25 March 2015

Meet Christian Igel - our outstanding Machine Learning researcher

Researcher portrait.

Professor Christian Igel teaches Machine Learning. In his research he uses Machine Learning to adress problems of societal relevance.



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"I am developing and analysing algorithms that extract useful information from data - algorithms that learn.

Christian Igel

What courses are you teaching at present?

- I am responsible for the course "Machine Learning". The amount and complexity of available data is steadily increasing. To make use of this wealth of information, computing systems are needed that turn the data into knowledge. Machine learning is about developing the required software that automatically analyses data for making predictions, categorizations, and recommendations. Machine learning algorithms are already an integral part of today's computing systems - for example in search engines, recommender systems, or biometrical application. The course introduces the most common techniques from statistical machine learning and pattern recognition.

- Furthermore, I am teaching part of the elective course "Advanced Topics in Machine Learning" for students who want to learn more about machine learning. I am also contributing to the course "Data Analysis Methods", in which non-Computer Science students can obtain a working knowledge of basic data modeling and data analysis using fundamental machine learning techniques.

What are your current research interests?

- As a machine learning researcher, I am developing and analysing algorithms that extract useful information from data - algorithms that learn.

Which of your research results are you the most proud of?

- I love both theory and algorithm engineering, and I am most happy if I can prove insightful properties of algorithms that are employed in practice and if the methods developed in my group are used by other people.

- For instance, my colleagues and I devised very fast learning algorithms (e.g., for support vector machines and also for neural networks) and novel multi-objective optimisation techniques, which we make available through our award-winning open-source machine learning library Shark.

- It is my goal to apply machine learning to addressing problems of societal relevance. For instance, my colleagues and I work on using machine learning to derive better biomarkers for diagnosing and prognosing diseases (e.g., breast cancer or Alzheimer's disease) or to support the verification of the nuclear test ban treaty.