Daniel D. Johnson (Preferred)
Daniel D. Johnson
University of Toronto
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.
****@hmc.edu
, ****@google.com
, ****@cs.toronto.edu
, ****@gmail.com
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
University of Toronto (toronto.edu)
2021 – Present
Researcher
Google (google.com)
2021 – Present
AI Resident
Google (google.com)
2019 – 2021
Perception software engineer
Cruise (getcruise.com)
2018 – 2019
Undergrad student
Harvey Mudd College (hmc.edu)
2014 – 2018
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
PhD Advisor
David Duvenaud
****@cs.toronto.edu
2021 – Present
PhD Advisor
Chris Maddison
****@cs.toronto.edu
2021 – Present
Coworker
Jacob Austin
****@google.com
2020 – Present
Coworker
Danny Tarlow
****@google.com
2019 – Present
Coworker
Hugo Larochelle
****@google.com
2019 – Present
Coworker
Dibya Ghosh
****@google.com
2019 – Present
Coworker
Linda Petrini
****@google.com
2019 – Present
Coworker
David Bieber
****@google.com
2019 – Present
Coworker
Disha Shrivastava
****@google.com
2019 – 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
uncertainty quantification
2022 – Present
decision theory
2022 – Present
variational methods
2020 – Present
transformers
2020 – Present
implicit differentiation
2019 – Present
program synthesis
2019 – Present
programming languages
2019 – Present
continuous relaxation
2019 – Present
bayesian inference
2019 – Present
probability theory
2018 – Present
graph neural networks
2017 – Present
structured data
2016 – Present
deep learning
2015 – Present
neural networks
2015 – Present
rnns
2015 – 2018