Nimita Shinde
Bio
I am a Postdoctoral Research Associate in the Department of Industrial and Systems Engineering at Lehigh University. I am currently working with Prof. Daniel Robinson and Prof. René Vidal. I was a Postdoctoral Fellow at ICERM, Brown University from September 2022-May 2023. I completed my Ph.D. in 2022 jointly from Indian Institute of Technology Bombay and Monash University under the supervision of Prof. Vishnu Narayanan and Dr. James Saunderson. My thesis was titled 'Memory-efficient structured convex optimization'. Here is my resume.
I am currently working on subspace clustering in low dimension. I have also recently worked on maximum entropy sampling problems and algorithmic developments for composite convex optimization problems involving self-concordant functions.
Email: nimitashinde25@gmail.com
Research Interests
I am broadly interested in approximate algorithms for large-scale convex optimization, and integer and combinatorial optimization problems proving the tightness of semidefinite relaxations. I have also worked on developing approximate first-order algorithms for composite self-concordant optimization problems and on developing convex relaxation of Maximum Entropy Sampling Problem (MESP). I am currently working on the problem of subspace clustering. The low- and high-dimensional subspace clustering require different approaches. My work focuses on developing methodologies that have improved theoretical convergence guarantees for clustering in low- and high-dimensional setting.
Publications and Preprints
Shinde, Nimita, Vishnu Narayanan, and James Saunderson. "Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering." Advances in Neural Information Processing Systems 34 (2021): 8269-8281. [pdf]
Shinde, Nimita, Vishnu Narayanan, and James Saunderson. "Memory-efficient structured convex optimization via extreme point sampling." SIAM Journal on Mathematics of Data Science 3.3 (2021): 787-814. [pdf]
Nimita Shinde, Vishnu Narayanan, and James Saunderson. "An Inexact Frank-Wolfe Algorithm for Composite Convex Optimization Involving a Self-Concordant Function." arXiv preprint arXiv:2310.14482, 2023 [pdf]
Posters, Talks, and Workshops
(Talk) Inexact Frank-Wolfe Algorithm for Optimizing Self-Concordant Barrier Functions, Department Seminar, Department of Management Sciences, IIT Kanpur, February 2024.
(Talk) Memory-Efficient Approximation Algorithm for Max-Cut, Max-K-Cut and Correlation Clustering, MOPTA, August 2023
(Talk) Memory-Efficient Approximation Algorithm for Max-Cut, Max-K-Cut and Correlation Clustering, SIAM Conference on Optimization, June 2023.
(Talk) Memory-efficient approximation algorithms for Max-Cut and Max-k-Cut, Semester Program on Discrete Optimization: Mathematics, Algorithms, and Computation, ICERM, 2023.
(Talk) Memory-efficient approximation techniques, Semester Program on Harmonic Analysis and Convexity, ICERM 2022.
(3-Minute Thesis Talk) ‘Memory-efficient algorithms for structured convex optimization problems.’, Monash University, 2021.
(Poster) Nimita Shinde, Vishnu Narayanan, and James Saunderson. Memory-efficient approximation algorithms for Max-Cut and Max-k-Cut, Current Themes of Discrete Optimization: Boot-camp for early-career researchers, ICERM, 2023.
(Poster) Nimita Shinde, Vishnu Narayanan, and James Saunderson. Memory-efficient approximation algorithms for Max-Cut and Max-k-Cut, IPCO, 2022.
(Workshop) Optimal Transport in Data Science, ICERM, May 8 - 12, 2023.
(Workshop) Trends in Computational Discrete Optimization, ICERM, Apr 24 - 28, 2023.
(Workshop) Combinatorics and Optimization, ICERM, Mar 27 - 31, 2023.
(Workshop) Linear and Non-Linear Mixed Integer Optimization, ICERM, Feb 27 - Mar 3, 2023.
(Workshop) Current Themes of Discrete Optimization: Boot-camp for early-career researchers, ICERM, Jan 30 - Feb 3, 2023.
Awards
Recipient of IEOR Alumnus Endowment: Excellence in Doctoral Dissertation Award for 2021-23 at IIT Bombay
Institute Silver Medal (M. Tech., 2016), IIT Bombay