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Sitemap
A list of all the posts and pages found on the site. For you robots out there is an [XML version](https://sourabhpalande.github.io/sitemap.xml) available for digesting as well.Pages
Posts
cvitem
MSc in Applied Mathematics
Published:
- Dissertation: “Analysis of the Source Trajectory in Cone Beam Micro CT.” Advisor: Prof. Bill Lionheart.
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Dissertation Award
Published:
- Awarded GBP 3000 in funding to carry out dissertation research with industrial collaborators.
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PhD in Computing
Published:
- Thesis: “Utilizing Topological Structures of Data for Machine Learning.” Advisor: Dr. Bei Wang-Phillips.
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Graduate Research Assistant
Published:
- Collaborated with neuroscientists, applying advanced data science techniques in autism research.
- Developed and implemented novel machine learning and data analysis methods for brain networks.
- Developed and implemented spectral algorithms for simplicial complexes and hypergraphs.
- Helped design a visualization tool for DNN interpretability: TopoAct
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Foundations of Data Science
Published:
- Invited to participate as a visiting graduate researcher in the Fall semester program on mathematical foundations of data science.
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Postdoctoral Research Associate
Published:
- Led interdisciplinary collaborative projects consisting of mathematicians, computer scientists and biologists.
- Developed image analysis techniques for 3D X-Ray scans and 2D RGB images for applications in plant biology.
- Developed exploratory visual analytics tools to study gene expression data across plant evolution.
- Guided undergrad and grad student research projects in computational biology.
- Helped design and publish a novel interactive book introducing python programming to biology students: Plants & Python
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Models and Methods for (Hyper) Network Science
Published:
- Invited to participate in Mathematical Research Communities (MRC)
- Established continued research collaborations to identify and solve open problems in hyper network science.
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Data Scientist II
Published:
publications
Kernel partial least squares regression for relating functional brain network topology to clinical measures of behavior
Published in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
We use topological features to analyze the relationship between functional brain networks and behavioral phenotypes.
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Topological data analysis of functional MRI connectivity in time and space domains
Published in Connectomics in NeuroImaging: Second International Workshop, CNI 2018, 2018
We use TDA to compare fMRI preprocessing strategies, and to analyze spatial vs temporal functional connectivity.
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Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference
Published in Brain Connectivity, 2019
We apply TDA in conjunction with scMRI to study structural and functional brain network abnormalities in ASD.
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Autism classification using topological features and deep learning: A cautionary tale
Published in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019
We present a cautionary tale about the limited discriminative power of topological features derived from fMRI data in ASD classification.
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Spectral sparsification of simplicial complexes for clustering and label propagation
Published in Journal of Computational Geometry (JoCG), 2020
We show that the theory of Spielman and Srivastava for the sparsification of graphs extends to simplicial complexes via the up Laplacian. We also introduce higher-order generalizations of spectral clustering and label propagation for simplicial complexes
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Utilizing Topological Structures of Data for Machine Learning
Published in The University of Utah ProQuest Dissertations Publishing, 2020
We describe ways to integrate ideas from TDA into different stages of a machine learning pipeline. First we present unsupervised and semisupervised learning algorithms that leverage the topological structure of the data. Then, we describe ways to extract topological features from data and ways to utilize them in classical machine learning models. Lastly, we present methods to compare complex objects such as graphs and their ensembles.
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Sketching merge trees for scientific data visualization
Published in arXiv preprint [cs.CG], 2021
We develop a framework for sketching a set of merge trees that combines the Gromov-Wasserstein probabilistic matching with techniques from matrix sketching.
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TopoAct: Visually exploring the shape of activations in deep learning
Published in Computer Graphics Forum, 2021
Applying tools from TDA, we present TopoAct, a visual exploration system to study topological summaries of activation vectors.
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Plants & Python: A series of lessons in coding, plant biology, computation, and bioinformatics
Published in The Plant Cell, 2022
An introductory python programming course for plant biologists published as a JupyterBook.
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The topological shape of gene expression across the evolution of flowering plants
Published in bioRxiv preprint [Plant Biology], 2022
We apply mapper to a novel data set of gene expression from diverse species spanning the evolution of flowering plants.
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Uncertainty Visualization for Graph Coarsening
Published in 2022 IEEE International Conference on Big Data (Big Data)., 2022
We present a framework to quantify and visualize the uncertainty associated with randomized graph reduction techniques.
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research
talks
Kernel partial least squares regression for relating functional brain network topology to clinical measures of behavior
Published:
Using topological features to analyze the relationship between functional brain networks and behavioral phenotypes.
Read more
Autism classification using topological features and deep learning: A cautionary tale
Published:
We present a cautionary tale about the discriminative power of topological features derived from fMRI data in ASD classification.
Read more
TDA + ML: Utilizing Topological Structures of Data for Machine Learning
Published:
Describing ways to integrate TDA into different stages of a machine learning pipeline.
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The topological shape of gene expression across the evolution of flowering plants
Published:
Applying mapper to a novel data set of gene expression from diverse species spanning the evolution of flowering plants.
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Leveraging Topological Structure in Data Analysis, Machine Learning, and Visualization
Published:
Presenting various methods to leverage topological structure in data analysis, machine learning, and visualization.
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teaching
Advanced Scientific Computing
, School of Computing, University of Utah, 2016
This was a graduate level course intended to give students exposure to the algorithms and implementations often used in scientific computing. In this course, we touched upon topics such as: computational linear algebra, eigenvalues and singular values, nonlinear systems and optimization, interpolation and approximation, numerical integration and differentiation.
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Computational Topology
, School of Computing, University of Utah, 2017
This was a graduate level course intended to familiarize students with Topological Data Analysis (TDA). In this course we covered basic comcepts, data structures and algorithms commonly used in TDA, Including homology, persistent homology, and mapper. Students learned about the various tools and their applications in machine learning, statistical analysis, and visualization.
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Plants and Python
, Michigan State University, 2020
This is a graduate level course that introduces students to data analysis, algorithmic thinking, model building, bioinformatics, and molecular biology using coding and computational resources. It selects specific examples in which mathematical, modeling, and bioinformatic approaches intersect with the biology of plants. Students apply learned objectives to a course research project.
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