Decebal Constantin Mocanu

University of Luxemburg

Names

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

Decebal Constantin Mocanu (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.

****@tue.nl
,
****@utwente.nl
,
****@gmail.com
,
****@uni.lu

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.

Associate Professor
University of Luxemburg (uni.lu)
2023Present
 
Assistant Professor
Eindhoven University of Technology (tue.nl)
2017Present
 
Assistant Professor
University of Twente (utwente.nl)
20202023
 
PhD student
Eindhoven University of Technology (tue.nl)
20132017
 
Visiting Scholar
University of Texas, Austin (utexas.edu)
20162016
 
Visiting Scholar
University of Pennsylvania (upenn.edu)
20142014
 
MSc student
Maastricht University (maastrichtuniversity.nl)
20112013
 

Advisors, Relations & Conflicts

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

Family
Elena Mocanu
****@utwente.nl
Present
 
PhD Advisee
Ghada Sokar
****@tue.nl
2019Present
 
PhD Advisee
Shiwei Liu
****@tue.nl
20182022
 
PhD Advisee
Anil Yaman
****@vu.nl
20172019
 
PhD Advisor
Antonio Liotta
****@unibz.it
20132017
 
MSc Advisor
Karl Tuyls
****@liverpool.ac.uk
20122013
 

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

sparse neural networks
Present
 
deep learning
Present
 
complex networks
Present
 
continual learning
Present
 
sparse training
Present
 
reinforcement learning
Present