Andreas Lehrmann

Borealis AI

Names

How do you usually write your name as author of a paper? Also add any other names you have authored papers under.

Andreas Lehrmann
,
Andreas M. Lehrmann

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.

****@gmail.com
,
****@tue.mpg.de
,
****@disneyresearch.com

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.

Senior Research Lead
Borealis AI (rbc.com)
20192021
 
Postdoc
Facebook (facebook.com)
20172019
 
Postdoc
Disney Research, Disney (disneyresearch.com)
20162017
 
PhD student
Swiss Federal Institute of Technology (ethz.ch)
20142016
 
PhD student
Max-Planck-Institute for Intelligent Systems (is.mpg.de)
20122016
 
MS student
University of Tuebingen (uni-tuebingen.de)
20052011
 

Advisors, Relations & Conflicts

Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.

Postdoc Advisor
Iain Matthews
****@oculus.com
2017Present
 
Postdoc Advisor
Leonid Sigal
****@cs.ubc.ca
20152017
 
PhD Advisor
Luc van Gool
****@vision.ee.ethz.ch
20142016
 
PhD Advisor
Peter Gehler
****@tuebingen.mpg.de
20122016
 
PhD Advisor
Sebastian Nowozin
****@gmail.com
20122016
 
Coauthor
Sebastian Nowozin
****@gmail.com
20132014
 
Coauthor
Peter Gehler
****@tuebingen.mpg.de
20112014
 
Coauthor
Peter Gehler
****@tue.mpg.de
20112014
 
Coauthor
Sebastian Nowozin
****@microsoft.com
20112014
 

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

deep learning, deep structured models, dynamic neural networks, non-parametric neural networks, deep generative networks, variational autoencoders, generative adversarial networks
20142017
 
approximate inference, variational inference in non-conjugate models, Monte Carlo inference in continuous graphical models, neural inference in non-parametric belief networks
20122017
 
dynamic scene modeling, personalized AR, conditional image and video synthesis, structured visual question answering, object and action detection, human pose and motion estimation, image and video segmentation
20122017