I'm a third-year Computer Science PhD student at Stanford University, advised by Professor Chris Manning in the Natural Language Processing group.

My research focuses on the development of deep learning techniques for natural language tasks such as translation and summarization. Currently I'm focused on improving the interpretability of deep learning — in particular, devising internal representations of text that can be better understood by humans, while retaining the expressive power and flexibility of deep learning.

I have a blog, where I write about mine and others' research.

Recent News

  • February 2018 — I moderated a debate between Yann LeCun and Chris Manning on deep learning, structure and innate priors.
  • January 2018 — I'm a head TA for CS224n: Natural Language Processing with Deep Learning, and will be giving some lectures for the class.
  • August 2017 — I attended ACL 2017 in Vancouver. Read my thoughts on the conference here.
  • July 2017 — At SAILORS 2017, I instructed eight high-schoolers to build a Naive Bayes classifier to classify tweets in a disaster relief setting (materials here).
  • May 2017 — I received an NVIDIA Graduate Fellowship. Thank you NVIDIA!
  • April 2017 — Our paper on summarization has been accepted to ACL — check out the blog post! I started this project during my Google Brain internship, then continued it at Stanford.
  • November 2016 — I spoke to Melinda Gates about the importance of women in AI, both on a personal level, and to society at large.
  • August 2016 — Attended ACL and presented my poster at CoNLL.
  • July 2016 — I gave two tutorials at SAILORS, Stanford AI's outreach program for high school girls. One tutorial was on graph search algorithms applied to path-finding, and the other on the nearest neighbor algorithm applied to movie recommendations (materials here). The students really impressed me with their talent and enthusiasm!
  • June 2016 — Started summer internship at Google Brain, hosted by Peter J Liu. I'll be working on automatic text summarization.
  • June 2016 — Our paper has been accepted to CoNLL. See you in Berlin!
  • April 2016 — I was a mentor for the AI track of Girls Teaching Girls To Code.



Get To The Point: Summarization with Pointer-Generator Networks
Abigail See, Peter J. Liu, Christopher D. Manning
Association for Computational Linguistics (ACL). 2017.
[blog post | poster (PDF | Keynote) | slides]


Compression of Neural Machine Translation Models via Pruning
Abigail See, Minh-Thang Luong, Christopher D. Manning
Computational Natural Language Learning (CoNLL). 2016.
[poster | spotlight slides]


The Cost of Principles: Analyzing Power in Compatibility Weighted Voting Games
Abigail See, Yoram Bachrach, Pushmeet Kohli
Autonomous Agents and Multi-Agent Systems (AAMAS). 2014.


Ramsey vs. Lexicographic Termination Proving
Byron Cook, Abigail See, Florian Zuleger
Tools and Algorithms for the Construction and Analysis of Systems (TACAS). 2013.

Other Projects


I'm originally from Cambridge in the UK, though I've also lived in Singapore. In 2014 I graduated with a MMath from Cambridge University's Mathematical Tripos (to read about the many peculiarities of the Tripos, see here). While at Cambridge my interests were Pure Mathematics — particularly Combinatorics, Logic and Operational Research.

During my undergraduate degree I became interested in Computer Science by interning twice at Microsoft Research Cambridge. In 2012 I worked with the Programming Principles and Tools group on the T2 project, and in 2013 I worked on co-operative Game Theory.

In my spare time I enjoy social dance, watching and discussing films, and writing.


Here is my CV.