Marco Bagatella (Preferred)
Marco Bagatella
Max Planck Institute for Intelligent Systems, Max Planck Institute for Intelligent Systems
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.
****@tue.mpg.de
, ****@ethz.ch
, ****@pm.me
Personal Links
Add links to your profiles on other sites. (Optional)
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
Max Planck Institute for Intelligent Systems, Max Planck Institute for Intelligent Systems (is.tue.mpg.de)
2022 – Present
Intern
Max Planck Institute for Intelligent Systems, Max Planck Institute for Intelligent Systems (is.tue.mpg.de)
2020 – 2021
MS student
Swiss Federal Institute of Technology (ethz.ch)
2019 – 2021
Undergrad student
Politecnico di Milano (polimi.it)
2016 – 2019
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
PhD Advisor
Andreas Krause
****@inf.ethz.ch
2022 – Present
PhD Advisor
Georg Martius
****@tue.mpg.de
2020 – Present
Advisor
Sammy Christen
****@inf.ethz.ch
2021 – 2022
Supervisor
Otmar Hilliges
****@inf.ethz.ch
2021 – 2022
Supervisor
Michal Rolinek
****@tue.mpg.de
2020 – 2021
Supervisor
Luis Haug
****@inf.ethz.ch
2020 – 2020
Supervisor
Paolo Penna
****@inf.ethz.ch
2020 – 2020
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
Present
Reinforcement Learning
Present
Representation Learning
Present