Topological Data Analysis (TDA): Persistent Homology for High-Dimensional Datasets
Uncover the Hidden Shape of Your Data with Topological Data Analysis.
Skills you will gain:
About Program:
Training is practical and Colab-friendly, using Python tools such as GUDHI/Ripser/Giotto-TDA + scikit-learn. Each day includes at least two hands-on sessions, ending with a structured mini-capstone pipeline (TDA → features → ML → interpretation). Suitable for students, PhD scholars, faculty, and industry professionals working with real-world high-dimensional datasets.
Aim: 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.
Program Objectives:
- 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.
What you will learn?
Day 1 — Geometry of Data
- Curse of dimensionality
- Simplicial complexes & filtrations
- Betti numbers & persistence diagrams
- Noise vs signal interpretation
- Hands-on 1: Build Rips complex
- Hands-on 2: Compute PH + visualize barcodes
Day 2 — TDA for Machine Learning
- Stability & diagram distances
- Vectorization (images, landscapes)
- TDA as feature engineering
- Scalability & computational trade-offs
- Hands-on 3: PH → ML classification
- Hands-on 4: Time-series anomaly detection
Day 3 — Advanced & Applied TDA
- Extended / multi-parameter persistence (conceptual)
- Embedding topology in deep learning
- When to use TDA (decision framework)
- Research & industry case study
- Hands-on 5: PH on embedding space
- Hands-on 6: Structured mini-capstone pipeline
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program Outcomes
- 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).