Mathias Niepert

Senior Researcher
NEC Labs Europe
Heidelberg, Germany

mathias dot niepert at neclab dot eu

MSc, PhD in Computer Science, Indiana University
Advisor: Dirk Van Gucht

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I am a senior researcher in the Systems and Machine Learning (SysML) group at NEC Labs Heidelberg. From 2012-2015 I was a research associate at the University of Washington in Seattle. I was also a member of the Data and Web Science Research Group at the University of Mannheim.

My research interests include representation learning for graph-structured data, unsupervised and semi-supervised learning, probabilistic graphical models, and statistical relational learning.

I am also co-founder of several open-source digital humanities projects such as the Indiana Philosophy Ontology Project and the Linked Humanities Project.

News

  • 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.
  • Our ongoing work on defining a spectrum of graph convolutional networks was accepted to IEEE Data Science Workshop
  • Our work on representation learning for knowledge bases with numerical, latent, and relational features accepted at UAI 2018.
  • I am a PC member of/reviewer for ICML, NIPS, UAI, AAAI, and IJCAI.
  • I am an invited speaker for the 27th International Conference on Inductive Logic Programming.
  • I am a PC member of/reviewer for ICML, NIPS, UAI, AAAI, and IJCAI.
  • The paper "Discriminative Gaifman Models" has been accepted at the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS).
  • The paper Learning Convolutional Neural Networks for Graphs has been accepted at the International Conference on Machine Learning (ICML).
  • (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)
  • Nominated by the University of Washington, I will present my work at the Information Theory and Applications Workshop (ITA) in San Diego
  • The paper "Out of Many, One: Unifying Web-Extracted Knowledge Bases" was accepted at the NIPS Workshop on Automated Knowledge Base Construction (AKBC) 2014
  • The paper "Lifted Probabilistic Inference for Asymmetric Graphical Models" was accepted at the 29th AAAI Conference on Artificial Intelligence
  • The paper "Generalized Conditional Independence and Decomposition Cognizant Curvature: Implications for Function Optimization" was accepted at the NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning
  • I was selected to participate in the 2nd Heidelberg Laureate Forum, to be held September 21-26 in Heidelberg, Germany
  • The paper "Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference" is one of 5 papers (out of 1400 submissions) nominated for the AAAI outstanding paper award.
  • The paper "Exchangeable Variable Models" was accepted at the International Conference on Machine Learning (ICML).
  • 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.