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Mike Schaekermann

University of Waterloo Computer Science Ph.D. candidate with a degree in Medicine

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Framework to combine machine and human intelligence for the scalable and accurate analysis of human clinical EEG recordings. CrowdEEG is an active research project in professor Edith Law’s CrowdLab at the University of Waterloo. I am currently conducting a study investigating the effects of implicit contextual information on the agreement rates among experts in a sleep staging task. The goal of the study is to identify such contextual dependencies and make them explicit for crowdworkers to be used in a similar crowdsourcing task.

Collaborative Biosignal and Gameplay Analysis

A collaborative web application for annotating gameplay videos, based on biometric time series data. This tool was developed as part of my bachelor thesis at Salzburg University of Applied Sciences.

Implicit Surface Modeling for 3D Printing

This project is an experimental prototype for the real-time customization and animation of 3D-printable objects. It makes use of implicit surfaces, raymarching and the isosurface extraction algorithm Marching Cubes.

3D Simulation of an Endocrine System

During my medical studies at the University of Marburg, I created this project as part of an elective course on “Simulation Methods in Physiology and Neurobiology”. It is a real-time 3D simulation of the hypothalamic-pituitary-adrenal (HPA) axis, a part of the human neuro-endocrine system, that controls reactions to stress and regulates many body processes, including digestion, the immune system, mood and emotions, sexuality, and energy storage and expenditure.