I'm currently working on my Ph. D. on Image and Video Quality Assessment. I'm a proud member of the SFB-TRR 161 on Quantitaive Methods for Visual Computing and have happily contributed to their Visual Computing blog series on topics like "Eye Tracking and Beyond", "How to use Crowdsourcing for Research?" or "Eye Tracking in the Context of Image Quality Assessment".
More information about my research interests, teaching experience and vita below.
A Means to an End
Consumption of multimedia content is at an all time high, due to the increased accessibility of devices capable of recording and playing multimedia content via the Internet. In particular, global mobile video traffic of upward of 8 billion devices made up 60% of global mobile data traffic in 2016 and is expected to exceed 78% by 2021, according to Cisco's Visual Networking Index. Naturally, this increase of exposure to video also affects user awareness of video characteristics including its perceptual quality. This consideration gives rise to the need for service providers to assess the users' Quality of Experience and for users to obtain information about a given video's perceived quality.
More than meets the eye
Eye Tracking has been growing in popularity for over 20 years. Regardless of whether it's about Multimedia Quality Assessment, User Experience, Interface Design, or even just Presentation of Information, Eye Tracking has been heralded as a silver bullet - the ultimate technology with which to diagnose issues. In my case, it is without a doubt an important part of my research, as video quality opinions are fundamentally linked to what we pay (don't) attention to. Quite simply, in VQA, visual attention is the basis for all further processing steps. So the real question then seems to be what can we get from eye tracking for VQA, and when is it a worthwhile expense? Like any evolving technology, eye tracking has the potential to come into everyday use as progress eliminates barriers to entry.
Humane Machine Intelligence
Currently the big hype in computer science is around artificial intelligence – imbuing computers with the ability to learn from data and make rational decisions in areas such as financial trading or healthcare. From September to December 2014, just nine AI companies raised $201.6 million from Silicon Valley investors who all want in on the gold rush. But emotion-sensing is as important for a machine’s intelligence as data-driven rationality. At home, your emotion-sensing refrigerator could tell you to resist the ice cream today, based on your stress levels, or your car could warn you to drive slowly this morning because you seem upset. The opportunities are plentiful and the improvements to our lives this research field can bring about are infathomable. Step aside, artificial intelligence, the age of the emotional intelligence is coming.
|2018 Summer||Lecture and Lab: Eye Tracking - Theory and Practice||Course Information|
|2017/2018 Winter||Seminary-Lecture: Affective Computing in Multimedia||Course Information|
|2017 Summer||Lecture and Lab: Crowdsourcing|
|2016/2017 Winter||Lecture and Lab: Eye Tracking - Theory and Practice|
|2017 Summer||Lecture: Eye Tracking - Theory and Practice|
|since 10/2015||Research assistant, Department of Computer and Information Science, University of Konstanz|
|03/2015||Master Thesis: Decomposition, Automatic Kernel Construction, and Prediction of Time Series Data|
|02/2011 - 03/2015||Master studies of Artificial Intelligence at Maastricht University|
|09/2007 - 01/2011||Bachelor studies of Knowledge Engineering at Maastricht University|