Piotr Koniusz

Australian National University

Names

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

Piotr Koniusz (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.

****@nicta.com.au
,
****@data61.csiro.au
,
****@surrey.ac.uk
,
****@inria.fr
,
****@data61.csiro.au
,
****@anu.edu.au

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 honorary lecturer
Australian National University (anu.edu.au)
2016Present
 
senior research scientist
Data61, CSIRO (data61.csiro.au)
2016Present
 
Postdoc
INRIA (inria.fr)
20142015
 
phd
University of Surrey (surrey.ac.uk)
20092014
 

Advisors, Relations & Conflicts

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

Coauthor
Fei Yan
****@surrey.ac.uk
20112014
 
Coauthor
Krystian Mikolajczyk
****@surrey.ac.uk
20112014
 
Coauthor
Josef Kittler
****@surrey.ac.uk
20112014
 
Coauthor
Julien Mairal
****@m4x.org
20112014
 
Coauthor
Zaid Harchaoui
****@inria.fr
20112014
 
Coauthor
Cordelia Schmid
****@inrialpes.fr
20112014
 
Coauthor
Muhammad Tahir
****@iiu.edu.pk
20112014
 
Coauthor
Cordelia Schmid
****@inria.fr
20112014
 
Coauthor
Julien Mairal
****@inria.fr
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

node embeddings, node classification, graph classification, higher-order tensors, kernels, bilinear pooling, feature learning, zero-, one- and few-shot learning, domain adaptation
Present