Shrikant Chand

Bio

Aug 2022 - Present	PhD Mathematics	Duke University
Sep 2020 - May 2022	MS Mathematics	NYU Courant Institute of Mathematical Sciences
Sep 2018 - May 2022	BS Mathematics	NYU Tandon School of Engineering

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Research

Interests

I am a 4th year PhD candidate in mathematics, coadvised by Hau-Tieng Wu (primarily at NYU Courant) and Jim Nolen. My academic interests are probability, computational/high-dimensional statistics, harmonic analysis, Fourier analysis, and numerical analysis with applications in machine learning and signal processing. Particular applications of interest include:

• Time-frequency and time-series analysis
• Manifold learning
• Randomized numerical linear algebra (hierarchical matrices and applications in kernel methods, e.g., Gaussian processes)
• Optimal transport

Publications and Preprints

Preprints

1. Z. Heile, P. He, J. Tran, R. Wang, S. Chand, Rigid-invariant sliced Wasserstein via independent embeddings, arXiv (2025).

Journal Articles

1. C. Allard, I. Ben-Ari, S. Chand, V. Hovenga, E. Lee, and J. Shapiro, Quasistationary distribution for the invasion model on a complete bipartite graph, Journal of Stochastic Analysis (2024), Vol. 5, No. 1, Art. 2, DOI 10.31390/josa.5.1.02.
2. S. Chand, N.-T. Chiu, Y.-H. Chou, A. A. Alian, K. H. Shelley, and H.-T. Wu, Comparison of feature-based indices derived from photoplethysmogram recorded from different body locations during lower body negative pressure, Physiological Measurement (2025), Vol. 46, No. 8, DOI 10.1088/1361-6579/adf489.
3. S. Chand, J. Nolen, and H.-T. Wu, On spectral interference of the short-time Fourier transform and its nonlinear variations, Applied and Computational Harmonic Analysis (2026), Vol. 85, Art. 101889, DOI 10.1016/j.acha.2026.101889.

Conference and Workshop Papers

1. Z. Heile, P. He, J. Tran, R. Wang, S. Chand, Scalable Rigid-invariant distance for shape matching and alignment, NeurIPS 2025 Workshop on Imageomics.
2. Z. Heile, P. He, J. Tran, R. Wang, S. Chand, Rigid Invariant Sliced Wasserstein via Independent Embeddings, ICLR 2026 Workshop on Geometry-grounded Representation Learning and Generative Modeling.
3. Z. Heile, P. He, J. Tran, R. Wang, S. Chand, Align Representations, Not Points: Efficient Rigid Invariant Transport Distance, ICLR 2026 Workshop on Representational Alignment.

In Preparation

• (with S. Poinelli) Time series changepoint detection via diffusion map embeddings.

Projects

• Fast Gaussian process regression via low-rank approximations

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Teaching

(GR) = Graduate course

Role	Course	Number	Term	Institute
TA	Algorithmic Trading (GR)	MATH 585	Fa 2025	Duke
TA	Vector Calculus	MATH 219	Sp 2025	Duke
TA	Vector Calculus	MATH 219	Fa 2024	Duke
TA	Probability	MATH 230	Fa 2023	Duke
TA	Laboratory Calculus I	MATH 111L	Fa 2022	Duke
TA	Applied Statistics	MA-UY 4114	Sp 2022	NYU
TA	Applied Partial Differential Equations	MA-UY 4414	Sp 2022	NYU
Grader	Machine Learning (GR)	ECE-GY 6143	Fa 2021	NYU
Grader	Multivariable Calculus	MA-UY 2114	Sp 2021	NYU
Grader	Multivariable Calculus	MA-UY 2114	Fa 2020	NYU