Abigail See
AI Research Scientist at DeepMind
United Kingdom
Summary
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Rockstar🎓
Top SchoolAbigail See is an AI Research Scientist at DeepMind with a decade of experience focused on natural language processing and sequence-to-sequence models, building on a PhD from Stanford and a mathematics master’s from Cambridge. She has a strong track record in production-ready research: contributions to influential open-source projects include robustness fixes and beam search improvements for the widely cited pointer-generator summarization code and work on Facebook’s ParlAI dialogue framework. Her internships at Google Brain and Facebook AI produced practical advances in long-document summarization, interpretability, and controllable text generation, reflecting a blend of theoretical rigor and engineering pragmatism. Based in the UK, she combines deep academic pedigree with hands-on ML engineering to improve model stability and real-world dialogue behavior.
10 years of coding experience
Master’s Degree, Mathematics, Distinction, Master’s Degree, Mathematics, Distinction at University of CambridgeDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Stanford University
English, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (17)
beam-search10
pytorch10
python10
text-summarization10
machine-learning10
trainings10
tensorflow10
natural-language-processing10
sequence-to-sequence10
nlp10
modeling10
data-processing9
ai9
model-optimization8
git7
Programming languages (5)
Github contributions (5)
abisee/pointer-generator
Apr 2017 - Apr 2019
Code for the ACL 2017 paper "Get To The Point: Summarization with Pointer-Generator Networks"
Role in this project:
ML Engineer
ML EngineerContributions:29 commits, 2 PRs, 26 pushes in 2 years
Contributions summary:Abigail primarily focused on improving the codebase related to the pointer-generator network for text summarization. Their contributions included fixing a bug in beam search, addressing issues with empty article texts, and implementing various changes to handle potential NaN (Not a Number) values in the model training process. These changes involved modifying the loss calculation, incorporating padding masks, and adding features for debugging and model restoration, indicating a focus on model robustness and stability.
nlppointer-generatorpointpointersummarization
facebookresearch/ParlAI
Nov 2018 - May 2019
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 7 PRs, 7 pushes in 6 months
Contributions summary:Abigail contributed to the `parlai` repository, which focuses on dialogue AI models. Their commits included fixes to the beam search algorithm, a core component for generating responses in sequence-to-sequence models. Furthermore, the user added functionality to the `TorchAgent` class with flags for historical context and token manipulation, likely improving model behavior. They also made changes related to loss functions in the `Seq2seqAgent` and addressed a backward compatibility issue with PyTorch, demonstrating a focus on model efficiency and maintenance.
nlpdeep-learningdatasetmachine-learningtraining
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