I am working in the core algorithms team of Bing Ads, with a focus on
Simulation and optimization of auction marketplaces
Large scale machine learning
Image understanding via Deep Learning
I studied computer science at Graz Unversity of Technology, Austria.. From 2000 to 2003, I completed my Ph.D. thesis on machine learning. My Ph.D. work was funded by an Ernst-von-Siemens scholarship, and supervised jointly by Volker Tresp at Siemens Corporate Technology, Munich, and Wolfgang Mass at Graz Unversity of Technology.
In May 2004, I joined the Intelligent Data Analysis Group at Fraunhofer FIRST in Berlin as a postdoc researcher. In my work there, I was leading a technology transfer project funded by Bayer Schering Pharma. This project, PCADMET, aimed at developing machine learning methods for predicting different properties of chemical compounds in early drug discovery.
In February 2008, I joined Microsoft Research Cambridge.
Estimating the Domain of Applicability for Machine Learning QSAR RModels: A Study on Aqueous Solubility of Drug Discovery MoleculesTimon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, Klaus-Robert Müller, in Journal of Computer Aided Molecular Design, January 1, 2007,
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical BayesKai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, Hong-Jiang Zhang, in Uncertainty in Artificial Intelligence: Proceedings of the 19th Conference (UAI-2003), Morgan Kaufmann, January 1, 2003,
- Videos from the NIPS*2008 workshop “Beyond Search: Computational Intelligence for the Web” are from the workshop schedule.
- My article “Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach”“ is 2007’s most cited article in the Journal of Chemical Information and Modeling (Impact factor: 3.4, total citations in 2007: 7026, publisher: American Chemical Society) – see the ACS Excellence Newsletter
- You might be interested in the book “Dataset Shift in Machine Learning”, edited by Joaquin Quiñonero Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence (MIT Press, 2009)