William Timkey

About

Hello! I am a fifth-year PhD student in the Department of Linguistics at New York University advised by Tal Linzen. I am also a member of the Computational Sentence Processing Lab at UMass Amherst, where I work with Brian Dillon.

I completed a BA in Linguistics at Cornell University in May 2020, and then continued at Cornell as a post-baccalaureate researcher with Marten van Schijndel before beginning my PhD.

My research interests lie broadly within the field of computational psycholinguistics: I am interested in developing computational models of the psychological processes underlying language comprehension. More specifically, my goal is to understand the memory representations and processes that underlie our ability to rapidly infer the meaning of a sentence from noisy and ambiguous sensory input. Toward this this goal, my work focuses on developing psycholinguistically-motivated, data-driven, interpretable neural network models of sentence processing, and empirically testing the predictions of these models through human experiments.

News

• 9/2026 - I recently gave a talk at AMLaP 2026 on a neural-network based model of working memory retrieval for syntactic dependency resolution. [abstract] [slides]
• 8/2026 - Our work revealing a dissociation between prediction and structural processing in eye movements during reading was accepted to PNAS!
• 11/13/2025 - Check out the preprint of our new work revealing a dissociation between prediction and structural processing in eye movements during reading.
• 3/2025 - Presented a poster at HSP 2025 about whether language model surprisal can better capture garden path effects when syntactic parallelism is limited. Here is the abstract.
• 3/8/2024 - Presented a poster at HSP 2024 about which measures of eye-tracking while reading are captured by LM surprisal. Here is the abstract.
• 12/6/2023 - Presented our new EMNLP Findings paper on modeling similarity-based interference effects with neural self-attention in Singapore at EMNLP 2023.
• 9/1/2022 - Started my PhD at NYU!
• 9/23/2021 - Our EMNLP paper was selected for an oral presentation!
• 8/26/2021 - Our paper on similarity measures in Transformer language models was accepted to EMNLP!
• 8/7/2021 - Presented our ACL Findings paper at ACL 2021.
• 5/6/2021 - Our paper exploring the abstractive capabilities of neural summarization models was accepted to Findings of ACL 2021
• 6/2020 - Joined the C.Psyd group at Cornell
• 5/2020 - Graduated (with distinction!) from Cornell Unviersity with a BA in Linguistics

Publications

William Timkey, Tal Linzen. Do Syntax Heads Influence Model Behavior? arXiv preprint (in review). 2026.”

William Timkey, Brian Dillon, Tal Linzen. Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis arXiv preprint (in review). 2026.

William Timkey, Kuan-Jung Huang, Byung-Doh Oh, Grusha Prasad, Suhas Arehalli, Tal Linzen, Brian Dillon. Eye movements reveal a dissociation between prediction and structural processing difficulty in language comprehension In Proceedings of the National Academy of Sciences (PNAS). 2026. [data+code]

William Timkey, and Tal Linzen. A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing In Findings of the Association for Computational Linguistics: EMNLP. 2023.

William Timkey and Marten van Schijndel. All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2021.

Matt Wilber, William Timkey, and Marten van Schijndel. To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text In Proceedings of the 2021 Findings of the ACL. 2021.

Teaching

I also have a passion for teaching. Before coming to NYU, I was a Teaching Fellow in the Department of Statisics at Harvard University.

Spring 2026: LING-UA 61 - Human Sentence Processing @ New York University (Teaching Assistant)

Fall 2024: LING-UA 27 - Language and Mind @ New York University (Teaching Assistant)

Fall 2023: LING-UA 27 - Language and Mind @ New York University (Teaching Assistant)

Fall 2021: STAT 104 - Introduction to Quantitative Methods for Economics @ Harvard University (Teaching Fellow)

Summer 2021: STAT 100 - Introduction to Quantitative Methods @ Harvard University (Teaching Fellow)

Spring 2021: STAT 102 - Introduction to Quantitative Methods for Life Sciences @ Harvard University (Teaching Fellow)

Fall 2020: STAT 104 - Introduction to Quantitative Methods for Economics @ Harvard University (Teaching Fellow)

Summer 2020: STAT 100 - Introduction to Quantitative Methods @ Harvard University (Teaching Fellow)

Spring 2020: INFO 2950 - Introduction to Data Science @ Cornell University (Teaching Assistant)

Fall 2017: CSE 199 - First Year Seminar in Computer Science @ SUNY Buffalo (Teaching Assistant)