Structure learning for cyclic linear causal models
Conference proceedings talk at 36th Conference on Uncertainty in Artificial Intelligence, Toronto, Canada
CV
Education
- B.S. in GitHub, GitHub University, 2012
- M.S. in Jekyll, GitHub University, 2014
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
Work experience
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Talks
Identifiability of linear structural equation models under homoscedastic errors using algebraic matroids
Talk at 10th World Congress in Probability and Statistics, Seoul National University, Korea (virtual)
Partial homoscedasticity in causal discovery with linear models
Talk at ETH-UCPH-TUM Workshop on Graphical Models, Academy center TUM Raitenhaslach, Germany
Identifiability of linear structural equation models with homoscedastic errors using algebraic matroids
Talk at German Probabiliy and Statistics Day 2023, University of Duisburg-Essen, Germany
Identifiability of cyclic linear SEMs via algebraic matroids
Talk at 18. Doktorand:innentreffen der Stochastik 2023, Heidelberg University, Germany
Teaching
Service and leadership
- Currently signed in to 43 different slack teams