Valentina Zantedeschi (Preferred)
Valentina Zantedeschi
ServiceNow Research
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.
****@gmail.com
, ****@servicenow.com
Personal Links
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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.
Researcher
ServiceNow Research (servicenow.com)
2022 – Present
Emeritus
University College London (ucl.ac.uk)
2021 – Present
Postdoc
INRIA (inria.fr)
2021 – 2022
PhD student
University Jean Monnet (univ-st-etienne.fr)
2015 – 2019
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coauthor
Pascal Germain
****@ift.ulaval.ca
Present
Coauthor
Ambrish Rawat
****@ie.ibm.com
Present
Coauthor
Maria-Irina Nicolae
****@bosch.com
Present
Coauthor
Matt Kusner
****@gmail.com
Present
PhD Advisor
Remi Emonet
****@univ-st-etienne.fr
Present
PhD Advisor
Marc Sebban
****@univ-st-etienne.fr
Present
Coauthor
Vlad Niculae
****@uva.nl
Present
Coauthor
Luca Franceschi
****@amazon.de
2022 – Present
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, atmospheric physics
Present
adversarial robustness, certified defenses
Present
decentralized learning, graph learning, multi-task learning
Present
kernel methods, kernel approximations
Present
learning theory, pac-bayesian guarantees
Present
causal discovery, causal representation learning
Present