Roman Pogodin (Preferred)
Roman Pogodin
McGill/Mila
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.
****@ucl.ac.uk
, ****@gmail.com
, ****@mila.quebec
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.
Postdoc
McGill/Mila (mila.quebec)
2023 – 2024
PhD student
University College London (ucl.ac.uk)
2017 – 2022
Undergrad student
Moscow Institute of Physics and Technology (phystech.edu)
2013 – 2017
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Postdoc Advisor
Blake Richards
****@mcgill.ca
2023 – 2024
Postdoc Advisor
Guillaume Lajoie
****@umontreal.ca
2023 – 2024
PhD Advisor
Peter E. Latham
****@gatsby.ucl.ac.uk
2017 – 2022
Coauthor
Yash Mehta
****@gmail.com
2020 – 2021
Coauthor
Timothy Lillicrap
****@gmail.com
2020 – 2021
Coauthor
Yazhe Li
****@google.com
2020 – 2021
Coauthor
Arthur Gretton
****@gmail.com
2020 – 2021
Coauthor
Danica Sutherland
****@gmail.com
2020 – 2021
Coauthor
Tor Lattimore
****@gmail.com
2018 – 2019
Internship advisor
Wulfram Gerstner
****@epfl.ch
2016 – 2019
Coauthor
Dane Corneil
****@corneil.ca
2016 – 2019
Coauthor
Alexander Seeholzer
****@gmail.com
2016 – 2019
Coauthor
Joseph Heng
****@gmail.com
2016 – 2019
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
self-supervised learning
2021 – 2024
theoretical neuroscience, biologically plausible deep learning, plasticity models, Hebbian learning
2018 – 2024
theoretical neuroscience, working memory models, Hopfield networks, attractor networks
2016 – 2020