My research interests include representation learning for graph-structured data, geometric deep learning,
probabilistic graphical models, and statistical relational learning. My group's methods are concerned with learning, inducing, and leveraging relational structure with applications in vision, natural language processing, and the (bio-)medical domain.
I am an invited speaker at the IPAM workshop "Geometric Deep Learning for Big Data and Applications" in LA, the Karlsruhe.AI and Heidelberg.AI speaker series, the RIKEN AI Insitute in Tokyo, and the ECML workshop "New Trends in Representation Learning with Knowledge Graphs"
We released MMKG a collection of multi-modal knowledge graphs with images and numerical features. The KGs are sub-KGs of DBpedia and Yago whose entities are those found in the commonly used KG completion benchmark FB15k.
Our work on augmenting hourglass networks with shortcut connections between conv layers of different spatial extent was accepted at MIDL, the Medical Imaging with Deep Learning conference.
(03/27/16) I am co-organizing the IJCAI-16 workshop Statistical Relational AI. Please visit the workshop's website for more information (including some exciting invited talks) and consider submitting your work!
The paper "Learning and Inference in Tractable Probabilistic Knowledge Bases" was accepted at the 31st Conference on Uncertainty in Artificial Intelligence. (UAI)
The paper "Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference" was accepted at this year's AAAI conference
I am serving on the PC of the AAAI workshop StarAI and the SIGMOD/PODS workshop BUDA.
Pedro Domingos, Daniel Lowd, and I are organizing the ICML workshop Learning Tractable Probabilistic Models. Please visit the workshop's website for more information (including some exciting invited talks) and consider submitting your work!
I will serve on the PC of AAAI, ECAI, and UAI 2014.