All Outputs (28)

A diffusion approach to Stein's method on Riemannian manifolds (2024)
Journal Article

We detail an approach to developing Stein’s method for bounding integral metrics on probability measures defined on a Riemannian manifold M. Our approach exploits the relationship between the generator of a diffusion on M having a target invariant me... Read More about A diffusion approach to Stein's method on Riemannian manifolds.

Tumor radiogenomics in gliomas with Bayesian layered variable selection (2023)
Journal Article

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor... Read More about Tumor radiogenomics in gliomas with Bayesian layered variable selection.

Probabilistic Learning of Treatment Trees in Cancer (2023)
Journal Article

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems, such as patient-derived xeno... Read More about Probabilistic Learning of Treatment Trees in Cancer.

Spatially penalized registration of multivariate functional data (2023)
Journal Article

Registration of multivariate functional data involves handling of both cross-component and cross-observation phase variations. Allowing for the two phase variations to be modelled as general diffeomorphic time warpings, in this work we focus on the h... Read More about Spatially penalized registration of multivariate functional data.

Shape and Structure Preserving Differential Privacy (2022)
Presentation / Conference

It is common for data structures such as images and shapes of 2D objects to be represented as points on a manifold. The utility of a mechanism to produce sanitized differentially private estimates from such data is intimately linked to how compatible... Read More about Shape and Structure Preserving Differential Privacy.

Variograms for kriging and clustering of spatial functional data with phase variation (2022)
Journal Article

Spatial, amplitude and phase variations in spatial functional data are confounded. Conclusions from the popular functional trace-variogram, which quantifies spatial variation, can be misleading when analyzing misaligned functional data with phase var... Read More about Variograms for kriging and clustering of spatial functional data with phase variation.

RADIOHEAD: Radiogenomic analysis incorporating tumor heterogeneity in imaging through densities (2021)
Journal Article

Recent technological advancements have enabled detailed investigation of associations between the molecular architecture and tumor heterogeneity through multisource integration of radiological imaging and genomic (radiogenomic) data. In this paper we... Read More about RADIOHEAD: Radiogenomic analysis incorporating tumor heterogeneity in imaging through densities.

Differential privacy over Riemannian manifolds (2021)
Conference Proceeding

In this work we consider the problem of releasing a differentially private statistical summary that resides on a Riemannian manifold. We present an extension of the Laplace or K-norm mechanism that utilizes intrinsic distances and volumes on the mani... Read More about Differential privacy over Riemannian manifolds.

Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data (2021)
Journal Article

It is quite common for functional data arising from imaging data to assume values in infinite-dimensional manifolds. Uncovering associations between two or more such nonlinear functional data extracted from the same object across medical imaging moda... Read More about Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data.

Shape-Based Classification of Partially Observed Curves, With Applications to Anthropology (2021)
Journal Article

We consider the problem of classifying curves when they are observed only partially on their parameter domains. We propose computational methods for (i) completion of partially observed curves; (ii) assessment of completion variability through a nonp... Read More about Shape-Based Classification of Partially Observed Curves, With Applications to Anthropology.

Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse, and Fragmented Functional Data (2021)
Journal Article

In many applications, smooth processes generate data that are recorded under a variety of observational regimes, including dense sampling and sparse or fragmented observations that are often contaminated with error. The statistical goal of registerin... Read More about Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse, and Fragmented Functional Data.

Invariance and identifiability issues for word embeddings (2019)
Conference Proceeding

Word embeddings are commonly obtained as optimisers of a criterion function f of 1 a text corpus, but assessed on word-task performance using a different evaluation 2 function g of the test data. We contend that a possible source of disparity in 3 pe... Read More about Invariance and identifiability issues for word embeddings.

Mutually disjoint, maximally commuting set of physical observables for optimum state determination (2019)
Journal Article

We consider the state determination problem using mutually unbiased bases (MUBs). For spin-1, spin-3/2 and spin-2 systems, analogous to Pauli operators of spin-1/2 system, which are experimentally implementable and correspond to the optimum measureme... Read More about Mutually disjoint, maximally commuting set of physical observables for optimum state determination.

Distribution on warp maps for alignment of open and closed curves (2019)
Journal Article

Alignment of curve data is an integral part of their statistical analysis, and can be achieved using model-or optimization-based approaches. The parameter space is usually the set of monotone, continuous warp maps of a domain. Infinite-dimensional na... Read More about Distribution on warp maps for alignment of open and closed curves.

A geometric variational approach to Bayesian inference (2019)
Journal Article

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher–Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold c... Read More about A geometric variational approach to Bayesian inference.

Radiologic image-based statistical shape analysis of brain tumors (2018)
Journal Article

We propose a curve-based Riemannian-geometric approach for general shape-based statistical analyses of tumors obtained from radiologic images. A key component of the framework is a suitable metric that (1) enables comparisons of tumor shapes, (2) pro... Read More about Radiologic image-based statistical shape analysis of brain tumors.

POVM construction: a simple recipe with applications to symmetric states (2017)
Journal Article

We propose a simple method for constructing POVMs using any set of matrices which form an orthonormal basis for the space of complex matrices. Considering the orthonormal set of irreducible spherical tensors, we examine the properties of the construc... Read More about POVM construction: a simple recipe with applications to symmetric states.

Geometric multiaxial representation of N-qubit mixed symmetric separable states (2017)
Journal Article

Study of an N qubit mixed symmetric separable states is a long standing challenging problem as there exist no unique separability criterion. In this regard, we take up the N-qubit mixed symmetric separable states for a detailed study as these states... Read More about Geometric multiaxial representation of N-qubit mixed symmetric separable states.

Statistical tests for large tree-structured data (2017)
Journal Article

We develop a general statistical framework for the analysis and inference of large tree-structured data, with a focus on developing asymptotic goodness-of-fit tests. We first propose a consistent statistical model for binary trees, from which we deve... Read More about Statistical tests for large tree-structured data.

Lowering the cost of large-scale energy storage: high temperature adiabatic compressed air energy storage (2017)
Journal Article

Compressed air energy storage is an energy storage technology with strong potential to play a significant role in balancing energy on transmission networks, owing to its use of mature technologies and low cost per unit of storage capacity. Adiabatic... Read More about Lowering the cost of large-scale energy storage: high temperature adiabatic compressed air energy storage.