Laura Ruis

Artificial Intelligence Student

University of Amsterdam

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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.



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