Welcome
Hi! My name is Laura and I am an artificial intelligence researcher. I'm (very broadly) interested in generalization in machine learning. Finding failure modes of contemporary deep learning methods, and trying to improve them. I want to learn more about the type of inductive biases allowing things like human-like or better systematic generalization and compositionality, and finding ways to learn them from data.
About me
Currently - Assistant Research Scientist at New York University in Brenden Lake's Human and Machine Learning Lab
Previously
Master Thesis Intern at Google in the Perception team working on human perception of audio
Research Intern at Facebook Artificial Intelligence Research (FAIR) working on systematic generalization in language
Software Engineering Intern at Google in the Assistant team working on automatic text simplification and partly autoregressive transformers
Teaching Assistant at University of Amsterdam (UvA) for courses like Data Processing and Natural Language Processing
News
December 2020 -- I'll present gSCAN at NeurIPS on Thursday December 10th at 9AM PST in poster session 6. Find the version of the poster with a bit more textual explanation than the one in the proceedings here.
September 2020 -- GroundedSCAN got accepted to NeurIPS 2020! The camera-ready version is now available on arXiv.
February 2020 -- We uploaded a preprint on ArXiv presenting GroundedSCAN! Check out the code for generating the benchmark and training models on it.
September 2019 -- Our paper "Insertion-Deletion Transformer" was accepted as an extended abstract to the WNGT workshop at EMNLP-IJCNLP 2019! Presented in Hong Kong by my awesome co-author Julia Proskurnia.
Research Interests & Experience
Language
Natural language is one of the drivers behind human intelligence, and modeling it is a great challenge.
Experience
Experience with structured prediction models for syntactic parsing (blogpost in the making!)
Partly autoregressive models for text simplification (proposed in this paper)
Grounded language understanding (designed a benchmark)
Cognitive Science
Human and other animal intelligence is remarkable and can be a great inspiration for artificial intelligence.
Experience
Designed a benchmark for systematic generalization based on the concept of compositionality at FAIR supervised by Brenden Lake