Nimita Shinde

Research Interests

My interests lie in the practical applications of optimization models and solution methodologies. Currently, I am working on various optimization problems that arise in radiation therapy for cancer treatment specifically on radiation dose planning.

I am also 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 have also worked on manifold clustering problems, with a focus on proving theoretical bounds on the clustering accuracy of existing methods.

Publications and Preprints

• Zhu, Y., Shinde, N., Lin, B., Gao, H., 2025. An energy optimization method based on mixed-integer model and variational quantum computing algorithm for faster IMPT. arXiv preprint arXiv:2504.10315. [pdf]

• Shinde, N., Zhu, Y., Shen, H., Gao, H., 2025. A quantum computing approach to beam angle optimization. arXiv preprint arXiv:2504.07844. [pdf]

• Shinde, N., Li, W., Chen, R.C. and Gao, H., 2025. Optimizing normal tissue sparing via spatiotemporal optimization under equivalent tumor-radical efficacy. arXiv preprint arXiv:2502.16333. [pdf]

• Shinde, N., Zhang, W., Lin, Y. and Gao, H., 2025. Minibeam-pLATTICE: A novel proton LATTICE modality using minibeams. arXiv preprint arXiv:2502.16332. [pdf]

• Shinde, N., Zhang, W., Lin, Y. and Gao, H., 2025. A mixed integer programming approach to minibeam aperture optimization for multi-collimator proton minibeam radiotherapy. arXiv preprint arXiv:2502.16326. [pdf]

• Shinde, N., Zhu, Y., Wang, W., Li, W., Lin, Y., Gan, G.N., Lominska, C., Rotondo, R., Chen, R.C. and Gao, H., 2024. Multi-IMPT: a biologically equivalent approach to proton ARC therapy. arXiv preprint arXiv:2411.17578. [pdf]

• Shinde, N., Ding, T., Robinson, D. and Vidal, R., Geometric Analysis of Nonlinear Manifold Clustering. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. [pdf]

• Shinde, N., Narayanan, V. and Saunderson, J., 2023. An Inexact Frank-Wolfe Algorithm for Composite Convex Optimization Involving a Self-Concordant Function. arXiv preprint arXiv:2310.14482. [pdf]

• Shinde, N., Narayanan, V. and Saunderson, J., 2021. Memory-efficient approximation algorithms for max-k-cut and correlation clustering. Advances in Neural Information Processing Systems, 34, pp.8269-8281. [pdf]

• Shinde, N., Narayanan, V. and Saunderson, J., 2021. Memory-efficient structured convex optimization via extreme point sampling. SIAM Journal on Mathematics of Data Science, 3(3), pp.787-814. [pdf]

Posters, Talks, and Workshops

1. (Poster) Geometric Analysis of Nonlinear Manifold Clustering, Institute for Data, Intelligent Systems, and Computation, April 2024.

2. (Talk) Inexact Frank-Wolfe Algorithm for Optimizing Self-Concordant Barrier Functions, Department Seminar, Department of Management Sciences, IIT Kanpur, February 2024.

3. (Talk) Memory-Efficient Approximation Algorithm for Max-Cut, Max-K-Cut and Correlation Clustering, MOPTA, August 2023

4. (Talk) Memory-Efficient Approximation Algorithm for Max-Cut, Max-K-Cut and Correlation Clustering, SIAM Conference on Optimization, June 2023.

5. (Talk) Memory-efficient approximation algorithms for Max-Cut and Max-k-Cut, Semester Program on Discrete Optimization: Mathematics, Algorithms, and Computation, ICERM, 2023.

6. (Talk) Memory-efficient approximation techniques, Semester Program on Harmonic Analysis and Convexity, ICERM 2022.

7. (3-Minute Thesis Talk) ‘Memory-efficient algorithms for structured convex optimization problems.’, Monash University, 2021.

8. (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.

9. (Poster) Nimita Shinde, Vishnu Narayanan, and James Saunderson. Memory-efficient approximation algorithms for Max-Cut and Max-k-Cut, IPCO, 2022.

10. (Workshop) Optimal Transport in Data Science, ICERM, May 8 - 12, 2023.

11. (Workshop) Trends in Computational Discrete Optimization, ICERM, Apr 24 - 28, 2023.

12. (Workshop) Combinatorics and Optimization, ICERM, Mar 27 - 31, 2023.

13. (Workshop) Linear and Non-Linear Mixed Integer Optimization, ICERM, Feb 27 - Mar 3, 2023.

14. (Workshop) Current Themes of Discrete Optimization: Boot-camp for early-career researchers, ICERM, Jan 30 - Feb 3, 2023.

Teaching

1. ISE 402: Applied Models in Operations Research, Spring 2024 at Lehigh University (Department of Industrial and Systems Engineering)

Awards

1. Recipient of IEOR Alumnus Endowment: Excellence in Doctoral Dissertation Award for 2021-23 at IIT Bombay

2. Institute Silver Medal (M. Tech., 2016), IIT Bombay