My main interest is in the application of machine learning systems to real world problems. Domains I have worked in so far include: bio acoustic modeling of dolphin communication, gesture recognition, web recommender systems and wearable computing. Besides machine learning I am always interested in exploring novel programming languages, data bases and networking systems.
My research interest during my PhD was focused on analysing sequential data recorded from biological signals such as dolphins or human gestures. In particular I was working on data driven methods for indexing, annotation and discovery of high dimensional sensor streams. My interest was to find and model patterns in dolphin communication and human activity data. Often behavioral data such as animal communication or human activity in everyday life show language like structures. My algorithms are designed to find the atomic units of such languages automatically from data as well as the temporal structure of their occurrence. In the end I believe that the analysis of continuous sensor data in terms of interpretable, language like structures can help to explain unexplored domains such as dolphin communication as well as help to build recognizers for complex domains such as activity recognition in every day life situations or false positive free gesture recognition. The systems I implemented for these purposes used convolutional unsupervised feature learning, hidden Markov models as well as grammar induction using alignment based learning.
In general, I aim to create intelligent agents that discover how data is organized naturally and communicate their findings in human understandable form.