Steven Lang

TU Darmstadt

Names

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

Steven Lang (Preferred)

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.

****@cs.tu-darmstadt.de

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.

PhD student
TU Darmstadt (tu-darmstadt.de)
2021Present
 
MS student
TU Darmstadt (tu-darmstadt.de)
20182021
 
Undergrad student
University of Mainz (uni-mainz.de)
20132017
 

Advisors, Relations & Conflicts

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

Coauthor
Martin Mundt
****@cs.tu-darmstadt.de
2021Present
 
Coauthor
Fabrizio Ventola
****@cs.tu-darmstadt.de
2021Present
 
Coauthor
Quentin Delfosse
****@cs.tu-darmstadt.de
2021Present
 
PhD Advisor
Kristian Kersting
****@cs.tu-darmstadt.de
2020Present
 
Coauthor
Karl Stelzner
****@cs.tu-darmstadt.de
2020Present
 
Coauthor
Robert Peharz
****@tue.nl
20202020
 
Coauthor
Antonio Vergari
****@cs.ucla.edu
20202020
 
Coauthor
Alejandro Molina
****@cs.tu-darmstadt.de
20202020
 
Coauthor
Martin Trapp
****@aalto.fi
20202020
 
Coauthor
Guy Van den Broeck
****@cs.ucla.edu
20202020
 
Coauthor
Zoubin Ghahramani
****@eng.cam.ac.uk
20202020
 
Coauthor
Eibe Frank
****@waikato.ac.nz
20192019
 

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
 
oriented object detection
Present
 
object detection
Present
 
deep generative models
Present
 
density estimation
Present
 
probabilistic circuits
Present
 
deep learning
Present
 
probabilistic models
Present