Dimensionality Reduction and Kernel Approaches for Biosignals 

19th March 2026

Timing : 1 pm ET

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For a list of all talks at the NanoBio seminar series Spring'26, see here

Biosignals are complex, high-dimensional, and rich with latent structure that is often difficult to interpret directly. In this talk, I will present two connected threads of my research aimed at addressing this challenge: dimensionality reduction and kernel-based analysis. On one side, manifold learning methods such as UMAP provide a way to uncover low-dimensional structure, visualize organization in data. On the other, kernel approaches offer a principled framework for comparing representations and quantifying structural similarity in biosignals. Through these ideas, I will show how geometry-aware methods can make biosignal analysis more interpretable, more rigorous, and more useful for downstream applications.

Dr. Mohammad Tariqul Islam
Dr. Mohammad Tariqul Islam is a postdoctoral scholar advised by Prof. Deblina Sarker at the Nano-Cybernetic Biotrek lab at Massachusetts Institute of Technology. He is supported by the MIT-Novo Nordisk Artificial Intelligence Fellowship. He completed PhD at Princeton University under the supervision of Prof. Jason Fleischer at the Imaging Physics Group. Tariq's research interest includes signal processing and machine learning focusing on biomedical applications.