Abigail See

Career

See's Stanford page describes a PhD in computer science completed in September 2021, advised by Christopher Manning in the Stanford Natural Language Processing group, with internships at Google Brain and Facebook AI Research during the doctorate. Earlier, an internship at Microsoft Research working on termination analysis drew See toward computer science. Since January 2022 See has been a Research Scientist at Google DeepMind.¹

Research

See's doctoral research, collected in the thesis "Neural generation of open-ended text and dialogue", aimed to improve the controllability, interpretability, and coherence of neural text generation for tasks such as story generation and chitchat dialogue. Papers from this period study how controllable attributes affect human judgments of conversation (NAACL 2019), whether large pretrained language models make better storytellers (CoNLL 2019), and user dissatisfaction in a neural generative chatbot (SIGDIAL 2021), and See was a co-first author of the Stanford team's Alexa Prize 2019 paper on emotionally engaging mixed-initiative conversations.¹

See's 2017 pointer-generator paper with Peter J. Liu and Christopher Manning proposed a summarization model that can copy words from the source text while still generating new ones, with released code. At Google DeepMind, See is a co-author of the 2025 AlphaEvolve paper on a coding agent for scientific and algorithmic discovery.²³⁴⁵

Teaching and writing

See was co-instructor and Head TA of Stanford's CS224n, Natural Language Processing with Deep Learning, in 2018 and 2019, lecturing on recurrent networks, language models, machine translation, and natural language generation, and leading a redesign of the course assignments. See also developed AI teaching materials for high school students, delivered through AI4ALL and Girls Teaching Girls To Code, and organized the Stanford AI Lab's AI Salon discussion forum.¹

Work and contributions

• Pointer-generator networks for summarization Open-source code

  First author, with Peter J. Liu and Christopher D. Manning, of the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks; the accompanying repository holds the code for the paper under the Apache License 2.0, with modifications copyrighted to See.²³⁴

Timeline

1. 2016Co-authored "Compression of Neural Machine Translation Models via Pruning" at CoNLL.¹
2. Apr 14, 2017Released "Get To The Point - Summarization with Pointer-Generator Networks" as first author.²
3. 2018Began serving as co-instructor and Head TA of Stanford's CS224n, continuing in 2019.¹
4. 2019Published work at NAACL on how controllable attributes affect human judgments of conversation.¹
5. Sep 2021Completed a PhD in computer science at Stanford, advised by Christopher Manning.¹
6. Jan 2022Joined Google DeepMind as a Research Scientist.¹
7. Jun 16, 2025Co-authored the AlphaEvolve paper on a coding agent for scientific and algorithmic discovery.⁵

Other documented roles

Roles the sources document that are not confirmed as current.

• PhD student in the Stanford NLP Group (graduated 2021); CS224n co-instructor and Head TA (2018–2019), Stanford University¹

Honors and recognition

• NVIDIA Graduate Fellowship · NVIDIA, 2017¹
• Centennial Teaching Assistant Award · Stanford University, 2018¹
• Gerald J. Lieberman Fellowship · Stanford University, 2018¹
• Google PhD Fellowship · Google, 2019¹
• Forsythe Teaching Award · Stanford University, 2019¹

Where they work

• Google DeepMind 64 people in the wiki

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