## Tomography 203

This video illustrates “ill-posedness”, the nemesis of all inverse problems. We see how in a very simple 12×12 pixel tomography problem “almost-ghosts” can produce very different targets with almost the same X-ray data.

This video illustrates “ill-posedness”, the nemesis of all inverse problems. We see how in a very simple 12×12 pixel tomography problem “almost-ghosts” can produce very different targets with almost the same X-ray data.

I started a new video series in my all-English YouTube channel called “Professor Sam”. Today’s upload is Tomography 201: tomography by pixels, which introduces the need for iterative variational regularization in tomographic image reconstruction with sparsely collected data. This 200-series of videos is a continuation of the 100-series explaining the basics of tomography.

I just published a new video in my lecture series on X-ray tomography (link). There I show how to use Matlab to simulate basic tomographic things such as sinograms and filtered back-projection algorithms. I also give an introductory discussion of measurement noise and how to supress it with a low-pass filter. This is how it

I gave a talk in the “Math Encounters” series at the Museum of Mathematics in New York City in February 2023. My topic was X-ray tomography. Now the video recording of the presentation is available! Enjoy!

I co-organized a workshop in March 2023 at the Isaac Newton Institute in Cambridge: https://www.newton.ac.uk/event/rntw02/ It was so nice to see many colleagues after a long break! Instead of a regular scientific talk, I gave a presentation by title “Science Showmanship”. It contained some scientific stand-up comedy and stories about being a mathematical YouTuber. In

My entertaining, yet scientifically accurate video series continues. It’s my educational experiment with material that serves both science popularization and academic teaching. Enjoy!

I made a fun X-ray tomography video about pi. Enjoy! (And make sure to subscribe to my channel Professor Sam)

I present a three-rule model for collective behaviour. Computational experiments show how he three rules can simulate realistic movement in a simulated school of fish.

This video starts my lecture series on the mathematical concept of convolution. The lecture is part of my course “Inverse Problems 1: Convolution and Deconvolution” that I currently teach at University of Helsinki, Finland. I am a professor of Industrial Mathematics at UH.

Can a computer learn to distinguish between summer and winter? Of course. I will describe in detail how artificial intelligence can be taught to perceive temperatures.