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Mike Schaekermann

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

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CHI2016: Resolvable vs. Irresolvable Ambiguity

In May 2016, I presented a position paper from our research group at the workshop on Human-Centered Machine Learning (HCML) at the CHI2016 conference in San Jose. In this paper, we tackle the conventional assumption that, in supervised machine learning, ground truth data always needs to provide exactly one correct label per training example. We propose a new hybrid framework for dealing with uncertainty in ground truth that fully acknowledges the notion of irresolvable expert disagreement.


This paper was one of two workshop papers from our research group, accepted at CHI2016 workshops:

  1. Schaekermann, Mike, Edith Law, Alex C Williams, and William Callaghan. “Resolvable Vs. Irresolvable Ambiguity: A New Hybrid Framework for Dealing with Uncertain Ground Truth.” In 1st Workshop On Human-Centered Machine Learning at SIGCHI 2016. San Jose, CA, 2016. View PDF
  2. Williams, Alex C, Josh Bradshaw, Mike Schaekermann, Timmy Tse, William Callaghan, and Edith Law. “The Big Picture: Preserving Context In the Decomposition of Complex Expert Tasks.” In 1st Workshop On Microproductivity at SIGCHI 2016. San Jose, CA, 2016. View PDF


Hacking Brain-Computer Interfaces

In March 2015, I had the pleasure to give a talk on brain-computer interface (BCI) technologies at the “Singularity meets Self-Improvement” (SMSI) meetup in Berlin which is regularly hosted by AI researcher and start-up founder Dr. Trent McConaghy. In this talk, the focus is on different ways of presenting, processing and analyzing electroencephalographic (EEG) data, alleging some fun examples from real-world BCI applications. In addition to that, the open-source/open-hardware device OpenBCI is introduced and demonstrated.

Implicit Surface Modeling for 3D Printing

In July 2015, the award-winning computer graphics experts at xy:matic invited to the first-ever WebGL Meetup Berlin at the time for which I was a speaker on the topic of implicit surface modeling for 3D printing. Implicit surfaces are a traditional way of accurately representing the shape of complex 3D objects in space. This type of representation was neglected in the field of computer graphics for a long time due to insufficient computing capabilities in GPUs. Today, as GPUs have become increasingly powerful, implicit surfaces are flourishing again as representations of customizable objects for 3D printing.