Shreya Kapoor

Shreya Kapoor

Researcher @FAU Erlangen, Germany | Former Data Analyst at Max Planck Institute for Human Cognitive and Brain Sciences | BSc.(H) Physics and M.Sc. Life Science Informatics.

Conference Proceeding talk 3 on Relevant Topic in Your Field

less than 1 minute read

Published in Testing Institute of America 2014 Annual Conference, 2014

This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.

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Restricted Boltzmann Machines for Interpretable Neuroimaging

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This post is a write-up of a seminar report I wrote during my M.Sc. in Life Science Informatics at the University of Bonn, for the “Visualization and Medical Image Analysis” seminar at b-it (WiSe 2019/20): Using Generative-Discriminative Learning in Neuroimaging for Interpretable Predictions. The full report is linked at the bottom; this is the part of it I still think about most, Restricted Boltzmann Machines, and why a fairly old, physics-flavored idea turned out to be a useful tool for making neuroimaging classifiers less of a black box.

Prosopagnosia: When Faces Refuse to Stick

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Published:

There is a particular kind of disorientation in not recognizing a face you’ve seen a hundred times: a colleague, a neighbor, sometimes even your own reflection in a photograph. This is prosopagnosia, or face blindness, the inability to recognize faces despite otherwise normal vision and intact intelligence. The name comes from the Greek prosopon (face) and agnosia (not knowing), and it was first described by the neurologist Joachim Bodamer in 1947.

Modeling Why the Necker Cube Won’t Sit Still

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Most write-ups of visual illusions stop at showing you the picture: here’s a cube that flips, isn’t the brain weird. That’s true but incomplete, it doesn’t say why perception should flip at all, rather than just settling on one interpretation and staying there, or why the flips arrive when they do rather than on some fixed schedule. This post builds a small computational model of exactly that switching process, for the Necker cube specifically, and asks whether a handful of very simple neural dynamics can reproduce the statistical signature of real perceptual switching.

Extracting the Most Predictive Subgraphs from the Human Connectome

9 minute read

Published:

This post is a summary of my Master’s thesis, Extracting Most Predictive Subgraphs From Models of Human Brain Connectivity, submitted in November 2020 for my M.Sc. in Life Science Informatics at the Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn. Examiners: Prof. Dr. Thomas Schultz and Prof. Dr. Holger Fröhlich; advisor: Mohammad Khatami. The full PDF is linked at the bottom.