Martin Trapp (Preferred)
Martin Trapp
Aalto University
Names
How do you usually write your name as author of a paper? Also add any other names you have authored papers under.
Emails
Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments.
****@ofai.at
, ****@gmail.com
, ****@tugraz.at
, ****@aalto.fi
Personal Links
Add links to your profiles on other sites. (Optional)
Education & Career History
Enter your education and career history. The institution domain is used for conflict of interest detection and institution ranking. For ongoing positions, leave the end field blank.
Postdoc
Aalto University (aalto.fi)
2020 – Present
PhD student
Graz University of Technology (tugraz.at)
2016 – 2020
Researcher
Austrian Research Institute for Artificial Intelligence (ofai.at)
2015 – 2019
Visitor
University of Cambridge (cam.ac.uk)
2018 – 2018
Researcher
VRVis Research Center (vrvis.at)
2009 – 2015
MS student
Technical University of Vienna (tuwien.ac.at)
2009 – 2013
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coauthor
Kristian Kersting
****@cs.tu-darmstadt.de
Present
Coauthor
Antonio Vergari
****@cs.ucla.edu
Present
Coauthor
Zoubin Ghahramani
****@eng.cam.ac.uk
Present
Coauthor
Hong Ge
****@cam.ac.uk
Present
Postdoc Advisor
Arno Solin
****@aalto.fi
2020 – Present
PhD Advisor
Franz Pernkopf
****@tugraz.at
2016 – 2020
PhD Advisor
Robert Peharz
****@tue.nl
2016 – 2020
PhD Advisor
Robert Trappl
****@ofai.at
2014 – 2019
Coauthor
Tamas Madl
****@ofai.at
2014 – 2019
Expertise
For each line, enter comma-separated keyphrases representing an intersection of your interests. Think of each line as a query for papers in which you would have expertise and interest. For example: deep learning, RNNs, dependency parsing
Sum-Product Networks
Present
Bayesian nonparametrics
Present
Tractable Probabilistic Models
Present
Probabilistic Circuits
Present
Bayesian deep learning
Present
Probabilistic Programming
Present