Overcoming Obstacles in Data Science

  • Experimental measurements are very noisy
  • Data have very high granularity (image sequences)
  • Extracting interpretable high-level dynamic information

How do we propose to overcome these obstacles?

  • Denoising techniques
  • Dimensionality reduction via clustering
  • Dynamic modeling: learned Markov model + spectral analysis to quantify temporal stability and identify mixing rates

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

Create your website with WordPress.com
Get started
%d bloggers like this: