Ashish Katiyar

Ashish Katiyar

PhD. Student DICE Track ECE Department University of Texas at Austin

Posts by Collection

portfolio

projects

A Bayesian approach to determine the composition of heterogeneous cancer tissue

We tackle the challenge of determining the composition of a heterogeneous cancer tissue. There exist high cost methods providing cell level resolution to observe important feature values. This method can be used to get accurate estimates of hetergeneous cell composition but the cost is prohibitive. We propose an algorithm that can get accurate estimates of the heterogeneous cancer tissue composition in a cost effective manner. It relies on one-time high cost cell level resolution measurements which can then be utilized in low cost low resolution aggregate measurements to estimate the composition of the heterogeneous cancer tissue.

Robust Estimation of Tree Structured Gaussian Graphical Models

Consider jointly Gaussian random variables whose conditional independence structure is specified by a graphical model. If we observe realizations of the variables, we can compute the covariance matrix, and it is well known that the support of the inverse covariance matrix corresponds to the edges of the graphical model. Instead, suppose we only have noisy observations. If the noise is independent, this allows us to compute the sum of the covariance matrix and an unknown diagonal. The inverse of this sum is (in general) dense. We ask: can the original independence structure be recovered? We address this question for tree structured graphical model. We prove the unidentifiability of this problem and provide the additional constraints to make this problem identifiable. We also provide an $\mathcal{O}(n^3)$ algorithm to find the underlying tree structure.

publications

Bayesian data and channel joint maximum-likelihood based error correction in wireless sensor networks

Published in Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief, 2011

The proposed algorithm employs the temporal correlation of the narrowband sensor data in conjunction with the channel state information (CSI) for detection and error correction of the data received over the Rayleigh fading wireless channel.

Recommended citation: Ashish Katiyar, and Aditya K. Jagannatham (2011) "Bayesian data and channel joint maximum-likelihood based error correction in wireless sensor networks" Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief pp 141-146 http://www.iitk.ac.in/mwn/papers/acwr2011_submission_ashish.pdf

A Bayesian approach to determine the composition of heterogeneous cancer tissue

Published in BMC bioinformatics, 2018

In this paper, we propose an algorithm to tackle the challenge of determining the composition of a heterogeneous cancer tissue by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework.

Recommended citation: Ashish Katiyar, Anwoy Mohanty, Jianping Hua, Sima Chao, Rosana Lopes, Aniruddha Datta and Michael L. Bittner(2018). "A Bayesian approach to determine the composition of heterogeneous cancer tissue" BMC Bioinformatics. 2018;19(Suppl 3)1 https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2062-0

Robust Estimation of Tree Structured Gaussian Graphical Models

Published in International Conference on Machine Learning, 2019

In this paper, we look at the problem of learning Gaussian Graphical Models when the samples have independent additive Gaussian noise.

Recommended citation: Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis, "Robust Estimation of Tree Structured Gaussian Graphical Models" Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3292-3300, 2019 http://proceedings.mlr.press/v97/katiyar19a/katiyar19a.pdf

Robust Estimation of Tree Structured Ising Models

Pre-print

We consider the task of learning tree structured Ising models when the signs of different random variables are flipped independently with possibly unequal, unknown probabilities.

Recommended citation: Ashish Katiyar, Vatsal Shah, and Constantine Caramanis. "Robust Estimation of Tree Structured Ising Models." arXiv preprint arXiv:2006.05601 (2020). https://arxiv.org/pdf/2006.05601.pdf

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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