Nimita Shinde

Research Interests

My research focuses on the development of advanced optimization and computational modeling frameworks for large-scale decision problems arising in healthcare, medical physics, and machine learning. I am particularly interested in mixed-integer programming, nonconvex optimization, quantum-inspired optimization, and scalable algorithms for computationally intensive systems.

My current work centers on optimization methods for radiation therapy treatment planning, including IMPT, IMRT, proton minibeam therapy, and LATTICE radiotherapy. I develop mixed-integer and nonlinear optimization models for biologically informed and geometry-adaptive treatment planning, along with scalable solution methods based on convex relaxation, decomposition, and ADMM frameworks.

I am also interested in hybrid classical-quantum optimization approaches, including variational quantum computing and quantum-inspired methods for high-dimensional and nonconvex planning problems. In parallel, my research explores theoretical aspects of structured optimization, including memory-efficient algorithms and approximation methods for combinatorial optimization problems such as Max-Cut, Max-k-Cut, and correlation clustering. I have additionally worked on robust high-dimensional learning models for sparse subspace and manifold clustering under noise and missing data settings.

Publications

• Shinde, N., Lin, Y., and Gao, H., 2026. Collimator position optimization for proton minibeam radiation therapy. Nature-Scientific Reports (accepted for publication).

• 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. International Journal of Particle Therapy (accepted for publication).

• Shinde, N., Gu, W., Domal, S., Badiyan, S., Lin, Y., and Gao, H., 2026. Lattice peak optimization: A mixed-integer framework for geometry-adaptive lattice radiotherapy. Physics in Medicine and Biology (accepted for publication).

• Shinde, N., Zhang, W., Lin, Y. and Gao, H., 2026. Minibeam‐pLATTICE: A novel proton LATTICE modality using minibeams. Medical Physics, 53(1), p.e70250.

• 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. Medical physics, 52(11), p.e70129.

• Shinde, N., Li, W., Chen, R.C. and Gao, H., 2025. Optimizing normal tissue sparing via spatiotemporal optimization under equivalent tumor-radical efficacy. Physics in Medicine and Biology, 70(12).

• Shinde, N., Ding, T., Robinson, D. and Vidal, R., Geometric Analysis of Nonlinear Manifold Clustering. Advances in Neural Information Processing Systems, 37, pp.128769-128797.

• 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.

• 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.

Under review

• Wu, W., Shinde, N., Li, J.,  Vanhaezebrouck, I., Jiang, K., Lin, Y., Li, Q., and Gao, H., 2026. Photon minibeam-LATTICE radiotherapy: A novel spatially fractionated technique for treating small and medium-sized tumors. Under review at Medical Physics.

• Tong, X., Zhu, Y., Shinde, N., Li, W., Zhang, W., Wang, C., Lin, Y., and Gao, H., 2025. A single-field-each-peak optimization method for motion-robust proton LATTICE therapy. Under review at Medical Physics.

• Zhu, Y., Shinde, N., Lin, B., and 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. Under review at Medical Physics.

• Shinde, N., Zhu, Y., Shen, H., and Gao, H., 2025. A quantum computing approach to beam angle optimization. arXiv preprint arXiv:2504.07844. Under review at Medical Physics.

• 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. 

Working papers

• Shinde, N., and Gao, H., 2026. Quantum-inspired tensor-network optimization for sparse QUBO problems.

• Shinde, N., and Gao, H., 2026. A scalable quantum-inspired optimization framework for radiotherapy planning problems.

• Shinde, N., Abuduxiku, M., and Gao, H., 2026. A slice-separable peak placement optimization framework for mini-lattice radiotherapy.

• Abuduxiku, M., Shinde, N., and Gao, H., 2026. Hybrid IMRT and mini-Lattice delivery treatment planning for LATTICE radiotherapy.

Posters, Talks, and Workshops

• Talk) Collimator position optimization for proton minibeam radiation therapy, 2026 Joint AAPM | COMP Annual Meeting & Exhibition, July 2026

• (Talk) Collimator position optimization for proton minibeam radiation therapy, 64th Annual Particle Therapy Co-Operative Group Conference, June 2026

• (Talk) Collimator position optimization for proton minibeam radiation therapy, 2026 Southwest Regional Chapter of American Association of Physicists in Medicine, January 2026

• (Talk) Minibeam-pLATTICE: A novel proton LATTICE modality using minibeams, AAPM 67th Annual Meeting and Exhibition, July 2025.

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

• (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.

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