research scientist at AI2
affiliate faculty, Department of Computer Science and Engineering at the University of Washington

I am co-organizing NIPS 2016 Worshop on Interpretable Machine Learning for Complex Systems.

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).

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

Google Scholar



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] [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]