Sourabh Palande, PhD

Sourabh Palande, PhD

Data Scientist II

Curriculum Vitae

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Summary

    I'm a Data scientist, an applied mathematician and a researcher in machine learning theoryand applications. My work focuses on development and implementation of novel methods that leverage topology and geometry in machine learning, data analysis and visualization. I've published research articles spanning a range of topics in leading journals and conferences. I have experience working with imaging (MRI, fMRI, 3D X-Ray CT, RGB) and network-structured (brain networks, *omics) data. I enjoy solving complex problems and I'm passionate about advancing science and making a real-world imapct with my research.

Work Experience

  • 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

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

Preprints

  • The topological shape of gene expression across the evolution of flowering plants

    Published in: bioRxiv preprint [Plant Biology] .
    Download PDF: .    Code available here: .   
    Sourabh Palande, Joshua AM Kaste, Miles D Roberts, Kenia Segura Aba, Carly Claucherty, Jamell Dacon, Rei Doko, Thilani B Jayakody, Hannah R Jeffery, Nathan Kelly, Andriana Manousidaki, Hannah M Parks, Emily M Roggenkamp, Ally M Schumacher, Jiaxin Yang, Sarah Percival, Jeremy Pardo, Aman Y Husbands, Arjun Krishnan, Beronda L Montgomery, Elizabeth Munch, Addie M Thompson, Alejandra Rougon-Cardoso, Daniel H Chitwood, Robert VanBuren. "The topological shape of gene expression across the evolution of flowering plants." bioRxiv preprint bioRxiv:10.1101/2022.09.07.506951(2022).

Journal Articles

Conference Papers

Dissertation