},
title     = {Parameterized algorithms for stable matching with ties and incomplete lists},
journal   = {Theor. Comput. Sci.},
volume= {723},
pages     = {1–10},
year      = {2018},
url       = {https://doi.org/10.1016/j.tcs.2018.03.015},
doi       = {10.1016/j.tcs.2018.03.015},
timestamp = {Wed, 04 Apr 2018 15:21:59 +0200}}

Matching Under Preferences

Deeksha Adil

MS Thesis IISER Pune

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Teaching Experience

University of Toronto (Teaching Assistant)

CSC373 - Algorithm Design, Analysis and Complexity (Winter 2018, Summer 2018, Winter 2019)
CSC304 - Algorithmic Game Theory and Mechanism Design (Fall 2018)
CSC236 - Theory of Computation (Summer 2019)
CSC336 - Numerical Analysis (Winter 2020, Summer 2020, Fall 2020)
CSC2421 - Topics in Algorithms, Graduate Course (Winter 2021)