research scientist at Google Brain

(starting April 2017)

I am interested in designing high-performance machine learning methods that make sense to humans, and can make sense of humans. Here is a short writeup about why I care.

This includes building interpretable latent variable models (featured at Talking Machines) and creating structured Bayesian models of human decisions .
I have applied these ideas to data from various domains: computer programming education, autism spectrum discorder data, recipes, disease data, 15 years of crime data from the city of Cambridge, human dialogue data from the AMI meeting corpus, and text-based chat data during disaster response.

I graduated with a PhD from CSAIL, MIT (worked with Prof. Julie Shah and Prof. Cynthia Rudin).
Previously, I worked as a developer of MATLAB, and built robots for collaborative navigation.
I have co-organized ICML 2016 Worshop on Human Interpretability in Machine Learning (WHI) and NIPS 2016 Worshop on Interpretable Machine Learning for Complex Systems.

I am an executive board member of Women in Machine Learning.

Google Scholar



Towards A Rigorous Science of Interpretable Machine Learning

Finale Doshi-Velez and Been Kim
arxiv 2017
[pdf] [bibtex]


Examples are not Enough, Learn to Criticize! Criticism for Interpretability

Been Kim, Rajiv Khanna and Sanmi Koyejo
Neural Information Processing Systems 2016
[pdf] [NIPS oral presentation talk slides] [talk video] [bibtex] [code]


Diff-clustering: Interpretable embedding example-based clustering

Been Kim, Peter Turney and Peter Clark
under review


Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction

Been Kim, Finale Doshi-Velez and Julie Shah
Neural Information Processing Systems 2015
[pdf] [variational inference in gory detail] [bibtex]


iBCM: Interactive Bayesian Case Model Empowering Humans via Intuitive Interaction

Been Kim, Elena Glassman, Brittney Johnson and Julie Shah
coming soon (see my thesis for details).


Bayesian Case Model:
A Generative Approach for Case-Based Reasoning and Prototype Classification

Been Kim, Cynthia Rudin and Julie Shah
Neural Information Processing Systems 2014
[pdf] [poster] [bibtex]

This work was featured on MIT news and MIT front page spotlight.


Scalable and interpretable data representation for
high-dimensional complex data

Been Kim, Kayur Patel, Afshin Rostamizadeh and Julie Shah
AAAI Conference on Artificial Intelligence 2015
[pdf] [bibtex]


A Bayesian Generative Modeling with Logic-Based Prior

Been Kim, Caleb Chacha and Julie Shah
Journal of Artificial Intelligence Research 2014
[pdf] [bibtex]


Learning about Meetings

Been Kim and Cynthia Rudin
Data Mining and Knowledge Discovery Journal 2014

[arxiv] [pdf] [bibtex]

This work was featured in Wall Street Journal.


Inferring Robot Task Plans from Human Team Meetings:
A Generative Modeling Approach with Logic-Based Prior

Been Kim, Caleb Chacha and Julie Shah
AAAI Conference on Artificial Intelligence 2013
[pdf] [bibtex] [video]

This work was featured in:
"Introduction to AI" course at Harvard (COMPSCI180: Computer science 182) by Barbara J. Grosz.
[Course website]
"Human in the loop planning and decision support" tutorial at AAAI15 by Kartik Talamadupula and Subbarao Kambhampati.
[slides From the tutorial]


Multiple Relative Pose Graphs for Robust Cooperative Mapping

Been Kim, Michael Kaess, Luke Fletcher, John Leonard, Abraham Bachrach, Nicholas Roy, and Seth Teller
International Conference on Robotics and Automation 2010
[pdf] [bibtex] [video]


Human-inspired Techniques for Human-Machine Team Planning

Julie Shah, Been Kim and Stefanos Nikolaidis
AAAI Technical Report - Human Control of Bioinspired Swarms 2013
[pdf] [bibtex]



Interactive and Interpretable Machine Learning Models for Human Machine Collaboration

Been Kim
PhD Thesis 2015
[pdf] [bibtex] [slides]