Curriculum Vitae

Summary

Work Experience

Data Scientist II

Topp Roots Lab, Donald Danforth Plant Science Center,   Saint Louis, Missouri,   August 2023 - Present


Postdoctoral Research Associate

CMSE, Michigan State University,   East Lansing, Michigan,   October 2020 - June 2023
- 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

Graduate Research Assistant

SCI Institute, University of Utah,   Salt Lake City, Utah,   May 2016 - July 2020
- 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

Education

PhD in Computing

University of Utah,   Salt Lake City, Utah,   August 2015 - July 2020
- Thesis: “Utilizing Topological Structures of Data for Machine Learning.” Advisor: Dr. Bei Wang-Phillips.

MSc in Applied Mathematics

University of Manchester,   Manchester, UK,   September 2013 - December 2014
- Dissertation: “Analysis of the Source Trajectory in Cone Beam Micro CT.” Advisor: Prof. Bill Lionheart.

Awards and Honors

American Mathematical Society (AMS)

Models and Methods for (Hyper) Network Science    2022 - 2023
- Invited to participate in Mathematical Research Communities (MRC)
- Established continued research collaborations to identify and solve open problems in hyper network science.

Simons Institute, UC Berkeley

Foundations of Data Science    Fall 2018
- Invited to participate as a visiting graduate researcher in the Fall semester program on mathematical foundations of data science.

XRadia (Zeiss) and University of Manchester

Dissertation Award    Summer 2014
- Awarded GBP 3000 in funding to carry out dissertation research with industrial collaborators.

Teaching

Guest Instructor, HRT 841: Plants and Python

Fall 2020, Michigan State University, East Lansing, Michigan
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.

Teaching Assistant, CS 6170: Computational Topology

Spring 2017, School of Computing, University of Utah, Salt Lake City, Utah
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.

Teaching Assistant, CS 6210: Advanced Scientific Computing

Fall 2016, School of Computing, University of Utah, Salt Lake City, Utah
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.

Publications

Any Graph is a Mapper Graph

Published in: ArXiv preprint [cs.CG] .
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Enrique Alvarado, Robin Belton, Kang-Ju Lee, et al. "Any Graph is a Mapper Graph." ArXiv preprint [cs.CG], 2024.

G-Mapper: Learning a Cover in the Mapper Construction

Published in: ArXiv preprint [cs.LG] .
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Enrique Alvarado, Robin Belton, Emily Fischer, Kang-Ju Lee, et al. "$G$-Mapper: Learning a Cover in the Mapper Construction." ArXiv [cs.LG], 2024.

Expression-based machine learning models for predicting plant tissue identity

Published in: Applications in Plant Sciences .
Download PDF: .    Code available here: .   
Sourabh Palande, Jeremy Arsenault, Patricia Basurto-Lozada, et al. "Expression-based machine learning models for predicting plant tissue identity." In Applications in Plant Sciences, e11621, 2024.

Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants

Published in: PLoS Biology .
Download PDF: .    Code available here: .   
Sourabh Palande, Joshua Kaste, Miles Roberts, et al. "Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants." In PLOS Biology 21(12): e3002397, 2023.

Plants & Python: A series of lessons in coding, plant biology, computation, and bioinformatics

Published in: The Plant Cell .
Code available here: .    Demo available here: .
Robert VanBuren, Alejandra Rougon-Cardoso, Erik Amézquita, et al. "Plants & Python: A series of lessons in coding, plant biology, computation, and bioinformatics." The Plant Cell, vol. 34, no. 7 (2022): e1.

TopoAct: Visually exploring the shape of activations in deep learning

Published in: Computer Graphics Forum .
Download PDF: .    Code available here: .    Demo available here: .
Archit Rathore, Nithin Chalapathi, Sourabh Palande, and Bei Wang. "TopoAct: Visually exploring the shape of activations in deep learning." In Computer Graphics Forum, vol. 40, no. 1, pp. 382-397. 2021.

Spectral sparsification of simplicial complexes for clustering and label propagation

Published in: Journal of Computational Geometry (JoCG) .
Download PDF: .    Code available here: .   
Braxton Osting, Sourabh Palande, and Bei Wang. "Spectral sparsification of simplicial complexes for clustering and label propagation." Journal of Computational Geometry 11, no. 1 (2020): 176-211.

Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference

Published in: Brain Connectivity .
Download PDF: .    Code available here: .   
Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P. Thomas Fletcher, and Bei Wang. "Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference." Brain Connectivity 9, no. 1 (2019): 13-21.

Sketching merge trees for scientific data visualization

Published in: 2023 Topological Data Analysis and Visualization (TopoInVis) .
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Mingzhe Li, Sourabh Palande, Lin Yan and Bei Wang, "Sketching Merge Trees for Scientific Visualization," In 2023 Topological Data Analysis and Visualization (TopoInVis), pp. 61-71, IEEE, 2023.

Uncertainty Visualization for Graph Coarsening

Published in: 2022 IEEE International Conference on Big Data (Big Data). .
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Fangfei Lan, Sourabh Palande, Michael Young, and Bei Wang. "Uncertainty Visualization for Graph Coarsening" 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan (2022).

Autism classification using topological features and deep learning: A cautionary tale

Published in: International Conference on Medical Image Computing and Computer-Assisted Intervention .
Download PDF: .    Code available here: .   
Archit Rathore, Sourabh Palande, Jeffrey Anderson, Brandon Zielinski, P. Thomas Fletcher, and Bei Wang. "Autism classification using topological features and deep learning: A cautionary tale." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 736-744. Springer, Cham, 2019.

Topological data analysis of functional MRI connectivity in time and space domains

Published in: Connectomics in NeuroImaging: Second International Workshop, CNI 2018 .
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Keri Anderson, Jeffrey Anderson, Sourabh Palande, and Bei Wang. "Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains." In Connectomics in NeuroImaging: Second international workshop, CNI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018: proceedings. CNI (Workshop)(2nd: 2018: Granada, Spain), vol. 11083, p. 67. NIH Public Access, 2018.

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) .
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Eleanor Wong, Sourabh Palande, Bei Wang, Brandon Zielinski, Jeffrey Anderson, and P. Thomas Fletcher. "Kernel partial least squares regression for relating functional brain network topology to clinical measures of behavior." In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1303-1306. IEEE, 2016.

Utilizing Topological Structures of Data for Machine Learning

Published in: The University of Utah ProQuest Dissertations Publishing .
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Sourabh Palande. "Utilizing Topological Structures of Data for Machine Learning." The University of Utah, (2020)