About Me

I’m a PhD student in computer science advised by Jackie Chi Kit Cheung at McGill and Mila. Currently, I’m a visiting student researcher at the Machine Learning and Mind Lab (MLML) at KAIST.

My research focus is knowledge acquisition and coreference. Colloquially, I’m studying computational approaches to representing knowledge, especially the types of general, commonsense knowledge needed for understanding what natural language is referring to in the world.

I’m lucky to be supported by a fellowship from the FRQ-NT (Fonds de recherche du Québec, Nature and Technologies). During my PhD, I’ve interned at Microsoft Research and Google Research. I previously completed a BSc in computer science at Stevens Institute of Technology, and I’m originally from Colorado.

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Selected Publications
Ian Porada, Alexandra Olteanu, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung (2024). Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective. arXiv.
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Ian Porada, Alessandro Sordoni, Jackie Cheung (2022). Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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Ian Porada, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung (2021). Modeling Event Plausibility with Consistent Conceptual Abstraction. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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Contact
ian.porada (at) mail.mcgill.ca
6666 St-Urbain, #200 Montreal, QC, H2S 3H1