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. The main goal of my research is the design of fast algorithms with provable guarantees for problems in optimization, machine learning, and theoretical computer science. My work leverages tools from optimization theory and continuous analysis.

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.

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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

NeurIPS 2021

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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)