Prof. Dr. Christian Bauckhage

Christian Bauckhage ist Professor für Informatik (Mustererkennung) an der Universität Bonn und Lead Scientist für Maschinelles Lernen am Fraunhofer IAIS. Nach erfolgreicher Promotion in Informatik an der Universität Bielefeld arbeitete er zunächst als PostDoc am Centre for Vision Research in Toronto und anschließend als Senior Research Scientist bei den Deutsche Telekom Laboratories in Berlin, bevor er 2008 nach Bonn berufen wurde. Seine Erfahrung im Bereich Data Science und Künstliche Intelligenz erstreckt sich bereits über mehr als 20 Jahre und begründet seine Rolle als renommierten KI-Experte und Referent auf Veranstaltungen, Messen und Tagungen. Aktuell befasst sich Prof. Bauckhage vor allem mit dem Thema Quantencomputing und den durch Quantencomputern entstehenden Möglichkeiten, bisher nahezu unlösbare Verfahren der Künstlichen Intelligenz und des Maschinellen Lernens zu erschließen.

Ausgewählte Publikationen

C. Bauckhage, R. Sifa, and S. Wrobel. Adiabatic Quantum Computing for Max-Sum Diversification. Proc. SIAM Int. Conf. on Data Mining, 2020

C. Bauckhage and R. Sifa. Joint Selection of Central and Extremal Prototypes Based on Kernel Minimum Enclosing Balls, Proc. IEEE Int. Conf.  on  Data Science and Advanced Analytics, 2019

C. Bauckhage, R. Sifa, and T. Dong. Prototypes within Minimum Enclosing Balls, Proc. Int. Conf on Artificial Neural Networks, 2019

T. Dong, Z. Wang, J. Li, C. Bauckhage, and A.B. Cremers. Triple Classification Using Regions and Fine-grained Entity Typing, Proc. AAAI Conf. on Artificial Intelligence, 2019

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, J. Schücker, and R. Sifa. Towards Shortest Paths Via Adiabatic Quantum Computing. Proc. Mining and Learning with Graphs, 2018

B. Wulff, J. Schücker, and C. Bauckhage. SPSA for Layer-wise Training of Deep Networks, Proc. Int. Conf on Artificial Neural Networks, 2018

R. Sifa and C. Bauckhage. Online k-Maxoids Clustering. Proc. IEEE Int. Conf.  on  Data Science and Advanced Analytics,  2017
 
C. Bauckhage. A Neural Network Implementation of Frank-Wolfe Optimization. Proc. Int. Conf on Artificial Neural Networks, 2017
 
R. Ramamurthy, C. Bauckhage, K. Buza, and S. Wrobel. Using Echo State Networks for Cryptography. Proc. Int. Conf on Artificial Neural Networks, 2017
 
C. Ojeda, K. Cvejoski, R. Sifa, C. Bauckhage. Inverse Dynamical Inheritance in Stack Exchange Taxonomies. Proc. AAAI Int. Conf. on Web and Social media, 2017

C. Bauckhage and K. Kersting. Collective Attention on the Web. Foundations and Trends in Web Science 5(1-2), 2016

M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting. Propagation kernels: efficient graph kernels from propagated information. Machine Learning 102(2), 2016

M. Höhne, A. Jahanbekam, C. Bauckhage, N. Axmacher, and J. Fell. Prediction of successful memory encoding based on single-trial rhinal and hippocampal phase information. NeuroImage 139, 2016

S. Zhang, D.A. Klein, C. Bauckhage, and A.B. Cremers. Fast Moving Pedestrian Detection Based on Motion Segmentation and New Motion Features. Multimedia Tools and Applications, 2015

S. Zhang, C. Bauckhage, and A.B. Cremers. Efficient Pedestrian Detection via Rectangular Features Based on a Statistical Shape Model. IEEE Trans. Intelligent Transportation Systems, 16(2), 2015

C. Bauckhage, F. Hadiji, and K. Kersting. How Viral Are Viral Videos?. Proc. AAAI Int. Conf. on Weblogs and Social Media, 2015

C. Bauckhage, K. Kersting, and B. Rastegarpanah. Collective Attention to Social Media Evolves According to Diffusion Models. Proc. ACM Int. World Wide Web Conf., 2014

C. Bauckhage and K. Manshaei. Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series. Proc. IAPR Int. Conf. on Pattern Recognition, IEEE, 2014

C. Bauckhage, K. Kersting, and F. Hadiji. Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes. Proc. AAAI Int. Conf. on Weblogs and Social Media, 2013

C. Bauckhage and K. Kersting. Can Computers Learn from the Aesthetic Wisdom of the Crowd? KI - Künstliche Intelligenz, 27(1), 2013

C. Bauckhage and K. Kersting. Data Mining and Pattern Recognition in Agriculture. KI - Künstliche Intelligenz, 27(4), 2013

G. Evangelidis and C. Bauckhage. Efficient Subframe Video Alignment using Short Descriptors. IEEE Trans. Pattern Analysis and Machine Intelligence, 35(10), 2013

C. Bauckhage, K. Kersting, and A. Schmit. Agriculture's Technological Makeover. IEEE Pervasive Computing, 11(2), 2012

C. Thurau, K. Kersting, M. Wahabzada, and C. Bauckhage. Descriptive Matrix Factorization for Sustainability: Adopting the Principle of Opposites. Data Mining and Knowledge Discovery, 24(2), 2012

B. Krausz and C. Bauckhage. Loveparade 2010: Automatic video analysis of a crowd disaster. Computer Vision and Image Understanding, 116(3), 2012

C. Bauckhage. Insights into Internet Memes. Proc. AAAI Int. Conf. on Weblogs and Social Media, 2011

C. Thurau, K. Kersting, M. Wahabzada, and C. Bauckhage. Convex Non-negative Matrix Factorization for Massive Datasets. Knowledge and Information Systems, 29(2), 2011

C. Thurau, K. Kersting, and C. Bauckhage. Yes We Can: Simplex Volume Maximization for Descriptive Web-scale Matrix Factorization. Proc. ACM Conf. on Information and Knowledge Management, 2010

R. Wetzker, C. Zimmermann, C. Bauckhage, and S. Albayrak. I Tag, You Tag: Translating Tags for Advanced User Models. Proc. ACM Int. Conf. on Web Search and Data Mining, 2010

C. Bauckhage and C. Thurau. Making Archetypal Analysis Practical, Proc. DAGM Symposium, 2009

J. Kunegis, A. Lommatzsch, and C. Bauckhage. The Slashdot Zoo: Mining a Social Network with Negative Edges. Proc. ACM Int. World Wide Web Conf., 2009

C. Bauckhage, J.K. Tsotsos, and F.E. Bunn. Automatic Detection of Abnormal Gait. Image and Vision Computing, 27(1-2), 2009