Topological Data Analysis (TDA): Persistent Homology for High-Dimensional Datasets

The aim of this workshop is to equip participants with the theoretical understanding and practical skills required to apply Topological Data Analysis (TDA) and Persistent Homology to high-dimensional datasets for extracting robust structural insights and integrating them into modern machine learning and research workflows.

• Introduce the fundamental concepts of Topological Data Analysis (TDA) and Persistent Homology.
• Enable participants to analyze high-dimensional datasets by constructing simplicial complexes and filtrations.
• Teach participants to compute and interpret persistence diagrams and barcode plots for distinguishing signal from noise.
• Show how to convert persistent homology features into machine learning–ready data representations.
• Equip participants to integrate TDA techniques into classification and anomaly detection workflows.
• Provide hands-on experience with Python-based TDA tools, such as GUDHI, Ripser, and Giotto-TDA.
• Develop practical TDA pipelines for real-world applications in research and industry.

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
• Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.

• Explain core TDA concepts and the purpose of Persistent Homology.
• Build filtrations/simplicial complexes from high-dimensional data.
• Compute and interpret barcodes and persistence diagrams (signal vs noise).
• Convert persistence results into ML features (images/landscapes/curves).
• Apply TDA in classification and anomaly detection workflows.
• Implement an end-to-end TDA pipeline in Python (using tools like GUDHI, Ripser, Giotto-TDA).